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Python SDK 0.21.3

The W&B Python SDK, accessible at wandb, enables you to train and fine-tune models, and manage models from experimentation to production.

After performing your training and fine-tuning operations with this SDK, you can use the Public API to query and analyze the data that was logged, and the Reports and Workspaces API to generate a web-publishable report summarizing your work.

Installation and setup

Sign up and create an API key

To authenticate your machine with W&B, you must first generate an API key at https://wandb.ai/authorize.

Install and import packages

Install the W&B library.

pip install wandb

Import W&B Python SDK:

import wandb

# Specify your team entity
entity = "<team_entity>"

# Project that the run is recorded to
project = "my-awesome-project"

with wandb.init(entity=entity, project=project) as run:
   run.log({"accuracy": 0.9, "loss": 0.1})

1 - Global Functions

Global functions in W&B are top-level functions that you call directly, such as wandb.init() or wandb.login(). Unlike methods that belong to specific classes, these functions provide direct access to W&B’s core functionality without needing to instantiate objects first.

Available Functions

Function Description
init() Start a new run to track and log to W&B. This is typically the first function you’ll call in your ML training pipeline.
login() Set up W&B login credentials to authenticate your machine with the platform.
setup() Prepare W&B for use in the current process and its children. Useful for multi-process applications.
teardown() Clean up W&B resources and shut down the backend process.
sweep() Initialize a hyperparameter sweep to search for optimal model configurations.
agent() Create a sweep agent to run hyperparameter optimization experiments.
controller() Manage and control sweep agents and their execution.
restore() Restore a previous run or experiment state for resuming work.

Example

The most common workflow begins with authenticating with W&B, initializing a run, and logging values (such as accuracy and loss) from your training loop. The first steps are to import wandb and use the global functions login() and init():

import wandb

# Authenticate with W&B
wandb.login()

# Hyperparameters and metadata
config = {
   "learning_rate": 0.01,
   "epochs": 10,
}

# Project that the run is recorded to
project = "my-awesome-project"

# Initialize a new run
with wandb.init(project=project, config=config) as run:
   # Your training code here...
   
   # Log values to W&B
   run.log({"accuracy": 0.9, "loss": 0.1})

1.1 - agent()

View the source code

function agent

agent(
    sweep_id: str,
    function: Optional[Callable] = None,
    entity: Optional[str] = None,
    project: Optional[str] = None,
    count: Optional[int] = None
) → None

Start one or more sweep agents.

The sweep agent uses the sweep_id to know which sweep it is a part of, what function to execute, and (optionally) how many agents to run.

Args:

  • sweep_id: The unique identifier for a sweep. A sweep ID is generated by W&B CLI or Python SDK.
  • function: A function to call instead of the “program” specified in the sweep config.
  • entity: The username or team name where you want to send W&B runs created by the sweep to. Ensure that the entity you specify already exists. If you don’t specify an entity, the run will be sent to your default entity, which is usually your username.
  • project: The name of the project where W&B runs created from the sweep are sent to. If the project is not specified, the run is sent to a project labeled “Uncategorized”.
  • count: The number of sweep config trials to try.

1.2 - controller()

View the source code

function controller

controller(
    sweep_id_or_config: Optional[str, Dict] = None,
    entity: Optional[str] = None,
    project: Optional[str] = None
) → _WandbController

Public sweep controller constructor.

Examples:

import wandb

tuner = wandb.controller(...)
print(tuner.sweep_config)
print(tuner.sweep_id)
tuner.configure_search(...)
tuner.configure_stopping(...)

1.3 - finish()

View the source code

function finish

finish(exit_code: 'int | None' = None, quiet: 'bool | None' = None) → None

Finish a run and upload any remaining data.

Marks the completion of a W&B run and ensures all data is synced to the server. The run’s final state is determined by its exit conditions and sync status.

Run States:

  • Running: Active run that is logging data and/or sending heartbeats.
  • Crashed: Run that stopped sending heartbeats unexpectedly.
  • Finished: Run completed successfully (exit_code=0) with all data synced.
  • Failed: Run completed with errors (exit_code!=0).

Args:

  • exit_code: Integer indicating the run’s exit status. Use 0 for success, any other value marks the run as failed.
  • quiet: Deprecated. Configure logging verbosity using wandb.Settings(quiet=...).

1.4 - init()

View the source code

function init

init(
    entity: 'str | None' = None,
    project: 'str | None' = None,
    dir: 'StrPath | None' = None,
    id: 'str | None' = None,
    name: 'str | None' = None,
    notes: 'str | None' = None,
    tags: 'Sequence[str] | None' = None,
    config: 'dict[str, Any] | str | None' = None,
    config_exclude_keys: 'list[str] | None' = None,
    config_include_keys: 'list[str] | None' = None,
    allow_val_change: 'bool | None' = None,
    group: 'str | None' = None,
    job_type: 'str | None' = None,
    mode: "Literal['online', 'offline', 'disabled', 'shared'] | None" = None,
    force: 'bool | None' = None,
    anonymous: "Literal['never', 'allow', 'must'] | None" = None,
    reinit: "bool | Literal[None, 'default', 'return_previous', 'finish_previous', 'create_new']" = None,
    resume: "bool | Literal['allow', 'never', 'must', 'auto'] | None" = None,
    resume_from: 'str | None' = None,
    fork_from: 'str | None' = None,
    save_code: 'bool | None' = None,
    tensorboard: 'bool | None' = None,
    sync_tensorboard: 'bool | None' = None,
    monitor_gym: 'bool | None' = None,
    settings: 'Settings | dict[str, Any] | None' = None
) → Run

Start a new run to track and log to W&B.

In an ML training pipeline, you could add wandb.init() to the beginning of your training script as well as your evaluation script, and each piece would be tracked as a run in W&B.

wandb.init() spawns a new background process to log data to a run, and it also syncs data to https://wandb.ai by default, so you can see your results in real-time. When you’re done logging data, call wandb.Run.finish() to end the run. If you don’t call run.finish(), the run will end when your script exits.

Run IDs must not contain any of the following special characters / \ # ? % :

Args:

  • entity: The username or team name the runs are logged to. The entity must already exist, so ensure you create your account or team in the UI before starting to log runs. If not specified, the run will default your default entity. To change the default entity, go to your settings and update the “Default location to create new projects” under “Default team”.
  • project: The name of the project under which this run will be logged. If not specified, we use a heuristic to infer the project name based on the system, such as checking the git root or the current program file. If we can’t infer the project name, the project will default to "uncategorized".
  • dir: The absolute path to the directory where experiment logs and metadata files are stored. If not specified, this defaults to the ./wandb directory. Note that this does not affect the location where artifacts are stored when calling download().
  • id: A unique identifier for this run, used for resuming. It must be unique within the project and cannot be reused once a run is deleted. For a short descriptive name, use the name field, or for saving hyperparameters to compare across runs, use config.
  • name: A short display name for this run, which appears in the UI to help you identify it. By default, we generate a random two-word name allowing easy cross-reference runs from table to charts. Keeping these run names brief enhances readability in chart legends and tables. For saving hyperparameters, we recommend using the config field.
  • notes: A detailed description of the run, similar to a commit message in Git. Use this argument to capture any context or details that may help you recall the purpose or setup of this run in the future.
  • tags: A list of tags to label this run in the UI. Tags are helpful for organizing runs or adding temporary identifiers like “baseline” or “production.” You can easily add, remove tags, or filter by tags in the UI. If resuming a run, the tags provided here will replace any existing tags. To add tags to a resumed run without overwriting the current tags, use run.tags += ("new_tag",) after calling run = wandb.init().
  • config: Sets wandb.config, a dictionary-like object for storing input parameters to your run, such as model hyperparameters or data preprocessing settings. The config appears in the UI in an overview page, allowing you to group, filter, and sort runs based on these parameters. Keys should not contain periods (.), and values should be smaller than 10 MB. If a dictionary, argparse.Namespace, or absl.flags.FLAGS is provided, the key-value pairs will be loaded directly into wandb.config. If a string is provided, it is interpreted as a path to a YAML file, from which configuration values will be loaded into wandb.config.
  • config_exclude_keys: A list of specific keys to exclude from wandb.config.
  • config_include_keys: A list of specific keys to include in wandb.config.
  • allow_val_change: Controls whether config values can be modified after their initial set. By default, an exception is raised if a config value is overwritten. For tracking variables that change during training, such as a learning rate, consider using wandb.log() instead. By default, this is False in scripts and True in Notebook environments.
  • group: Specify a group name to organize individual runs as part of a larger experiment. This is useful for cases like cross-validation or running multiple jobs that train and evaluate a model on different test sets. Grouping allows you to manage related runs collectively in the UI, making it easy to toggle and review results as a unified experiment.
  • job_type: Specify the type of run, especially helpful when organizing runs within a group as part of a larger experiment. For example, in a group, you might label runs with job types such as “train” and “eval”. Defining job types enables you to easily filter and group similar runs in the UI, facilitating direct comparisons.
  • mode: Specifies how run data is managed, with the following options:
    • "online" (default): Enables live syncing with W&B when a network connection is available, with real-time updates to visualizations.
    • "offline": Suitable for air-gapped or offline environments; data is saved locally and can be synced later. Ensure the run folder is preserved to enable future syncing.
    • "disabled": Disables all W&B functionality, making the run’s methods no-ops. Typically used in testing to bypass W&B operations.
    • "shared": (This is an experimental feature). Allows multiple processes, possibly on different machines, to simultaneously log to the same run. In this approach you use a primary node and one or more worker nodes to log data to the same run. Within the primary node you initialize a run. For each worker node, initialize a run using the run ID used by the primary node.
  • force: Determines if a W&B login is required to run the script. If True, the user must be logged in to W&B; otherwise, the script will not proceed. If False (default), the script can proceed without a login, switching to offline mode if the user is not logged in.
  • anonymous: Specifies the level of control over anonymous data logging. Available options are:
    • "never" (default): Requires you to link your W&B account before tracking the run. This prevents unintentional creation of anonymous runs by ensuring each run is associated with an account.
    • "allow": Enables a logged-in user to track runs with their account, but also allows someone running the script without a W&B account to view the charts and data in the UI.
    • "must": Forces the run to be logged to an anonymous account, even if the user is logged in.
  • reinit: Shorthand for the “reinit” setting. Determines the behavior of wandb.init() when a run is active.
  • resume: Controls the behavior when resuming a run with the specified id. Available options are:
    • "allow": If a run with the specified id exists, it will resume from the last step; otherwise, a new run will be created.
    • "never": If a run with the specified id exists, an error will be raised. If no such run is found, a new run will be created.
    • "must": If a run with the specified id exists, it will resume from the last step. If no run is found, an error will be raised.
    • "auto": Automatically resumes the previous run if it crashed on this machine; otherwise, starts a new run.
    • True: Deprecated. Use "auto" instead.
    • False: Deprecated. Use the default behavior (leaving resume unset) to always start a new run. If resume is set, fork_from and resume_from cannot be used. When resume is unset, the system will always start a new run.
  • resume_from: Specifies a moment in a previous run to resume a run from, using the format {run_id}?_step={step}. This allows users to truncate the history logged to a run at an intermediate step and resume logging from that step. The target run must be in the same project. If an id argument is also provided, the resume_from argument will take precedence. resume, resume_from and fork_from cannot be used together, only one of them can be used at a time. Note that this feature is in beta and may change in the future.
  • fork_from: Specifies a point in a previous run from which to fork a new run, using the format {id}?_step={step}. This creates a new run that resumes logging from the specified step in the target run’s history. The target run must be part of the current project. If an id argument is also provided, it must be different from the fork_from argument, an error will be raised if they are the same. resume, resume_from and fork_from cannot be used together, only one of them can be used at a time. Note that this feature is in beta and may change in the future.
  • save_code: Enables saving the main script or notebook to W&B, aiding in experiment reproducibility and allowing code comparisons across runs in the UI. By default, this is disabled, but you can change the default to enable on your settings page.
  • tensorboard: Deprecated. Use sync_tensorboard instead.
  • sync_tensorboard: Enables automatic syncing of W&B logs from TensorBoard or TensorBoardX, saving relevant event files for viewing in the W&B UI.
  • saving relevant event files for viewing in the W&B UI. (Default: False)
  • monitor_gym: Enables automatic logging of videos of the environment when using OpenAI Gym.
  • settings: Specifies a dictionary or wandb.Settings object with advanced settings for the run.

Returns: A Run object.

Raises:

  • Error: If some unknown or internal error happened during the run initialization.
  • AuthenticationError: If the user failed to provide valid credentials.
  • CommError: If there was a problem communicating with the WandB server.
  • UsageError: If the user provided invalid arguments.
  • KeyboardInterrupt: If user interrupts the run.

Examples: wandb.init() returns a Run object. Use the run object to log data, save artifacts, and manage the run lifecycle.

import wandb

config = {"lr": 0.01, "batch_size": 32}
with wandb.init(config=config) as run:
    # Log accuracy and loss to the run
    acc = 0.95  # Example accuracy
    loss = 0.05  # Example loss
    run.log({"accuracy": acc, "loss": loss})

1.5 - login()

View the source code

function login

login(
    anonymous: Optional[Literal['must', 'allow', 'never']] = None,
    key: Optional[str] = None,
    relogin: Optional[bool] = None,
    host: Optional[str] = None,
    force: Optional[bool] = None,
    timeout: Optional[int] = None,
    verify: bool = False,
    referrer: Optional[str] = None
) → bool

Set up W&B login credentials.

By default, this will only store credentials locally without verifying them with the W&B server. To verify credentials, pass verify=True.

Args:

  • anonymous: Set to “must”, “allow”, or “never”. If set to “must”, always log a user in anonymously. If set to “allow”, only create an anonymous user if the user isn’t already logged in. If set to “never”, never log a user anonymously. Default set to “never”. Defaults to None.
  • key: The API key to use.
  • relogin: If true, will re-prompt for API key.
  • host: The host to connect to.
  • force: If true, will force a relogin.
  • timeout: Number of seconds to wait for user input.
  • verify: Verify the credentials with the W&B server.
  • referrer: The referrer to use in the URL login request.

Returns:

  • bool: If key is configured.

Raises:

  • AuthenticationError: If api_key fails verification with the server.
  • UsageError: If api_key cannot be configured and no tty.

1.6 - restore()

View the source code

function restore

restore(
    name: 'str',
    run_path: 'str | None' = None,
    replace: 'bool' = False,
    root: 'str | None' = None
) → None | TextIO

Download the specified file from cloud storage.

File is placed into the current directory or run directory. By default, will only download the file if it doesn’t already exist.

Args:

  • name: The name of the file.
  • run_path: Optional path to a run to pull files from, i.e. username/project_name/run_id if wandb.init has not been called, this is required.
  • replace: Whether to download the file even if it already exists locally
  • root: The directory to download the file to. Defaults to the current directory or the run directory if wandb.init was called.

Returns: None if it can’t find the file, otherwise a file object open for reading.

Raises:

  • CommError: If W&B can’t connect to the W&B backend.
  • ValueError: If the file is not found or can’t find run_path.

1.7 - setup()

View the source code

function setup

setup(settings: 'Settings | None' = None) → _WandbSetup

Prepares W&B for use in the current process and its children.

You can usually ignore this as it is implicitly called by wandb.init().

When using wandb in multiple processes, calling wandb.setup() in the parent process before starting child processes may improve performance and resource utilization.

Note that wandb.setup() modifies os.environ, and it is important that child processes inherit the modified environment variables.

See also wandb.teardown().

Args:

  • settings: Configuration settings to apply globally. These can be overridden by subsequent wandb.init() calls.

Example:

import multiprocessing

import wandb


def run_experiment(params):
   with wandb.init(config=params):
        # Run experiment
        pass


if __name__ == "__main__":
   # Start backend and set global config
   wandb.setup(settings={"project": "my_project"})

   # Define experiment parameters
   experiment_params = [
        {"learning_rate": 0.01, "epochs": 10},
        {"learning_rate": 0.001, "epochs": 20},
   ]

   # Start multiple processes, each running a separate experiment
   processes = []
   for params in experiment_params:
        p = multiprocessing.Process(target=run_experiment, args=(params,))
        p.start()
        processes.append(p)

   # Wait for all processes to complete
   for p in processes:
        p.join()

   # Optional: Explicitly shut down the backend
   wandb.teardown()

1.8 - sweep()

View the source code

function sweep

sweep(
    sweep: Union[dict, Callable],
    entity: Optional[str] = None,
    project: Optional[str] = None,
    prior_runs: Optional[List[str]] = None
) → str

Initialize a hyperparameter sweep.

Search for hyperparameters that optimizes a cost function of a machine learning model by testing various combinations.

Make note the unique identifier, sweep_id, that is returned. At a later step provide the sweep_id to a sweep agent.

See Sweep configuration structure for information on how to define your sweep.

Args:

  • sweep: The configuration of a hyperparameter search. (or configuration generator). If you provide a callable, ensure that the callable does not take arguments and that it returns a dictionary that conforms to the W&B sweep config spec.
  • entity: The username or team name where you want to send W&B runs created by the sweep to. Ensure that the entity you specify already exists. If you don’t specify an entity, the run will be sent to your default entity, which is usually your username.
  • project: The name of the project where W&B runs created from the sweep are sent to. If the project is not specified, the run is sent to a project labeled ‘Uncategorized’.
  • prior_runs: The run IDs of existing runs to add to this sweep.

Returns:

  • str: A unique identifier for the sweep.

1.9 - teardown()

View the source code

function teardown

teardown(exit_code: 'int | None' = None) → None

Waits for W&B to finish and frees resources.

Completes any runs that were not explicitly finished using run.finish() and waits for all data to be uploaded.

It is recommended to call this at the end of a session that used wandb.setup(). It is invoked automatically in an atexit hook, but this is not reliable in certain setups such as when using Python’s multiprocessing module.

2 - Data Types

Data Types in W&B are classes that wrap media and structured data for logging to runs. They include visualization components in the W&B UI and handle data serialization, storage, and retrieval.

Available Data Types

Data Type Description
Image Log images with support for masks, bounding boxes, and segmentation.
Video Track video data for model outputs or dataset samples.
Audio Log audio samples for audio processing tasks.
Table Create tables that can contain mixed media types.
Plotly Log Plotly charts for data visualization.
Html Embed custom HTML content.
Object3D Visualize 3D point clouds and meshes.
Molecule Log molecular structures for computational chemistry.

Examples

This example uses an Image:

import wandb
import matplotlib.pyplot as plt

# Generate an image for demonstration purposes
path_to_img = "/path/to/cafe.png"
im = plt.imread(path_to_img)

# Initialize a new run
with wandb.init(project="awesome-project") as run:

    # Log the image
    run.log({"img": [wandb.Image(im, caption="Cafe")]})

This example uses a Table to log a table with mixed text and labels:

import wandb

# Initialize a new run
with wandb.init(project="visualize-predictions", name="tables") as run:

    # Create tabular data, using a list of lists
    data = [["Cat", "1", "1"],["Dog", "0", "-1"]]
    run.log({"Table 1": wandb.Table(data=data, columns=["Text", "Predicted Label", "True Label"])})

    # Create tabular data, using `wandb.Table.add_data()` method
    table = wandb.Table(columns=["Text", "Predicted Label", "True Label"])
    table.add_data("Cat", "1", "1")
    table.add_data("Dog", "0", "-1")
    run.log({"Table 2": table})

2.1 - Audio

View the source code

class Audio

W&B class for audio clips.

method Audio.__init__

__init__(
    data_or_path: Union[str, pathlib.Path, list, ForwardRef('np.ndarray')],
    sample_rate: Optional[int] = None,
    caption: Optional[str] = None
)

Accept a path to an audio file or a numpy array of audio data.

Args:

  • data_or_path: A path to an audio file or a NumPy array of audio data.
  • sample_rate: Sample rate, required when passing in raw NumPy array of audio data.
  • caption: Caption to display with audio.

classmethod Audio.durations

durations(audio_list)

Calculate the duration of the audio files.

classmethod Audio.sample_rates

sample_rates(audio_list)

Get sample rates of the audio files.

2.2 - box3d()

View the source code

function box3d

box3d(
    center: 'npt.ArrayLike',
    size: 'npt.ArrayLike',
    orientation: 'npt.ArrayLike',
    color: 'RGBColor',
    label: 'Optional[str]' = None,
    score: 'Optional[numeric]' = None
) → Box3D

Returns a Box3D.

Args:

  • center: The center point of the box as a length-3 ndarray.
  • size: The box’s X, Y and Z dimensions as a length-3 ndarray.
  • orientation: The rotation transforming global XYZ coordinates into the box’s local XYZ coordinates, given as a length-4 ndarray [r, x, y, z] corresponding to the non-zero quaternion r + xi + yj + zk.
  • color: The box’s color as an (r, g, b) tuple with 0 <= r,g,b <= 1.
  • label: An optional label for the box.
  • score: An optional score for the box.

2.3 - Histogram

View the source code

class Histogram

W&B class for histograms.

This object works just like numpy’s histogram function https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html

Attributes:

  • bins ([float]): Edges of bins
  • histogram ([int]): Number of elements falling in each bin.

method Histogram.__init__

__init__(
    sequence: Optional[Sequence] = None,
    np_histogram: Optional[ForwardRef('NumpyHistogram')] = None,
    num_bins: int = 64
) → None

Initialize a Histogram object.

Args: sequence: Input data for histogram. np_histogram: Alternative input of a precomputed histogram. num_bins: Number of bins for the histogram. The default number of bins is 64. The maximum number of bins is 512.

Examples: Generate histogram from a sequence.

import wandb

wandb.Histogram([1, 2, 3])

Efficiently initialize from np.histogram.

import numpy as np
import wandb

hist = np.histogram(data)
wandb.Histogram(np_histogram=hist)

2.4 - Html

View the source code

class Html

W&B class for logging HTML content to W&B.

method Html.__init__

__init__(
    data: Union[str, pathlib.Path, ForwardRef('TextIO')],
    inject: bool = True,
    data_is_not_path: bool = False
) → None

Creates a W&B HTML object.

Args: data: A string that is a path to a file with the extension “.html”, or a string or IO object containing literal HTML.

  • inject: Add a stylesheet to the HTML object. If set to False the HTML will pass through unchanged.
  • data_is_not_path: If set to False, the data will be treated as a path to a file.

Examples: It can be initialized by providing a path to a file:

with wandb.init() as run:
    run.log({"html": wandb.Html("./index.html")})

Alternatively, it can be initialized by providing literal HTML, in either a string or IO object:

with wandb.init() as run:
    run.log({"html": wandb.Html("<h1>Hello, world!</h1>")})

2.5 - Image

View the source code

class Image

A class for logging images to W&B.

method Image.__init__

__init__(
    data_or_path: 'ImageDataOrPathType',
    mode: Optional[str] = None,
    caption: Optional[str] = None,
    grouping: Optional[int] = None,
    classes: Optional[ForwardRef('Classes'), Sequence[dict]] = None,
    boxes: Optional[Dict[str, ForwardRef('BoundingBoxes2D')], Dict[str, dict]] = None,
    masks: Optional[Dict[str, ForwardRef('ImageMask')], Dict[str, dict]] = None,
    file_type: Optional[str] = None,
    normalize: bool = True
) → None

Initialize a wandb.Image object.

Args:

  • data_or_path: Accepts NumPy array/pytorch tensor of image data, a PIL image object, or a path to an image file. If a NumPy array or pytorch tensor is provided, the image data will be saved to the given file type. If the values are not in the range [0, 255] or all values are in the range [0, 1], the image pixel values will be normalized to the range [0, 255] unless normalize is set to False.
    • pytorch tensor should be in the format (channel, height, width)
    • NumPy array should be in the format (height, width, channel)
  • mode: The PIL mode for an image. Most common are “L”, “RGB”,
  • "RGBA". Full explanation at https: //pillow.readthedocs.io/en/stable/handbook/concepts.html#modes
  • caption: Label for display of image.
  • grouping: The grouping number for the image.
  • classes: A list of class information for the image, used for labeling bounding boxes, and image masks.
  • boxes: A dictionary containing bounding box information for the image.
  • see: https://docs.wandb.ai/ref/python/data-types/boundingboxes2d/
  • masks: A dictionary containing mask information for the image.
  • see: https://docs.wandb.ai/ref/python/data-types/imagemask/
  • file_type: The file type to save the image as. This parameter has no effect if data_or_path is a path to an image file.
  • normalize: If True, normalize the image pixel values to fall within the range of [0, 255]. Normalize is only applied if data_or_path is a numpy array or pytorch tensor.

Examples: Create a wandb.Image from a numpy array

import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
         image = wandb.Image(pixels, caption=f"random field {i}")
         examples.append(image)
    run.log({"examples": examples})

Create a wandb.Image from a PILImage

import numpy as np
from PIL import Image as PILImage
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(
             low=0, high=256, size=(100, 100, 3), dtype=np.uint8
         )
         pil_image = PILImage.fromarray(pixels, mode="RGB")
         image = wandb.Image(pil_image, caption=f"random field {i}")
         examples.append(image)
    run.log({"examples": examples})

Log .jpg rather than .png (default)

import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
         image = wandb.Image(
             pixels, caption=f"random field {i}", file_type="jpg"
         )
         examples.append(image)
    run.log({"examples": examples})

property Image.image

2.6 - Molecule

View the source code

class Molecule

W&B class for 3D Molecular data.

method Molecule.__init__

__init__(
    data_or_path: Union[str, pathlib.Path, ForwardRef('TextIO')],
    caption: Optional[str] = None,
    **kwargs: str
) → None

Initialize a Molecule object.

Args:

  • data_or_path: Molecule can be initialized from a file name or an io object.
  • caption: Caption associated with the molecule for display.

2.7 - Object3D

View the source code

class Object3D

W&B class for 3D point clouds.

method Object3D.__init__

__init__(
    data_or_path: Union[ForwardRef('np.ndarray'), str, pathlib.Path, ForwardRef('TextIO'), dict],
    caption: Optional[str] = None,
    **kwargs: Optional[str, ForwardRef('FileFormat3D')]
) → None

Creates a W&B Object3D object.

Args:

  • data_or_path: Object3D can be initialized from a file or a numpy array.
  • caption: Caption associated with the object for display.

Examples: The shape of the numpy array must be one of either

[[x y z],       ...] nx3
[[x y z c],     ...] nx4 where c is a category with supported range [1, 14]
[[x y z r g b], ...] nx6 where is rgb is color

2.8 - Plotly

View the source code

class Plotly

W&B class for Plotly plots.

method Plotly.__init__

__init__(
    val: Union[ForwardRef('plotly.Figure'), ForwardRef('matplotlib.artist.Artist')]
)

Initialize a Plotly object.

Args:

  • val: Matplotlib or Plotly figure.

2.9 - Table

View the source code

class Table

The Table class used to display and analyze tabular data.

Unlike traditional spreadsheets, Tables support numerous types of data: scalar values, strings, numpy arrays, and most subclasses of wandb.data_types.Media. This means you can embed Images, Video, Audio, and other sorts of rich, annotated media directly in Tables, alongside other traditional scalar values.

This class is the primary class used to generate W&B Tables https://docs.wandb.ai/guides/models/tables/.

method Table.__init__

__init__(
    columns=None,
    data=None,
    rows=None,
    dataframe=None,
    dtype=None,
    optional=True,
    allow_mixed_types=False,
    log_mode: Optional[Literal['IMMUTABLE', 'MUTABLE', 'INCREMENTAL']] = 'IMMUTABLE'
)

Initializes a Table object.

The rows is available for legacy reasons and should not be used. The Table class uses data to mimic the Pandas API.

Args:

  • columns: (List[str]) Names of the columns in the table. Defaults to [“Input”, “Output”, “Expected”].
  • data: (List[List[any]]) 2D row-oriented array of values.
  • dataframe: (pandas.DataFrame) DataFrame object used to create the table. When set, data and columns arguments are ignored.
  • rows: (List[List[any]]) 2D row-oriented array of values.
  • optional: (Union[bool,List[bool]]) Determines if None values are allowed. Default to True - If a singular bool value, then the optionality is enforced for all columns specified at construction time - If a list of bool values, then the optionality is applied to each column - should be the same length as columns applies to all columns. A list of bool values applies to each respective column.
  • allow_mixed_types: (bool) Determines if columns are allowed to have mixed types (disables type validation). Defaults to False
  • log_mode: Optional[str] Controls how the Table is logged when mutations occur. Options: - “IMMUTABLE” (default): Table can only be logged once; subsequent logging attempts after the table has been mutated will be no-ops. - “MUTABLE”: Table can be re-logged after mutations, creating a new artifact version each time it’s logged. - “INCREMENTAL”: Table data is logged incrementally, with each log creating a new artifact entry containing the new data since the last log.

method Table.add_column

add_column(name, data, optional=False)

Adds a column of data to the table.

Args:

  • name: (str) - the unique name of the column
  • data: (list | np.array) - a column of homogeneous data
  • optional: (bool) - if null-like values are permitted

method Table.add_computed_columns

add_computed_columns(fn)

Adds one or more computed columns based on existing data.

Args:

  • fn: A function which accepts one or two parameters, ndx (int) and row (dict), which is expected to return a dict representing new columns for that row, keyed by the new column names.
    • ndx is an integer representing the index of the row. Only included if include_ndx is set to True.
    • row is a dictionary keyed by existing columns

method Table.add_data

add_data(*data)

Adds a new row of data to the table.

The maximum amount ofrows in a table is determined by wandb.Table.MAX_ARTIFACT_ROWS.

The length of the data should match the length of the table column.

method Table.add_row

add_row(*row)

Deprecated. Use Table.add_data method instead.

method Table.cast

cast(col_name, dtype, optional=False)

Casts a column to a specific data type.

This can be one of the normal python classes, an internal W&B type, or an example object, like an instance of wandb.Image or wandb.Classes.

Args:

  • col_name (str): The name of the column to cast.
  • dtype (class, wandb.wandb_sdk.interface._dtypes.Type, any): The target dtype.
  • optional (bool): If the column should allow Nones.

method Table.get_column

get_column(name, convert_to=None)

Retrieves a column from the table and optionally converts it to a NumPy object.

Args:

  • name: (str) - the name of the column
  • convert_to: (str, optional) - “numpy”: will convert the underlying data to numpy object

method Table.get_dataframe

get_dataframe()

Returns a pandas.DataFrame of the table.

method Table.get_index

get_index()

Returns an array of row indexes for use in other tables to create links.

2.10 - Video

View the source code

class Video

A class for logging videos to W&B.

method Video.__init__

__init__(
    data_or_path: Union[str, pathlib.Path, ForwardRef('np.ndarray'), ForwardRef('TextIO'), ForwardRef('BytesIO')],
    caption: Optional[str] = None,
    fps: Optional[int] = None,
    format: Optional[Literal['gif', 'mp4', 'webm', 'ogg']] = None
)

Initialize a W&B Video object.

Args:

  • data_or_path: Video can be initialized with a path to a file or an io object. Video can be initialized with a numpy tensor. The numpy tensor must be either 4 dimensional or 5 dimensional. The dimensions should be (number of frames, channel, height, width) or (batch, number of frames, channel, height, width) The format parameter must be specified with the format argument when initializing with a numpy array or io object.
  • caption: Caption associated with the video for display.
  • fps: The frame rate to use when encoding raw video frames. Default value is 4. This parameter has no effect when data_or_path is a string, or bytes.
  • format: Format of video, necessary if initializing with a numpy array or io object. This parameter will be used to determine the format to use when encoding the video data. Accepted values are “gif”, “mp4”, “webm”, or “ogg”. If no value is provided, the default format will be “gif”.

Examples: Log a numpy array as a video

import numpy as np
import wandb

with wandb.init() as run:
    # axes are (number of frames, channel, height, width)
    frames = np.random.randint(
         low=0, high=256, size=(10, 3, 100, 100), dtype=np.uint8
    )
    run.log({"video": wandb.Video(frames, format="mp4", fps=4)})

3 - Experiments

These classes comprise the core building blocks for tracking machine learning experiments, managing artifacts, and configuring SDK behavior. These foundational classes enable you to log metrics, store model checkpoints, version datasets, and manage experiment configurations with full reproducibility and collaboration features.

For more details on using these classes in ML experiments, consult the Experiments and Artifacts docs.

Core Classes

Class Description
Run The primary unit of computation logged by W&B, representing a single ML experiment with metrics, configurations, and outputs.
Artifact Flexible and lightweight building block for dataset and model versioning with automatic deduplication and lineage tracking.
Settings Configuration management for the W&B SDK, controlling behavior from logging to API interactions.

Getting Started

Track an experiment

Create and track a machine learning experiment with metrics logging:

import wandb

# Initialize a new run
with wandb.init(project="my-experiments", config={"learning_rate": 0.001}) as run:
    # Access configuration
    config = run.config
    
    # Log metrics during training
    for epoch in range(10):
        metrics = train_one_epoch()  # Your training logic
        run.log({
            "loss": metrics["loss"],
            "accuracy": metrics["accuracy"],
            "epoch": epoch
        })
    
    # Log summary metrics
    run.summary["best_accuracy"] = max_accuracy

Version a model artifact

Create and log a versioned model artifact with metadata:

import wandb

with wandb.init(project="my-models") as run:
    # Train your model
    model = train_model()
    
    # Create an artifact for the model
    model_artifact = wandb.Artifact(
        name="my-model",
        type="model",
        description="ResNet-50 trained on ImageNet subset",
        metadata={
            "architecture": "ResNet-50",
            "dataset": "ImageNet-1K",
            "accuracy": 0.95
        }
    )
    
    # Add model files to the artifact
    model_artifact.add_file("model.pt")
    model_artifact.add_dir("model_configs/")
    
    # Log the artifact to W&B
    run.log_artifact(model_artifact)

Configure SDK settings

Customize W&B SDK behavior for your specific requirements:

import wandb

# Configure settings programmatically
wandb.Settings(
    project="production-runs",
    entity="my-team",
    mode="offline",  # Run offline, sync later
    save_code=True,   # Save source code
    quiet=True        # Reduce console output
)

# Or use environment variables
# export WANDB_PROJECT=production-runs
# export WANDB_MODE=offline

# Initialize with custom settings
with wandb.init() as run:
    # Your experiment code here
    pass

Track relationships between datasets, models, and evaluations:

import wandb

with wandb.init(project="ml-pipeline") as run:
    # Use a dataset artifact
    dataset = run.use_artifact("dataset:v1")
    dataset_dir = dataset.download()
    
    # Train model using the dataset
    model = train_on_dataset(dataset_dir)
    
    # Create model artifact with dataset lineage
    model_artifact = wandb.Artifact(
        name="trained-model",
        type="model"
    )
    model_artifact.add_file("model.pt")
    
    # Log with automatic lineage tracking
    run.log_artifact(model_artifact)

3.1 - Artifact

View the source code

class Artifact

Flexible and lightweight building block for dataset and model versioning.

Construct an empty W&B Artifact. Populate an artifacts contents with methods that begin with add. Once the artifact has all the desired files, you can call run.log_artifact() to log it.

method Artifact.__init__

__init__(
    name: 'str',
    type: 'str',
    description: 'str | None' = None,
    metadata: 'dict[str, Any] | None' = None,
    incremental: 'bool' = False,
    use_as: 'str | None' = None
) → None

Args:

  • name (str): A human-readable name for the artifact. Use the name to identify a specific artifact in the W&B App UI or programmatically. You can interactively reference an artifact with the use_artifact Public API. A name can contain letters, numbers, underscores, hyphens, and dots. The name must be unique across a project.
  • type (str): The artifact’s type. Use the type of an artifact to both organize and differentiate artifacts. You can use any string that contains letters, numbers, underscores, hyphens, and dots. Common types include dataset or model. Include model within your type string if you want to link the artifact to the W&B Model Registry. Note that some types reserved for internal use and cannot be set by users. Such types include job and types that start with wandb-.
  • description (str | None) = None: A description of the artifact. For Model or Dataset Artifacts, add documentation for your standardized team model or dataset card. View an artifact’s description programmatically with the Artifact.description attribute or programmatically with the W&B App UI. W&B renders the description as markdown in the W&B App.
  • metadata (dict[str, Any] | None) = None: Additional information about an artifact. Specify metadata as a dictionary of key-value pairs. You can specify no more than 100 total keys.
  • incremental: Use Artifact.new_draft() method instead to modify an existing artifact.
  • use_as: Deprecated.
  • is_link: Boolean indication of if the artifact is a linked artifact(True) or source artifact(False).

Returns: An Artifact object.

property Artifact.aliases

List of one or more semantically-friendly references or

identifying “nicknames” assigned to an artifact version.

Aliases are mutable references that you can programmatically reference. Change an artifact’s alias with the W&B App UI or programmatically. See Create new artifact versions for more information.

Returns:

  • list[str]: The aliases property value.

property Artifact.collection

The collection this artifact was retrieved from.

A collection is an ordered group of artifact versions. If this artifact was retrieved from a portfolio / linked collection, that collection will be returned rather than the collection that an artifact version originated from. The collection that an artifact originates from is known as the source sequence.

Returns:

  • ArtifactCollection: The collection property value.

property Artifact.commit_hash

The hash returned when this artifact was committed.

Returns:

  • str: The commit_hash property value.

property Artifact.created_at

Timestamp when the artifact was created.

Returns:

  • str: The created_at property value.

property Artifact.description

A description of the artifact.

Returns:

  • str | None: The description property value.

property Artifact.digest

The logical digest of the artifact.

The digest is the checksum of the artifact’s contents. If an artifact has the same digest as the current latest version, then log_artifact is a no-op.

Returns:

  • str: The digest property value.

property Artifact.entity

The name of the entity that the artifact collection belongs to.

If the artifact is a link, the entity will be the entity of the linked artifact.

Returns:

  • str: The entity property value.

property Artifact.file_count

The number of files (including references).

Returns:

  • int: The file_count property value.

property Artifact.history_step

The nearest step at which history metrics were logged for the source run of the artifact.

Examples:

run = artifact.logged_by()
if run and (artifact.history_step is not None):
    history = run.sample_history(
        min_step=artifact.history_step,
        max_step=artifact.history_step + 1,
        keys=["my_metric"],
    )

Returns:

  • int | None: The history_step property value.

property Artifact.id

The artifact’s ID.

Returns:

  • str | None: The id property value.

Boolean flag indicating if the artifact is a link artifact.

True: The artifact is a link artifact to a source artifact. False: The artifact is a source artifact.

Returns:

  • bool: The is_link property value.

property Artifact.linked_artifacts

Returns a list of all the linked artifacts of a source artifact.

If the artifact is a link artifact (artifact.is_link == True), it will return an empty list. Limited to 500 results.

Returns:

  • list[Artifact]: The linked_artifacts property value.

property Artifact.manifest

The artifact’s manifest.

The manifest lists all of its contents, and can’t be changed once the artifact has been logged.

Returns:

  • ArtifactManifest: The manifest property value.

property Artifact.metadata

User-defined artifact metadata.

Structured data associated with the artifact.

Returns:

  • dict: The metadata property value.

property Artifact.name

The artifact name and version of the artifact.

A string with the format {collection}:{alias}. If fetched before an artifact is logged/saved, the name won’t contain the alias. If the artifact is a link, the name will be the name of the linked artifact.

Returns:

  • str: The name property value.

property Artifact.project

The name of the project that the artifact collection belongs to.

If the artifact is a link, the project will be the project of the linked artifact.

Returns:

  • str: The project property value.

property Artifact.qualified_name

The entity/project/name of the artifact.

If the artifact is a link, the qualified name will be the qualified name of the linked artifact path.

Returns:

  • str: The qualified_name property value.

property Artifact.size

The total size of the artifact in bytes.

Includes any references tracked by this artifact.

Returns:

  • int: The size property value.

property Artifact.source_artifact

Returns the source artifact. The source artifact is the original logged artifact.

If the artifact itself is a source artifact (artifact.is_link == False), it will return itself.

Returns:

  • Artifact: The source_artifact property value.

property Artifact.source_collection

The artifact’s source collection.

The source collection is the collection that the artifact was logged from.

Returns:

  • ArtifactCollection: The source_collection property value.

property Artifact.source_entity

The name of the entity of the source artifact.

Returns:

  • str: The source_entity property value.

property Artifact.source_name

The artifact name and version of the source artifact.

A string with the format {source_collection}:{alias}. Before the artifact is saved, contains only the name since the version is not yet known.

Returns:

  • str: The source_name property value.

property Artifact.source_project

The name of the project of the source artifact.

Returns:

  • str: The source_project property value.

property Artifact.source_qualified_name

The source_entity/source_project/source_name of the source artifact.

Returns:

  • str: The source_qualified_name property value.

property Artifact.source_version

The source artifact’s version.

A string with the format v{number}.

Returns:

  • str: The source_version property value.

property Artifact.state

The status of the artifact. One of: “PENDING”, “COMMITTED”, or “DELETED”.

Returns:

  • str: The state property value.

property Artifact.tags

List of one or more tags assigned to this artifact version.

Returns:

  • list[str]: The tags property value.

property Artifact.ttl

The time-to-live (TTL) policy of an artifact.

Artifacts are deleted shortly after a TTL policy’s duration passes. If set to None, the artifact deactivates TTL policies and will be not scheduled for deletion, even if there is a team default TTL. An artifact inherits a TTL policy from the team default if the team administrator defines a default TTL and there is no custom policy set on an artifact.

Raises:

  • ArtifactNotLoggedError: Unable to fetch inherited TTL if the artifact has not been logged or saved.

Returns:

  • timedelta | None: The ttl property value.

property Artifact.type

The artifact’s type. Common types include dataset or model.

Returns:

  • str: The type property value.

property Artifact.updated_at

The time when the artifact was last updated.

Returns:

  • str: The updated_at property value.

property Artifact.url

Constructs the URL of the artifact.

Returns:

  • str: The URL of the artifact.

Returns:

  • str: The url property value.

property Artifact.use_as

Deprecated.

Returns:

  • str | None: The use_as property value.

property Artifact.version

The artifact’s version.

A string with the format v{number}. If the artifact is a link artifact, the version will be from the linked collection.

Returns:

  • str: The version property value.

method Artifact.add

add(
    obj: 'WBValue',
    name: 'StrPath',
    overwrite: 'bool' = False
) → ArtifactManifestEntry

Add wandb.WBValue obj to the artifact.

Args:

  • obj: The object to add. Currently support one of Bokeh, JoinedTable, PartitionedTable, Table, Classes, ImageMask, BoundingBoxes2D, Audio, Image, Video, Html, Object3D
  • name: The path within the artifact to add the object.
  • overwrite: If True, overwrite existing objects with the same file path if applicable.

Returns: The added manifest entry

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.

method Artifact.add_dir

add_dir(
    local_path: 'str',
    name: 'str | None' = None,
    skip_cache: 'bool | None' = False,
    policy: "Literal['mutable', 'immutable'] | None" = 'mutable',
    merge: 'bool' = False
) → None

Add a local directory to the artifact.

Args:

  • local_path: The path of the local directory.
  • name: The subdirectory name within an artifact. The name you specify appears in the W&B App UI nested by artifact’s type. Defaults to the root of the artifact.
  • skip_cache: If set to True, W&B will not copy/move files to the cache while uploading
  • policy: By default, “mutable”.
    • mutable: Create a temporary copy of the file to prevent corruption during upload.
    • immutable: Disable protection, rely on the user not to delete or change the file.
  • merge: If False (default), throws ValueError if a file was already added in a previous add_dir call and its content has changed. If True, overwrites existing files with changed content. Always adds new files and never removes files. To replace an entire directory, pass a name when adding the directory using add_dir(local_path, name=my_prefix) and call remove(my_prefix) to remove the directory, then add it again.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.
  • ValueError: Policy must be “mutable” or “immutable”

method Artifact.add_file

add_file(
    local_path: 'str',
    name: 'str | None' = None,
    is_tmp: 'bool | None' = False,
    skip_cache: 'bool | None' = False,
    policy: "Literal['mutable', 'immutable'] | None" = 'mutable',
    overwrite: 'bool' = False
) → ArtifactManifestEntry

Add a local file to the artifact.

Args:

  • local_path: The path to the file being added.
  • name: The path within the artifact to use for the file being added. Defaults to the basename of the file.
  • is_tmp: If true, then the file is renamed deterministically to avoid collisions.
  • skip_cache: If True, do not copy files to the cache after uploading.
  • policy: By default, set to “mutable”. If set to “mutable”, create a temporary copy of the file to prevent corruption during upload. If set to “immutable”, disable protection and rely on the user not to delete or change the file.
  • overwrite: If True, overwrite the file if it already exists.

Returns: The added manifest entry.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.
  • ValueError: Policy must be “mutable” or “immutable”

method Artifact.add_reference

add_reference(
    uri: 'ArtifactManifestEntry | str',
    name: 'StrPath | None' = None,
    checksum: 'bool' = True,
    max_objects: 'int | None' = None
) → Sequence[ArtifactManifestEntry]

Add a reference denoted by a URI to the artifact.

Unlike files or directories that you add to an artifact, references are not uploaded to W&B. For more information, see Track external files.

By default, the following schemes are supported:

  • http(s): The size and digest of the file will be inferred by the Content-Length and the ETag response headers returned by the server.
  • s3: The checksum and size are pulled from the object metadata. If bucket versioning is enabled, then the version ID is also tracked.
  • gs: The checksum and size are pulled from the object metadata. If bucket versioning is enabled, then the version ID is also tracked.
  • https, domain matching *.blob.core.windows.net
  • Azure: The checksum and size are be pulled from the blob metadata. If storage account versioning is enabled, then the version ID is also tracked.
  • file: The checksum and size are pulled from the file system. This scheme is useful if you have an NFS share or other externally mounted volume containing files you wish to track but not necessarily upload.

For any other scheme, the digest is just a hash of the URI and the size is left blank.

Args:

  • uri: The URI path of the reference to add. The URI path can be an object returned from Artifact.get_entry to store a reference to another artifact’s entry.
  • name: The path within the artifact to place the contents of this reference.
  • checksum: Whether or not to checksum the resource(s) located at the reference URI. Checksumming is strongly recommended as it enables automatic integrity validation. Disabling checksumming will speed up artifact creation but reference directories will not iterated through so the objects in the directory will not be saved to the artifact. We recommend setting checksum=False when adding reference objects, in which case a new version will only be created if the reference URI changes.
  • max_objects: The maximum number of objects to consider when adding a reference that points to directory or bucket store prefix. By default, the maximum number of objects allowed for Amazon S3, GCS, Azure, and local files is 10,000,000. Other URI schemas do not have a maximum.

Returns: The added manifest entries.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.

method Artifact.checkout

checkout(root: 'str | None' = None) → str

Replace the specified root directory with the contents of the artifact.

WARNING: This will delete all files in root that are not included in the artifact.

Args:

  • root: The directory to replace with this artifact’s files.

Returns: The path of the checked out contents.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.delete

delete(delete_aliases: 'bool' = False) → None

Delete an artifact and its files.

If called on a linked artifact, only the link is deleted, and the source artifact is unaffected.

Use artifact.unlink() instead of artifact.delete() to remove a link between a source artifact and a linked artifact.

Args:

  • delete_aliases: If set to True, deletes all aliases associated with the artifact. Otherwise, this raises an exception if the artifact has existing aliases. This parameter is ignored if the artifact is linked (a member of a portfolio collection).

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.download

download(
    root: 'StrPath | None' = None,
    allow_missing_references: 'bool' = False,
    skip_cache: 'bool | None' = None,
    path_prefix: 'StrPath | None' = None,
    multipart: 'bool | None' = None
) → FilePathStr

Download the contents of the artifact to the specified root directory.

Existing files located within root are not modified. Explicitly delete root before you call download if you want the contents of root to exactly match the artifact.

Args:

  • root: The directory W&B stores the artifact’s files.
  • allow_missing_references: If set to True, any invalid reference paths will be ignored while downloading referenced files.
  • skip_cache: If set to True, the artifact cache will be skipped when downloading and W&B will download each file into the default root or specified download directory.
  • path_prefix: If specified, only files with a path that starts with the given prefix will be downloaded. Uses unix format (forward slashes).
  • multipart: If set to None (default), the artifact will be downloaded in parallel using multipart download if individual file size is greater than 2GB. If set to True or False, the artifact will be downloaded in parallel or serially regardless of the file size.

Returns: The path to the downloaded contents.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.file

file(root: 'str | None' = None) → StrPath

Download a single file artifact to the directory you specify with root.

Args:

  • root: The root directory to store the file. Defaults to ./artifacts/self.name/.

Returns: The full path of the downloaded file.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • ValueError: If the artifact contains more than one file.

method Artifact.files

files(names: 'list[str] | None' = None, per_page: 'int' = 50) → ArtifactFiles

Iterate over all files stored in this artifact.

Args:

  • names: The filename paths relative to the root of the artifact you wish to list.
  • per_page: The number of files to return per request.

Returns: An iterator containing File objects.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.finalize

finalize() → None

Finalize the artifact version.

You cannot modify an artifact version once it is finalized because the artifact is logged as a specific artifact version. Create a new artifact version to log more data to an artifact. An artifact is automatically finalized when you log the artifact with log_artifact.

method Artifact.get

get(name: 'str') → WBValue | None

Get the WBValue object located at the artifact relative name.

Args:

  • name: The artifact relative name to retrieve.

Returns: W&B object that can be logged with run.log() and visualized in the W&B UI.

Raises:

  • ArtifactNotLoggedError: if the artifact isn’t logged or the run is offline.

method Artifact.get_added_local_path_name

get_added_local_path_name(local_path: 'str') → str | None

Get the artifact relative name of a file added by a local filesystem path.

Args:

  • local_path: The local path to resolve into an artifact relative name.

Returns: The artifact relative name.

method Artifact.get_entry

get_entry(name: 'StrPath') → ArtifactManifestEntry

Get the entry with the given name.

Args:

  • name: The artifact relative name to get

Returns: A W&B object.

Raises:

  • ArtifactNotLoggedError: if the artifact isn’t logged or the run is offline.
  • KeyError: if the artifact doesn’t contain an entry with the given name.

method Artifact.get_path

get_path(name: 'StrPath') → ArtifactManifestEntry

Deprecated. Use get_entry(name).

method Artifact.is_draft

is_draft() → bool

Check if artifact is not saved.

Returns: Boolean. False if artifact is saved. True if artifact is not saved.

method Artifact.json_encode

json_encode() → dict[str, Any]

Returns the artifact encoded to the JSON format.

Returns: A dict with string keys representing attributes of the artifact.

link(target_path: 'str', aliases: 'list[str] | None' = None) → Artifact

Link this artifact to a portfolio (a promoted collection of artifacts).

Args:

  • target_path: The path to the portfolio inside a project. The target path must adhere to one of the following schemas {portfolio}, {project}/{portfolio} or {entity}/{project}/{portfolio}. To link the artifact to the Model Registry, rather than to a generic portfolio inside a project, set target_path to the following schema {"model-registry"}/{Registered Model Name} or {entity}/{"model-registry"}/{Registered Model Name}.
  • aliases: A list of strings that uniquely identifies the artifact inside the specified portfolio.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

Returns: The linked artifact.

method Artifact.logged_by

logged_by() → Run | None

Get the W&B run that originally logged the artifact.

Returns: The name of the W&B run that originally logged the artifact.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.new_draft

new_draft() → Artifact

Create a new draft artifact with the same content as this committed artifact.

Modifying an existing artifact creates a new artifact version known as an “incremental artifact”. The artifact returned can be extended or modified and logged as a new version.

Returns: An Artifact object.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.new_file

new_file(
    name: 'str',
    mode: 'str' = 'x',
    encoding: 'str | None' = None
) → Iterator[IO]

Open a new temporary file and add it to the artifact.

Args:

  • name: The name of the new file to add to the artifact.
  • mode: The file access mode to use to open the new file.
  • encoding: The encoding used to open the new file.

Returns: A new file object that can be written to. Upon closing, the file is automatically added to the artifact.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.

method Artifact.remove

remove(item: 'StrPath | ArtifactManifestEntry') → None

Remove an item from the artifact.

Args:

  • item: The item to remove. Can be a specific manifest entry or the name of an artifact-relative path. If the item matches a directory all items in that directory will be removed.

Raises:

  • ArtifactFinalizedError: You cannot make changes to the current artifact version because it is finalized. Log a new artifact version instead.
  • FileNotFoundError: If the item isn’t found in the artifact.

method Artifact.save

save(
    project: 'str | None' = None,
    settings: 'wandb.Settings | None' = None
) → None

Persist any changes made to the artifact.

If currently in a run, that run will log this artifact. If not currently in a run, a run of type “auto” is created to track this artifact.

Args:

  • project: A project to use for the artifact in the case that a run is not already in context.
  • settings: A settings object to use when initializing an automatic run. Most commonly used in testing harness.
unlink() → None

Unlink this artifact if it is currently a member of a promoted collection of artifacts.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • ValueError: If the artifact is not linked, in other words, it is not a member of a portfolio collection.

method Artifact.used_by

used_by() → list[Run]

Get a list of the runs that have used this artifact and its linked artifacts.

Returns: A list of Run objects.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.

method Artifact.verify

verify(root: 'str | None' = None) → None

Verify that the contents of an artifact match the manifest.

All files in the directory are checksummed and the checksums are then cross-referenced against the artifact’s manifest. References are not verified.

Args:

  • root: The directory to verify. If None artifact will be downloaded to ‘./artifacts/self.name/’.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • ValueError: If the verification fails.

method Artifact.wait

wait(timeout: 'int | None' = None) → Artifact

If needed, wait for this artifact to finish logging.

Args:

  • timeout: The time, in seconds, to wait.

Returns: An Artifact object.

3.2 - Run

View the source code

class Run

A unit of computation logged by W&B. Typically, this is an ML experiment.

Call wandb.init() to create a new run. wandb.init() starts a new run and returns a wandb.Run object. Each run is associated with a unique ID (run ID). W&B recommends using a context (with statement) manager to automatically finish the run.

For distributed training experiments, you can either track each process separately using one run per process or track all processes to a single run. See Log distributed training experiments for more information.

You can log data to a run with wandb.Run.log(). Anything you log using wandb.Run.log() is sent to that run. See Create an experiment or wandb.init API reference page or more information.

There is a another Run object in the wandb.apis.public namespace. Use this object is to interact with runs that have already been created.

Attributes:

  • summary: (Summary) A summary of the run, which is a dictionary-like object. For more information, see
  • [Log summary metrics](https: //docs.wandb.ai/guides/track/log/log-summary/).

Examples: Create a run with wandb.init():

import wandb

# Start a new run and log some data
# Use context manager (`with` statement) to automatically finish the run
with wandb.init(entity="entity", project="project") as run:
    run.log({"accuracy": acc, "loss": loss})

property Run.config

Config object associated with this run.

Returns:

  • wandb_config.Config: The config property value.

property Run.config_static

Static config object associated with this run.

Returns:

  • wandb_config.ConfigStatic: The config_static property value.

property Run.dir

The directory where files associated with the run are saved.

Returns:

  • str: The dir property value.

property Run.disabled

True if the run is disabled, False otherwise.

Returns:

  • bool: The disabled property value.

property Run.entity

The name of the W&B entity associated with the run.

Entity can be a username or the name of a team or organization.

Returns:

  • str: The entity property value.

property Run.group

Returns the name of the group associated with this run.

Grouping runs together allows related experiments to be organized and visualized collectively in the W&B UI. This is especially useful for scenarios such as distributed training or cross-validation, where multiple runs should be viewed and managed as a unified experiment.

In shared mode, where all processes share the same run object, setting a group is usually unnecessary, since there is only one run and no grouping is required.

Returns:

  • str: The group property value.

property Run.id

Identifier for this run.

Returns:

  • str: The id property value.

property Run.job_type

Name of the job type associated with the run.

View a run’s job type in the run’s Overview page in the W&B App.

You can use this to categorize runs by their job type, such as “training”, “evaluation”, or “inference”. This is useful for organizing and filtering runs in the W&B UI, especially when you have multiple runs with different job types in the same project. For more information, see Organize runs.

Returns:

  • str: The job_type property value.

property Run.name

Display name of the run.

Display names are not guaranteed to be unique and may be descriptive. By default, they are randomly generated.

Returns:

  • str | None: The name property value.

property Run.notes

Notes associated with the run, if there are any.

Notes can be a multiline string and can also use markdown and latex equations inside $$, like $x + 3$.

Returns:

  • str | None: The notes property value.

property Run.offline

True if the run is offline, False otherwise.

Returns:

  • bool: The offline property value.

property Run.path

Path to the run.

Run paths include entity, project, and run ID, in the format entity/project/run_id.

Returns:

  • str: The path property value.

property Run.project

Name of the W&B project associated with the run.

Returns:

  • str: The project property value.

property Run.project_url

URL of the W&B project associated with the run, if there is one.

Offline runs do not have a project URL.

Returns:

  • str | None: The project_url property value.

property Run.resumed

True if the run was resumed, False otherwise.

Returns:

  • bool: The resumed property value.

property Run.settings

A frozen copy of run’s Settings object.

Returns:

  • Settings: The settings property value.

property Run.start_time

Unix timestamp (in seconds) of when the run started.

Returns:

  • float: The start_time property value.

property Run.sweep_id

Identifier for the sweep associated with the run, if there is one.

Returns:

  • str | None: The sweep_id property value.

property Run.sweep_url

URL of the sweep associated with the run, if there is one.

Offline runs do not have a sweep URL.

Returns:

  • str | None: The sweep_url property value.

property Run.tags

Tags associated with the run, if there are any.

Returns:

  • tuple | None: The tags property value.

property Run.url

The url for the W&B run, if there is one.

Offline runs will not have a url.

Returns:

  • str | None: The url property value.

method Run.alert

alert(
    title: 'str',
    text: 'str',
    level: 'str | AlertLevel | None' = None,
    wait_duration: 'int | float | timedelta | None' = None
) → None

Create an alert with the given title and text.

Args:

  • title: The title of the alert, must be less than 64 characters long.
  • text: The text body of the alert.
  • level: The alert level to use, either: INFO, WARN, or ERROR.
  • wait_duration: The time to wait (in seconds) before sending another alert with this title.

method Run.define_metric

define_metric(
    name: 'str',
    step_metric: 'str | wandb_metric.Metric | None' = None,
    step_sync: 'bool | None' = None,
    hidden: 'bool | None' = None,
    summary: 'str | None' = None,
    goal: 'str | None' = None,
    overwrite: 'bool | None' = None
) → wandb_metric.Metric

Customize metrics logged with wandb.Run.log().

Args:

  • name: The name of the metric to customize.
  • step_metric: The name of another metric to serve as the X-axis for this metric in automatically generated charts.
  • step_sync: Automatically insert the last value of step_metric into wandb.Run.log() if it is not provided explicitly. Defaults to True if step_metric is specified.
  • hidden: Hide this metric from automatic plots.
  • summary: Specify aggregate metrics added to summary. Supported aggregations include “min”, “max”, “mean”, “last”, “first”, “best”, “copy” and “none”. “none” prevents a summary from being generated. “best” is used together with the goal parameter, “best” is deprecated and should not be used, use “min” or “max” instead. “copy” is deprecated and should not be used.
  • goal: Specify how to interpret the “best” summary type. Supported options are “minimize” and “maximize”. “goal” is deprecated and should not be used, use “min” or “max” instead.
  • overwrite: If false, then this call is merged with previous define_metric calls for the same metric by using their values for any unspecified parameters. If true, then unspecified parameters overwrite values specified by previous calls.

Returns: An object that represents this call but can otherwise be discarded.

method Run.display

display(height: 'int' = 420, hidden: 'bool' = False) → bool

Display this run in Jupyter.

method Run.finish

finish(exit_code: 'int | None' = None, quiet: 'bool | None' = None) → None

Finish a run and upload any remaining data.

Marks the completion of a W&B run and ensures all data is synced to the server. The run’s final state is determined by its exit conditions and sync status.

Run States:

  • Running: Active run that is logging data and/or sending heartbeats.
  • Crashed: Run that stopped sending heartbeats unexpectedly.
  • Finished: Run completed successfully (exit_code=0) with all data synced.
  • Failed: Run completed with errors (exit_code!=0).
  • Killed: Run was forcibly stopped before it could finish.

Args:

  • exit_code: Integer indicating the run’s exit status. Use 0 for success, any other value marks the run as failed.
  • quiet: Deprecated. Configure logging verbosity using wandb.Settings(quiet=...).

method Run.finish_artifact

finish_artifact(
    artifact_or_path: 'Artifact | str',
    name: 'str | None' = None,
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    distributed_id: 'str | None' = None
) → Artifact

Finishes a non-finalized artifact as output of a run.

Subsequent “upserts” with the same distributed ID will result in a new version.

Args:

  • artifact_or_path: A path to the contents of this artifact, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path You can also pass an Artifact object created by calling wandb.Artifact.
  • name: An artifact name. May be prefixed with entity/project. Valid names can be in the following forms: - name:version - name:alias - digest This will default to the basename of the path prepended with the current run id if not specified.
  • type: The type of artifact to log, examples include dataset, model
  • aliases: Aliases to apply to this artifact, defaults to ["latest"]
  • distributed_id: Unique string that all distributed jobs share. If None, defaults to the run’s group name.

Returns: An Artifact object.

link_artifact(
    artifact: 'Artifact',
    target_path: 'str',
    aliases: 'list[str] | None' = None
) → Artifact

Link the given artifact to a portfolio (a promoted collection of artifacts).

Linked artifacts are visible in the UI for the specified portfolio.

Args:

  • artifact: the (public or local) artifact which will be linked
  • target_path: str - takes the following forms: {portfolio}, {project}/{portfolio}, or {entity}/{project}/{portfolio}
  • aliases: List[str] - optional alias(es) that will only be applied on this linked artifact inside the portfolio. The alias “latest” will always be applied to the latest version of an artifact that is linked.

Returns: The linked artifact.

link_model(
    path: 'StrPath',
    registered_model_name: 'str',
    name: 'str | None' = None,
    aliases: 'list[str] | None' = None
) → Artifact | None

Log a model artifact version and link it to a registered model in the model registry.

Linked model versions are visible in the UI for the specified registered model.

This method will:

  • Check if ’name’ model artifact has been logged. If so, use the artifact version that matches the files located at ‘path’ or log a new version. Otherwise log files under ‘path’ as a new model artifact, ’name’ of type ‘model’.
  • Check if registered model with name ‘registered_model_name’ exists in the ‘model-registry’ project. If not, create a new registered model with name ‘registered_model_name’.
  • Link version of model artifact ’name’ to registered model, ‘registered_model_name’.
  • Attach aliases from ‘aliases’ list to the newly linked model artifact version.

Args:

  • path: (str) A path to the contents of this model, can be in the following forms:
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • registered_model_name: The name of the registered model that the model is to be linked to. A registered model is a collection of model versions linked to the model registry, typically representing a team’s specific ML Task. The entity that this registered model belongs to will be derived from the run.
  • name: The name of the model artifact that files in ‘path’ will be logged to. This will default to the basename of the path prepended with the current run id if not specified.
  • aliases: Aliases that will only be applied on this linked artifact inside the registered model. The alias “latest” will always be applied to the latest version of an artifact that is linked.

Raises:

  • AssertionError: If registered_model_name is a path or if model artifact ’name’ is of a type that does not contain the substring ‘model’.
  • ValueError: If name has invalid special characters.

Returns: The linked artifact if linking was successful, otherwise None.

method Run.log

log(
    data: 'dict[str, Any]',
    step: 'int | None' = None,
    commit: 'bool | None' = None
) → None

Upload run data.

Use log to log data from runs, such as scalars, images, video, histograms, plots, and tables. See Log objects and media for code snippets, best practices, and more.

Basic usage:

import wandb

with wandb.init() as run:
     run.log({"train-loss": 0.5, "accuracy": 0.9})

The previous code snippet saves the loss and accuracy to the run’s history and updates the summary values for these metrics.

Visualize logged data in a workspace at wandb.ai, or locally on a self-hosted instance of the W&B app, or export data to visualize and explore locally, such as in a Jupyter notebook, with the Public API.

Logged values don’t have to be scalars. You can log any W&B supported Data Type such as images, audio, video, and more. For example, you can use wandb.Table to log structured data. See Log tables, visualize and query data tutorial for more details.

W&B organizes metrics with a forward slash (/) in their name into sections named using the text before the final slash. For example, the following results in two sections named “train” and “validate”:

with wandb.init() as run:
     # Log metrics in the "train" section.
     run.log(
         {
             "train/accuracy": 0.9,
             "train/loss": 30,
             "validate/accuracy": 0.8,
             "validate/loss": 20,
         }
     )

Only one level of nesting is supported; run.log({"a/b/c": 1}) produces a section named “a/b”.

run.log() is not intended to be called more than a few times per second. For optimal performance, limit your logging to once every N iterations, or collect data over multiple iterations and log it in a single step.

By default, each call to log creates a new “step”. The step must always increase, and it is not possible to log to a previous step. You can use any metric as the X axis in charts. See Custom log axes for more details.

In many cases, it is better to treat the W&B step like you’d treat a timestamp rather than a training step.

with wandb.init() as run:
     # Example: log an "epoch" metric for use as an X axis.
     run.log({"epoch": 40, "train-loss": 0.5})

It is possible to use multiple wandb.Run.log() invocations to log to the same step with the step and commit parameters. The following are all equivalent:

with wandb.init() as run:
     # Normal usage:
     run.log({"train-loss": 0.5, "accuracy": 0.8})
     run.log({"train-loss": 0.4, "accuracy": 0.9})

     # Implicit step without auto-incrementing:
     run.log({"train-loss": 0.5}, commit=False)
     run.log({"accuracy": 0.8})
     run.log({"train-loss": 0.4}, commit=False)
     run.log({"accuracy": 0.9})

     # Explicit step:
     run.log({"train-loss": 0.5}, step=current_step)
     run.log({"accuracy": 0.8}, step=current_step)
     current_step += 1
     run.log({"train-loss": 0.4}, step=current_step)
     run.log({"accuracy": 0.9}, step=current_step)

Args:

  • data: A dict with str keys and values that are serializable
  • Python objects including: int, float and string; any of the wandb.data_types; lists, tuples and NumPy arrays of serializable Python objects; other dicts of this structure.
  • step: The step number to log. If None, then an implicit auto-incrementing step is used. See the notes in the description.
  • commit: If true, finalize and upload the step. If false, then accumulate data for the step. See the notes in the description. If step is None, then the default is commit=True; otherwise, the default is commit=False.

Examples: For more and more detailed examples, see our guides to logging.

Basic usage

import wandb

with wandb.init() as run:
    run.log({"train-loss": 0.5, "accuracy": 0.9

Incremental logging

import wandb

with wandb.init() as run:
    run.log({"loss": 0.2}, commit=False)
    # Somewhere else when I'm ready to report this step:
    run.log({"accuracy": 0.8})

Histogram

import numpy as np
import wandb

# sample gradients at random from normal distribution
gradients = np.random.randn(100, 100)
with wandb.init() as run:
    run.log({"gradients": wandb.Histogram(gradients)})

Image from NumPy

import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
         image = wandb.Image(pixels, caption=f"random field {i}")
         examples.append(image)
    run.log({"examples": examples})

Image from PIL

import numpy as np
from PIL import Image as PILImage
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
         pixels = np.random.randint(
             low=0,
             high=256,
             size=(100, 100, 3),
             dtype=np.uint8,
         )
         pil_image = PILImage.fromarray(pixels, mode="RGB")
         image = wandb.Image(pil_image, caption=f"random field {i}")
         examples.append(image)
    run.log({"examples": examples})

Video from NumPy

import numpy as np
import wandb

with wandb.init() as run:
    # axes are (time, channel, height, width)
    frames = np.random.randint(
         low=0,
         high=256,
         size=(10, 3, 100, 100),
         dtype=np.uint8,
    )
    run.log({"video": wandb.Video(frames, fps=4)})

Matplotlib plot

from matplotlib import pyplot as plt
import numpy as np
import wandb

with wandb.init() as run:
    fig, ax = plt.subplots()
    x = np.linspace(0, 10)
    y = x * x
    ax.plot(x, y)  # plot y = x^2
    run.log({"chart": fig})

PR Curve

import wandb

with wandb.init() as run:
    run.log({"pr": wandb.plot.pr_curve(y_test, y_probas, labels)})

3D Object

import wandb

with wandb.init() as run:
    run.log(
         {
             "generated_samples": [
                 wandb.Object3D(open("sample.obj")),
                 wandb.Object3D(open("sample.gltf")),
                 wandb.Object3D(open("sample.glb")),
             ]
         }
    )

Raises:

  • wandb.Error: If called before wandb.init().
  • ValueError: If invalid data is passed.

method Run.log_artifact

log_artifact(
    artifact_or_path: 'Artifact | StrPath',
    name: 'str | None' = None,
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    tags: 'list[str] | None' = None
) → Artifact

Declare an artifact as an output of a run.

Args:

  • artifact_or_path: (str or Artifact) A path to the contents of this artifact, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path You can also pass an Artifact object created by calling wandb.Artifact.
  • name: (str, optional) An artifact name. Valid names can be in the following forms: - name:version - name:alias - digest This will default to the basename of the path prepended with the current run id if not specified.
  • type: (str) The type of artifact to log, examples include dataset, model
  • aliases: (list, optional) Aliases to apply to this artifact, defaults to ["latest"]
  • tags: (list, optional) Tags to apply to this artifact, if any.

Returns: An Artifact object.

method Run.log_code

log_code(
    root: 'str | None' = '.',
    name: 'str | None' = None,
    include_fn: 'Callable[[str, str], bool] | Callable[[str], bool]' = <function _is_py_requirements_or_dockerfile at 0x104a016c0>,
    exclude_fn: 'Callable[[str, str], bool] | Callable[[str], bool]' = <function exclude_wandb_fn at 0x10572a680>
) → Artifact | None

Save the current state of your code to a W&B Artifact.

By default, it walks the current directory and logs all files that end with .py.

Args:

  • root: The relative (to os.getcwd()) or absolute path to recursively find code from.
  • name: (str, optional) The name of our code artifact. By default, we’ll name the artifact source-$PROJECT_ID-$ENTRYPOINT_RELPATH. There may be scenarios where you want many runs to share the same artifact. Specifying name allows you to achieve that.
  • include_fn: A callable that accepts a file path and (optionally) root path and returns True when it should be included and False otherwise. This
  • defaults to lambda path, root: path.endswith(".py").
  • exclude_fn: A callable that accepts a file path and (optionally) root path and returns True when it should be excluded and False otherwise. This defaults to a function that excludes all files within <root>/.wandb/ and <root>/wandb/ directories.

Examples: Basic usage

import wandb

with wandb.init() as run:
    run.log_code()

Advanced usage

import wandb

with wandb.init() as run:
    run.log_code(
         root="../",
         include_fn=lambda path: path.endswith(".py") or path.endswith(".ipynb"),
         exclude_fn=lambda path, root: os.path.relpath(path, root).startswith(
             "cache/"
         ),
    )

Returns: An Artifact object if code was logged

method Run.log_model

log_model(
    path: 'StrPath',
    name: 'str | None' = None,
    aliases: 'list[str] | None' = None
) → None

Logs a model artifact containing the contents inside the ‘path’ to a run and marks it as an output to this run.

The name of model artifact can only contain alphanumeric characters, underscores, and hyphens.

Args:

  • path: (str) A path to the contents of this model, can be in the following forms: - /local/directory - /local/directory/file.txt - s3://bucket/path
  • name: A name to assign to the model artifact that the file contents will be added to. This will default to the basename of the path prepended with the current run id if not specified.
  • aliases: Aliases to apply to the created model artifact, defaults to ["latest"]

Raises:

  • ValueError: If name has invalid special characters.

Returns: None

method Run.mark_preempting

mark_preempting() → None

Mark this run as preempting.

Also tells the internal process to immediately report this to server.

method Run.restore

restore(
    name: 'str',
    run_path: 'str | None' = None,
    replace: 'bool' = False,
    root: 'str | None' = None
) → None | TextIO

Download the specified file from cloud storage.

File is placed into the current directory or run directory. By default, will only download the file if it doesn’t already exist.

Args:

  • name: The name of the file.
  • run_path: Optional path to a run to pull files from, i.e. username/project_name/run_id if wandb.init has not been called, this is required.
  • replace: Whether to download the file even if it already exists locally
  • root: The directory to download the file to. Defaults to the current directory or the run directory if wandb.init was called.

Returns: None if it can’t find the file, otherwise a file object open for reading.

Raises:

  • CommError: If W&B can’t connect to the W&B backend.
  • ValueError: If the file is not found or can’t find run_path.

method Run.save

save(
    glob_str: 'str | os.PathLike',
    base_path: 'str | os.PathLike | None' = None,
    policy: 'PolicyName' = 'live'
) → bool | list[str]

Sync one or more files to W&B.

Relative paths are relative to the current working directory.

A Unix glob, such as “myfiles/*”, is expanded at the time save is called regardless of the policy. In particular, new files are not picked up automatically.

A base_path may be provided to control the directory structure of uploaded files. It should be a prefix of glob_str, and the directory structure beneath it is preserved.

When given an absolute path or glob and no base_path, one directory level is preserved as in the example above.

Args:

  • glob_str: A relative or absolute path or Unix glob.
  • base_path: A path to use to infer a directory structure; see examples.
  • policy: One of live, now, or end.
    • live: upload the file as it changes, overwriting the previous version
    • now: upload the file once now
    • end: upload file when the run ends

Returns: Paths to the symlinks created for the matched files.

For historical reasons, this may return a boolean in legacy code.

import wandb

run = wandb.init()

run.save("these/are/myfiles/*")
# => Saves files in a "these/are/myfiles/" folder in the run.

run.save("these/are/myfiles/*", base_path="these")
# => Saves files in an "are/myfiles/" folder in the run.

run.save("/User/username/Documents/run123/*.txt")
# => Saves files in a "run123/" folder in the run. See note below.

run.save("/User/username/Documents/run123/*.txt", base_path="/User")
# => Saves files in a "username/Documents/run123/" folder in the run.

run.save("files/*/saveme.txt")
# => Saves each "saveme.txt" file in an appropriate subdirectory
#    of "files/".

# Explicitly finish the run since a context manager is not used.
run.finish()

method Run.status

status() → RunStatus

Get sync info from the internal backend, about the current run’s sync status.

method Run.unwatch

unwatch(
    models: 'torch.nn.Module | Sequence[torch.nn.Module] | None' = None
) → None

Remove pytorch model topology, gradient and parameter hooks.

Args:

  • models: Optional list of pytorch models that have had watch called on them.

method Run.upsert_artifact

upsert_artifact(
    artifact_or_path: 'Artifact | str',
    name: 'str | None' = None,
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    distributed_id: 'str | None' = None
) → Artifact

Declare (or append to) a non-finalized artifact as output of a run.

Note that you must call run.finish_artifact() to finalize the artifact. This is useful when distributed jobs need to all contribute to the same artifact.

Args:

  • artifact_or_path: A path to the contents of this artifact, can be in the following forms:
    • /local/directory
    • /local/directory/file.txt
    • s3://bucket/path
  • name: An artifact name. May be prefixed with “entity/project”. Defaults to the basename of the path prepended with the current run ID if not specified. Valid names can be in the following forms:
    • name:version
    • name:alias
    • digest
  • type: The type of artifact to log. Common examples include dataset, model.
  • aliases: Aliases to apply to this artifact, defaults to ["latest"].
  • distributed_id: Unique string that all distributed jobs share. If None, defaults to the run’s group name.

Returns: An Artifact object.

method Run.use_artifact

use_artifact(
    artifact_or_name: 'str | Artifact',
    type: 'str | None' = None,
    aliases: 'list[str] | None' = None,
    use_as: 'str | None' = None
) → Artifact

Declare an artifact as an input to a run.

Call download or file on the returned object to get the contents locally.

Args:

  • artifact_or_name: The name of the artifact to use. May be prefixed with the name of the project the artifact was logged to ("" or “/”). If no entity is specified in the name, the Run or API setting’s entity is used. Valid names can be in the following forms
    • name:version
    • name:alias
  • type: The type of artifact to use.
  • aliases: Aliases to apply to this artifact
  • use_as: This argument is deprecated and does nothing.

Returns: An Artifact object.

Examples:

import wandb

run = wandb.init(project="<example>")

# Use an artifact by name and alias
artifact_a = run.use_artifact(artifact_or_name="<name>:<alias>")

# Use an artifact by name and version
artifact_b = run.use_artifact(artifact_or_name="<name>:v<version>")

# Use an artifact by entity/project/name:alias
artifact_c = run.use_artifact(
   artifact_or_name="<entity>/<project>/<name>:<alias>"
)

# Use an artifact by entity/project/name:version
artifact_d = run.use_artifact(
   artifact_or_name="<entity>/<project>/<name>:v<version>"
)

# Explicitly finish the run since a context manager is not used.
run.finish()

method Run.use_model

use_model(name: 'str') → FilePathStr

Download the files logged in a model artifact ’name’.

Args:

  • name: A model artifact name. ’name’ must match the name of an existing logged model artifact. May be prefixed with entity/project/. Valid names can be in the following forms
    • model_artifact_name:version
    • model_artifact_name:alias

Returns:

  • path (str): Path to downloaded model artifact file(s).

Raises:

  • AssertionError: If model artifact ’name’ is of a type that does not contain the substring ‘model’.

method Run.watch

watch(
    models: 'torch.nn.Module | Sequence[torch.nn.Module]',
    criterion: 'torch.F | None' = None,
    log: "Literal['gradients', 'parameters', 'all'] | None" = 'gradients',
    log_freq: 'int' = 1000,
    idx: 'int | None' = None,
    log_graph: 'bool' = False
) → None

Hook into given PyTorch model to monitor gradients and the model’s computational graph.

This function can track parameters, gradients, or both during training.

Args:

  • models: A single model or a sequence of models to be monitored.
  • criterion: The loss function being optimized (optional).
  • log: Specifies whether to log “gradients”, “parameters”, or “all”. Set to None to disable logging. (default=“gradients”).
  • log_freq: Frequency (in batches) to log gradients and parameters. (default=1000)
  • idx: Index used when tracking multiple models with wandb.watch. (default=None)
  • log_graph: Whether to log the model’s computational graph. (default=False)

Raises: ValueError: If wandb.init() has not been called or if any of the models are not instances of torch.nn.Module.

3.3 - Settings

View the source code

class Settings

Settings for the W&B SDK.

This class manages configuration settings for the W&B SDK, ensuring type safety and validation of all settings. Settings are accessible as attributes and can be initialized programmatically, through environment variables (WANDB_ prefix), and with configuration files.

The settings are organized into three categories:

  1. Public settings: Core configuration options that users can safely modify to customize W&B’s behavior for their specific needs.
  2. Internal settings: Settings prefixed with ‘x_’ that handle low-level SDK behavior. These settings are primarily for internal use and debugging. While they can be modified, they are not considered part of the public API and may change without notice in future versions.
  3. Computed settings: Read-only settings that are automatically derived from other settings or the environment.

method Settings.__init__

__init__(
    allow_offline_artifacts: 'bool' = True,
    allow_val_change: 'bool' = False,
    anonymous: 'Literal['must', 'allow', 'never'] | None' = None,
    api_key: 'str | None' = None,
    azure_account_url_to_access_key: 'dict[str, str] | None' = None,
    base_url: 'str' = 'https://api.wandb.ai',
    code_dir: 'str | None' = None,
    config_paths: 'Sequence | None' = None,
    console: 'Literal['auto', 'off', 'wrap', 'redirect', 'wrap_raw', 'wrap_emu']' = 'auto',
    console_multipart: 'bool' = False,
    credentials_file: 'str' = None,
    disable_code: 'bool' = False,
    disable_git: 'bool' = False,
    disable_job_creation: 'bool' = True,
    docker: 'str | None' = None,
    email: 'str | None' = None,
    entity: 'str | None' = None,
    organization: 'str | None' = None,
    force: 'bool' = False,
    fork_from: 'RunMoment | None' = None,
    git_commit: 'str | None' = None,
    git_remote: 'str' = 'origin',
    git_remote_url: 'str | None' = None,
    git_root: 'str | None' = None,
    heartbeat_seconds: 'int' = 30,
    host: 'str | None' = None,
    http_proxy: 'str | None' = None,
    https_proxy: 'str | None' = None,
    identity_token_file: 'str | None' = None,
    ignore_globs: 'Sequence' = (),
    init_timeout: 'float' = 90.0,
    insecure_disable_ssl: 'bool' = False,
    job_name: 'str | None' = None,
    job_source: 'Literal['repo', 'artifact', 'image'] | None' = None,
    label_disable: 'bool' = False,
    launch: 'bool' = False,
    launch_config_path: 'str | None' = None,
    login_timeout: 'float | None' = None,
    mode: 'Literal['online', 'offline', 'shared', 'disabled', 'dryrun', 'run']' = 'online',
    notebook_name: 'str | None' = None,
    program: 'str | None' = None,
    program_abspath: 'str | None' = None,
    program_relpath: 'str | None' = None,
    project: 'str | None' = None,
    quiet: 'bool' = False,
    reinit: 'Literal['default', 'return_previous', 'finish_previous', 'create_new'] | bool' = 'default',
    relogin: 'bool' = False,
    resume: 'Literal['allow', 'must', 'never', 'auto'] | None' = None,
    resume_from: 'RunMoment | None' = None,
    resumed: 'bool' = False,
    root_dir: 'str' = None,
    run_group: 'str | None' = None,
    run_id: 'str | None' = None,
    run_job_type: 'str | None' = None,
    run_name: 'str | None' = None,
    run_notes: 'str | None' = None,
    run_tags: 'tuple[str, Ellipsis] | None' = None,
    sagemaker_disable: 'bool' = False,
    save_code: 'bool | None' = None,
    settings_system: 'str | None' = None,
    max_end_of_run_history_metrics: 'int' = 10,
    max_end_of_run_summary_metrics: 'int' = 10,
    show_colors: 'bool | None' = None,
    show_emoji: 'bool | None' = None,
    show_errors: 'bool' = True,
    show_info: 'bool' = True,
    show_warnings: 'bool' = True,
    silent: 'bool' = False,
    start_method: 'str | None' = None,
    strict: 'bool | None' = None,
    summary_timeout: 'int' = 60,
    summary_warnings: 'int' = 5,
    sweep_id: 'str | None' = None,
    sweep_param_path: 'str | None' = None,
    symlink: 'bool' = None,
    sync_tensorboard: 'bool | None' = None,
    table_raise_on_max_row_limit_exceeded: 'bool' = False,
    username: 'str | None' = None,
    x_cli_only_mode: 'bool' = False,
    x_disable_meta: 'bool' = False,
    x_disable_stats: 'bool' = False,
    x_disable_viewer: 'bool' = False,
    x_disable_machine_info: 'bool' = False,
    x_executable: 'str | None' = None,
    x_extra_http_headers: 'dict[str, str] | None' = None,
    x_file_stream_max_bytes: 'int | None' = None,
    x_file_stream_max_line_bytes: 'int | None' = None,
    x_file_stream_transmit_interval: 'float | None' = None,
    x_file_stream_retry_max: 'int | None' = None,
    x_file_stream_retry_wait_min_seconds: 'float | None' = None,
    x_file_stream_retry_wait_max_seconds: 'float | None' = None,
    x_file_stream_timeout_seconds: 'float | None' = None,
    x_file_transfer_retry_max: 'int | None' = None,
    x_file_transfer_retry_wait_min_seconds: 'float | None' = None,
    x_file_transfer_retry_wait_max_seconds: 'float | None' = None,
    x_file_transfer_timeout_seconds: 'float | None' = None,
    x_files_dir: 'str | None' = None,
    x_flow_control_custom: 'bool | None' = None,
    x_flow_control_disabled: 'bool | None' = None,
    x_graphql_retry_max: 'int | None' = None,
    x_graphql_retry_wait_min_seconds: 'float | None' = None,
    x_graphql_retry_wait_max_seconds: 'float | None' = None,
    x_graphql_timeout_seconds: 'float | None' = None,
    x_internal_check_process: 'float' = 8.0,
    x_jupyter_name: 'str | None' = None,
    x_jupyter_path: 'str | None' = None,
    x_jupyter_root: 'str | None' = None,
    x_label: 'str | None' = None,
    x_live_policy_rate_limit: 'int | None' = None,
    x_live_policy_wait_time: 'int | None' = None,
    x_log_level: 'int' = 20,
    x_network_buffer: 'int | None' = None,
    x_primary: 'bool' = True,
    x_proxies: 'dict[str, str] | None' = None,
    x_runqueue_item_id: 'str | None' = None,
    x_save_requirements: 'bool' = True,
    x_server_side_derived_summary: 'bool' = False,
    x_server_side_expand_glob_metrics: 'bool' = True,
    x_service_transport: 'str | None' = None,
    x_service_wait: 'float' = 30.0,
    x_skip_transaction_log: 'bool' = False,
    x_start_time: 'float | None' = None,
    x_stats_pid: 'int' = 16976,
    x_stats_sampling_interval: 'float' = 15.0,
    x_stats_neuron_monitor_config_path: 'str | None' = None,
    x_stats_dcgm_exporter: 'str | None' = None,
    x_stats_open_metrics_endpoints: 'dict[str, str] | None' = None,
    x_stats_open_metrics_filters: 'dict[str, dict[str, str]] | Sequence | None' = None,
    x_stats_open_metrics_http_headers: 'dict[str, str] | None' = None,
    x_stats_disk_paths: 'Sequence | None' = ('/',),
    x_stats_cpu_count: 'int | None' = None,
    x_stats_cpu_logical_count: 'int | None' = None,
    x_stats_gpu_count: 'int | None' = None,
    x_stats_gpu_type: 'str | None' = None,
    x_stats_gpu_device_ids: 'Sequence | None' = None,
    x_stats_buffer_size: 'int' = 0,
    x_stats_coreweave_metadata_base_url: 'str' = 'http://169.254.169.254',
    x_stats_coreweave_metadata_endpoint: 'str' = '/api/v2/cloud-init/meta-data',
    x_stats_track_process_tree: 'bool' = False,
    x_sync: 'bool' = False,
    x_update_finish_state: 'bool' = True
) → None

Args:

  • allow_offline_artifacts (bool): Flag to allow table artifacts to be synced in offline mode. To revert to the old behavior, set this to False.

  • allow_val_change (bool): Flag to allow modification of Config values after they’ve been set.

  • anonymous (Optional[Literal[‘must’, ‘allow’, ’never’]]): Controls anonymous data logging. Possible values are:

    • “never”: requires you to link your W&B account before tracking the run, so you don’t accidentally create an anonymous run.
    • “allow”: lets a logged-in user track runs with their account, but lets someone who is running the script without a W&B account see the charts in the UI.
    • “must”: sends the run to an anonymous account instead of to a signed-up user account.

  • api_key (Optional[str]): The W&B API key.

  • azure_account_url_to_access_key (Optional[Dict[str, str]]): Mapping of Azure account URLs to their corresponding access keys for Azure integration.

  • base_url (str): The URL of the W&B backend for data synchronization.

  • code_dir (Optional[str]): Directory containing the code to be tracked by W&B.

  • config_paths (Optional[Sequence]): Paths to files to load configuration from into the Config object.

  • console (Literal[‘auto’, ‘off’, ‘wrap’, ‘redirect’, ‘wrap_raw’, ‘wrap_emu’]): The type of console capture to be applied. Possible values are: “auto” - Automatically selects the console capture method based on the system environment and settings. “off” - Disables console capture. “redirect” - Redirects low-level file descriptors for capturing output. “wrap” - Overrides the write methods of sys.stdout/sys.stderr. Will be mapped to either “wrap_raw” or “wrap_emu” based on the state of the system. “wrap_raw” - Same as “wrap” but captures raw output directly instead of through an emulator. Derived from the wrap setting and should not be set manually. “wrap_emu” - Same as “wrap” but captures output through an emulator. Derived from the wrap setting and should not be set manually.

  • console_multipart (bool): Whether to produce multipart console log files.

  • credentials_file (str): Path to file for writing temporary access tokens.

  • disable_code (bool): Whether to disable capturing the code.

  • disable_git (bool): Whether to disable capturing the git state.

  • disable_job_creation (bool): Whether to disable the creation of a job artifact for W&B Launch.

  • docker (Optional[str]): The Docker image used to execute the script.

  • email (Optional[str]): The email address of the user.

  • entity (Optional[str]): The W&B entity, such as a user or a team.

  • organization (Optional[str]): The W&B organization.

  • force (bool): Whether to pass the force flag to wandb.login().

  • fork_from (Optional[RunMoment]): Specifies a point in a previous execution of a run to fork from. The point is defined by the run ID, a metric, and its value. Currently, only the metric ‘_step’ is supported.

  • git_commit (Optional[str]): The git commit hash to associate with the run.

  • git_remote (str): The git remote to associate with the run.

  • git_remote_url (Optional[str]): The URL of the git remote repository.

  • git_root (Optional[str]): Root directory of the git repository.

  • host (Optional[str]): Hostname of the machine running the script.

  • http_proxy (Optional[str]): Custom proxy servers for http requests to W&B.

  • https_proxy (Optional[str]): Custom proxy servers for https requests to W&B.

  • identity_token_file (Optional[str]): Path to file containing an identity token (JWT) for authentication.

  • ignore_globs (Sequence): Unix glob patterns relative to files_dir specifying files to exclude from upload.

  • init_timeout (float): Time in seconds to wait for the wandb.init call to complete before timing out.

  • insecure_disable_ssl (bool): Whether to insecurely disable SSL verification.

  • job_name (Optional[str]): Name of the Launch job running the script.

  • job_source (Optional[Literal[‘repo’, ‘artifact’, ‘image’]]): Source type for Launch.

  • label_disable (bool): Whether to disable automatic labeling features.

  • launch_config_path (Optional[str]): Path to the launch configuration file.

  • login_timeout (Optional[float]): Time in seconds to wait for login operations before timing out.

  • mode (Literal[‘online’, ‘offline’, ‘shared’, ‘disabled’, ‘dryrun’, ‘run’]): The operating mode for W&B logging and synchronization.

  • notebook_name (Optional[str]): Name of the notebook if running in a Jupyter-like environment.

  • program (Optional[str]): Path to the script that created the run, if available.

  • program_abspath (Optional[str]): The absolute path from the root repository directory to the script that created the run. Root repository directory is defined as the directory containing the .git directory, if it exists. Otherwise, it’s the current working directory.

  • program_relpath (Optional[str]): The relative path to the script that created the run.

  • project (Optional[str]): The W&B project ID.

  • quiet (bool): Flag to suppress non-essential output.

  • reinit (Union[Literal[‘default’, ‘return_previous’, ‘finish_previous’, ‘create_new’], bool]): What to do when wandb.init() is called while a run is active. Options:

    • “default”: Use “finish_previous” in notebooks and “return_previous” otherwise.
    • “return_previous”: Return the most recently created run that is not yet finished. This does not update wandb.run; see the “create_new” option.
    • “finish_previous”: Finish all active runs, then return a new run.
    • “create_new”: Create a new run without modifying other active runs. Does not update wandb.run and top-level functions like wandb.log. Because of this, some older integrations that rely on the global run will not work. Can also be a boolean, but this is deprecated. False is the same as “return_previous”, and True is the same as “finish_previous”.

  • relogin (bool): Flag to force a new login attempt.

  • resume (Optional[Literal[‘allow’, ‘must’, ’never’, ‘auto’]]): Specifies the resume behavior for the run. Options:

    • “must”: Resumes from an existing run with the same ID. If no such run exists, it will result in failure.
    • “allow”: Attempts to resume from an existing run with the same ID. If none is found, a new run will be created.
    • “never”: Always starts a new run. If a run with the same ID already exists, it will result in failure.
    • “auto”: Automatically resumes from the most recent failed run on the same machine.

  • resume_from (Optional[RunMoment]): Specifies a point in a previous execution of a run to resume from. The point is defined by the run ID, a metric, and its value. Currently, only the metric ‘_step’ is supported.

  • root_dir (str): The root directory to use as the base for all run-related paths. In particular, this is used to derive the wandb directory and the run directory.

  • run_group (Optional[str]): Group identifier for related runs. Used for grouping runs in the UI.

  • run_id (Optional[str]): The ID of the run.

  • run_job_type (Optional[str]): Type of job being run (e.g., training, evaluation).

  • run_name (Optional[str]): Human-readable name for the run.

  • run_notes (Optional[str]): Additional notes or description for the run.

  • run_tags (Optional[Tuple[str, Ellipsis]]): Tags to associate with the run for organization and filtering.

  • sagemaker_disable (bool): Flag to disable SageMaker-specific functionality.

  • save_code (Optional[bool]): Whether to save the code associated with the run.

  • settings_system (Optional[str]): Path to the system-wide settings file.

  • max_end_of_run_history_metrics (int): Maximum number of history sparklines to display at the end of a run.

  • max_end_of_run_summary_metrics (int): Maximum number of summary metrics to display at the end of a run.

  • show_errors (bool): Whether to display error messages.

  • show_info (bool): Whether to display informational messages.

  • show_warnings (bool): Whether to display warning messages.

  • silent (bool): Flag to suppress all output.

  • strict (Optional[bool]): Whether to enable strict mode for validation and error checking.

  • summary_timeout (int): Time in seconds to wait for summary operations before timing out.

  • sweep_id (Optional[str]): Identifier of the sweep this run belongs to.

  • sweep_param_path (Optional[str]): Path to the sweep parameters configuration.

  • symlink (bool): Whether to use symlinks (True by default except on Windows).

  • sync_tensorboard (Optional[bool]): Whether to synchronize TensorBoard logs with W&B.

  • table_raise_on_max_row_limit_exceeded (bool): Whether to raise an exception when table row limits are exceeded.

  • username (Optional[str]): Username.

  • x_disable_meta (bool): Flag to disable the collection of system metadata.

  • x_disable_stats (bool): Flag to disable the collection of system metrics.

  • x_extra_http_headers (Optional[Dict[str, str]]): Additional headers to add to all outgoing HTTP requests.

  • x_label (Optional[str]): Label to assign to system metrics and console logs collected for the run. This is used to group data by on the frontend and can be used to distinguish data from different processes in a distributed training job.

  • x_primary (bool): Determines whether to save internal wandb files and metadata. In a distributed setting, this is useful for avoiding file overwrites from secondary processes when only system metrics and logs are needed, as the primary process handles the main logging.

  • x_save_requirements (bool): Flag to save the requirements file.

  • x_server_side_derived_summary (bool): Flag to delegate automatic computation of summary from history to the server. This does not disable user-provided summary updates.

  • x_service_wait (float): Time in seconds to wait for the wandb-core internal service to start.

  • x_skip_transaction_log (bool): Whether to skip saving the run events to the transaction log. This is only relevant for online runs. Can be used to reduce the amount of data written to disk. Should be used with caution, as it removes the gurantees about recoverability.

  • x_stats_sampling_interval (float): Sampling interval for the system monitor in seconds.

  • x_stats_dcgm_exporter (Optional[str]): Endpoint to extract Nvidia DCGM metrics from. Options:

    • Extract DCGM-related metrics from a query to the Prometheus /api/v1/query endpoint. It is a common practice to aggregate metrics reported by the instances of the DCGM Exporter running on different nodes in a cluster using Prometheus.
    • TODO: Parse metrics directly from the /metrics endpoint of the DCGM Exporter. Examples:
    • http://localhost:9400/api/v1/query?query=DCGM_FI_DEV_GPU_TEMP{node="l1337", cluster="globular"}.

  • x_stats_open_metrics_endpoints (Optional[Dict[str, str]]): OpenMetrics /metrics endpoints to monitor for system metrics.

  • x_stats_open_metrics_filters (Union[Dict[str, Dict[str, str]], Sequence, None]): Filter to apply to metrics collected from OpenMetrics /metrics endpoints. Supports two formats:

    • {“metric regex pattern, including endpoint name as prefix”: {“label”: “label value regex pattern”}}
    • (“metric regex pattern 1”, “metric regex pattern 2”, …)

  • x_stats_open_metrics_http_headers (Optional[Dict[str, str]]): HTTP headers to add to OpenMetrics requests.

  • x_stats_disk_paths (Optional[Sequence]): System paths to monitor for disk usage.

  • x_stats_cpu_count (Optional[int]): System CPU count. If set, overrides the auto-detected value in the run metadata.

  • x_stats_cpu_logical_count (Optional[int]): Logical CPU count. If set, overrides the auto-detected value in the run metadata.

  • x_stats_gpu_count (Optional[int]): GPU device count. If set, overrides the auto-detected value in the run metadata.

  • x_stats_gpu_type (Optional[str]): GPU device type. If set, overrides the auto-detected value in the run metadata.

  • x_stats_gpu_device_ids (Optional[Sequence]): GPU device indices to monitor. If not set, the system monitor captures metrics for all GPUs. Assumes 0-based indexing matching CUDA/ROCm device enumeration.

  • x_stats_track_process_tree (bool): Monitor the entire process tree for resource usage, starting from x_stats_pid. When True, the system monitor aggregates the RSS, CPU%, and thread count from the process with PID x_stats_pid and all of its descendants. This can have a performance overhead and is disabled by default.

  • x_update_finish_state (bool): Flag to indicate whether this process can update the run’s final state on the server. Set to False in distributed training when only the main process should determine the final state.

Returns: An Settings object.

classmethod Settings.catch_private_settings

catch_private_settings(
    values
) → None

Check if a private field is provided and assign to the corresponding public one.

This is a compatibility layer to handle previous versions of the settings.

method Settings.validate_skip_transaction_log

validate_skip_transaction_log() → None

classmethod Settings.validate_run_tags

validate_run_tags(
    value
) → None

Validate run tags.

Validates that each tag:

  • Is between 1 and 64 characters in length (inclusive)
  • Converts single string values to tuple format
  • Preserves None values

Args:

  • value: A string, list, tuple, or None representing tags

Returns:

  • tuple: A tuple of validated tags, or None

Raises:

  • ValueError: If any tag is empty or exceeds 64 characters
  • <!-- lazydoc-ignore-classmethod: internal –>

property Settings.colab_url

The URL to the Colab notebook, if running in Colab.

Returns:

  • Optional[str]: The colab_url property value.

property Settings.deployment

property Settings.files_dir

Absolute path to the local directory where the run’s files are stored.

Returns:

  • str: The files_dir property value.

property Settings.is_local

property Settings.log_dir

The directory for storing log files.

Returns:

  • str: The log_dir property value.

property Settings.log_internal

The path to the file to use for internal logs.

Returns:

  • str: The log_internal property value.

The path to the symlink to the internal log file of the most recent run.

Returns:

  • str: The log_symlink_internal property value.

The path to the symlink to the user-process log file of the most recent run.

Returns:

  • str: The log_symlink_user property value.

property Settings.log_user

The path to the file to use for user-process logs.

Returns:

  • str: The log_user property value.

property Settings.project_url

The W&B URL where the project can be viewed.

Returns:

  • str: The project_url property value.

property Settings.resume_fname

The path to the resume file.

Returns:

  • str: The resume_fname property value.

property Settings.run_mode

The mode of the run. Can be either “run” or “offline-run”.

Returns:

  • Literal['run', 'offline-run']: The run_mode property value.

property Settings.run_url

The W&B URL where the run can be viewed.

Returns:

  • str: The run_url property value.

property Settings.settings_workspace

The path to the workspace settings file.

Returns:

  • str: The settings_workspace property value.

property Settings.sweep_url

The W&B URL where the sweep can be viewed.

Returns:

  • str: The sweep_url property value.

property Settings.sync_dir

The directory for storing the run’s files.

Returns:

  • str: The sync_dir property value.

property Settings.sync_file

Path to the append-only binary transaction log file.

Returns:

  • str: The sync_file property value.

Path to the symlink to the most recent run’s transaction log file.

Returns:

  • str: The sync_symlink_latest property value.

property Settings.timespec

The time specification for the run.

Returns:

  • str: The timespec property value.

property Settings.wandb_dir

Full path to the wandb directory.

Returns:

  • str: The wandb_dir property value.

method Settings.update_from_system_config_file

update_from_system_config_file() → None

Update settings from the system config file.

method Settings.update_from_workspace_config_file

update_from_workspace_config_file() → None

Update settings from the workspace config file.

method Settings.update_from_env_vars

update_from_env_vars(
    environ: 'Dict[str, Any]'
) → None

Update settings from environment variables.

method Settings.update_from_system_environment

update_from_system_environment() → None

Update settings from the system environment.

method Settings.update_from_dict

update_from_dict(
    settings: 'Dict[str, Any]'
) → None

Update settings from a dictionary.

method Settings.update_from_settings

update_from_settings(
    settings: 'Settings'
) → None

Update settings from another instance of Settings.

method Settings.to_proto

to_proto() → wandb_settings_pb2.Settings

Generate a protobuf representation of the settings.

3.4 - System Metrics Reference

Metrics automatically logged by W&B.

This page provides detailed information about the system metrics that are tracked by the W&B SDK.

CPU

Process CPU Percent (CPU)

Percentage of CPU usage by the process, normalized by the number of available CPUs.

W&B assigns a cpu tag to this metric.

Process CPU Threads

The number of threads utilized by the process.

W&B assigns a proc.cpu.threads tag to this metric.

Disk

By default, the usage metrics are collected for the / path. To configure the paths to be monitored, use the following setting:

run = wandb.init(
    settings=wandb.Settings(
        x_stats_disk_paths=("/System/Volumes/Data", "/home", "/mnt/data"),
    ),
)

Disk Usage Percent

Represents the total system disk usage in percentage for specified paths.

W&B assigns a disk.{path}.usagePercent tag to this metric.

Disk Usage

Represents the total system disk usage in gigabytes (GB) for specified paths. The paths that are accessible are sampled, and the disk usage (in GB) for each path is appended to the samples.

W&B assigns a disk.{path}.usageGB tag to this metric.

Disk In

Indicates the total system disk read in megabytes (MB). The initial disk read bytes are recorded when the first sample is taken. Subsequent samples calculate the difference between the current read bytes and the initial value.

W&B assigns a disk.in tag to this metric.

Disk Out

Represents the total system disk write in megabytes (MB). Similar to Disk In, the initial disk write bytes are recorded when the first sample is taken. Subsequent samples calculate the difference between the current write bytes and the initial value.

W&B assigns a disk.out tag to this metric.

Memory

Process Memory RSS

Represents the Memory Resident Set Size (RSS) in megabytes (MB) for the process. RSS is the portion of memory occupied by a process that is held in main memory (RAM).

W&B assigns a proc.memory.rssMB tag to this metric.

Process Memory Percent

Indicates the memory usage of the process as a percentage of the total available memory.

W&B assigns a proc.memory.percent tag to this metric.

Memory Percent

Represents the total system memory usage as a percentage of the total available memory.

W&B assigns a memory_percent tag to this metric.

Memory Available

Indicates the total available system memory in megabytes (MB).

W&B assigns a proc.memory.availableMB tag to this metric.

Network

Network Sent

Represents the total bytes sent over the network. The initial bytes sent are recorded when the metric is first initialized. Subsequent samples calculate the difference between the current bytes sent and the initial value.

W&B assigns a network.sent tag to this metric.

Network Received

Indicates the total bytes received over the network. Similar to Network Sent, the initial bytes received are recorded when the metric is first initialized. Subsequent samples calculate the difference between the current bytes received and the initial value.

W&B assigns a network.recv tag to this metric.

NVIDIA GPU

In addition to the metrics described below, if the process and/or its descendants use a particular GPU, W&B captures the corresponding metrics as gpu.process.{gpu_index}.{metric_name}

GPU Memory Utilization

Represents the GPU memory utilization in percent for each GPU.

W&B assigns a gpu.{gpu_index}.memory tag to this metric.

GPU Memory Allocated

Indicates the GPU memory allocated as a percentage of the total available memory for each GPU.

W&B assigns a gpu.{gpu_index}.memoryAllocated tag to this metric.

GPU Memory Allocated Bytes

Specifies the GPU memory allocated in bytes for each GPU.

W&B assigns a gpu.{gpu_index}.memoryAllocatedBytes tag to this metric.

GPU Utilization

Reflects the GPU utilization in percent for each GPU.

W&B assigns a gpu.{gpu_index}.gpu tag to this metric.

GPU Temperature

The GPU temperature in Celsius for each GPU.

W&B assigns a gpu.{gpu_index}.temp tag to this metric.

GPU Power Usage Watts

Indicates the GPU power usage in Watts for each GPU.

W&B assigns a gpu.{gpu_index}.powerWatts tag to this metric.

GPU Power Usage Percent

Reflects the GPU power usage as a percentage of its power capacity for each GPU.

W&B assigns a gpu.{gpu_index}.powerPercent tag to this metric.

GPU SM Clock Speed

Represents the clock speed of the Streaming Multiprocessor (SM) on the GPU in MHz. This metric is indicative of the processing speed within the GPU cores responsible for computation tasks.

W&B assigns a gpu.{gpu_index}.smClock tag to this metric.

GPU Memory Clock Speed

Represents the clock speed of the GPU memory in MHz, which influences the rate of data transfer between the GPU memory and processing cores.

W&B assigns a gpu.{gpu_index}.memoryClock tag to this metric.

GPU Graphics Clock Speed

Represents the base clock speed for graphics rendering operations on the GPU, expressed in MHz. This metric often reflects performance during visualization or rendering tasks.

W&B assigns a gpu.{gpu_index}.graphicsClock tag to this metric.

GPU Corrected Memory Errors

Tracks the count of memory errors on the GPU that W&B automatically corrects by error-checking protocols, indicating recoverable hardware issues.

W&B assigns a gpu.{gpu_index}.correctedMemoryErrors tag to this metric.

GPU Uncorrected Memory Errors

Tracks the count of memory errors on the GPU that W&B uncorrected, indicating non-recoverable errors which can impact processing reliability.

W&B assigns a gpu.{gpu_index}.unCorrectedMemoryErrors tag to this metric.

GPU Encoder Utilization

Represents the percentage utilization of the GPU’s video encoder, indicating its load when encoding tasks (for example, video rendering) are running.

W&B assigns a gpu.{gpu_index}.encoderUtilization tag to this metric.

AMD GPU

W&B extracts metrics from the output of the rocm-smi tool supplied by AMD (rocm-smi -a --json).

ROCm 6.x (latest) and 5.x formats are supported. Learn more about ROCm formats in the AMD ROCm documentation. The newer format includes more details.

AMD GPU Utilization

Represents the GPU utilization in percent for each AMD GPU device.

W&B assigns a gpu.{gpu_index}.gpu tag to this metric.

AMD GPU Memory Allocated

Indicates the GPU memory allocated as a percentage of the total available memory for each AMD GPU device.

W&B assigns a gpu.{gpu_index}.memoryAllocated tag to this metric.

AMD GPU Temperature

The GPU temperature in Celsius for each AMD GPU device.

W&B assigns a gpu.{gpu_index}.temp tag to this metric.

AMD GPU Power Usage Watts

The GPU power usage in Watts for each AMD GPU device.

W&B assigns a gpu.{gpu_index}.powerWatts tag to this metric.

AMD GPU Power Usage Percent

Reflects the GPU power usage as a percentage of its power capacity for each AMD GPU device.

W&B assigns a gpu.{gpu_index}.powerPercent to this metric.

Apple ARM Mac GPU

Apple GPU Utilization

Indicates the GPU utilization in percent for Apple GPU devices, specifically on ARM Macs.

W&B assigns a gpu.0.gpu tag to this metric.

Apple GPU Memory Allocated

The GPU memory allocated as a percentage of the total available memory for Apple GPU devices on ARM Macs.

W&B assigns a gpu.0.memoryAllocated tag to this metric.

Apple GPU Temperature

The GPU temperature in Celsius for Apple GPU devices on ARM Macs.

W&B assigns a gpu.0.temp tag to this metric.

Apple GPU Power Usage Watts

The GPU power usage in Watts for Apple GPU devices on ARM Macs.

W&B assigns a gpu.0.powerWatts tag to this metric.

Apple GPU Power Usage Percent

The GPU power usage as a percentage of its power capacity for Apple GPU devices on ARM Macs.

W&B assigns a gpu.0.powerPercent tag to this metric.

Graphcore IPU

Graphcore IPUs (Intelligence Processing Units) are unique hardware accelerators designed specifically for machine intelligence tasks.

IPU Device Metrics

These metrics represent various statistics for a specific IPU device. Each metric has a device ID (device_id) and a metric key (metric_key) to identify it. W&B assigns a ipu.{device_id}.{metric_key} tag to this metric.

Metrics are extracted using the proprietary gcipuinfo library, which interacts with Graphcore’s gcipuinfo binary. The sample method fetches these metrics for each IPU device associated with the process ID (pid). Only the metrics that change over time, or the first time a device’s metrics are fetched, are logged to avoid logging redundant data.

For each metric, the method parse_metric is used to extract the metric’s value from its raw string representation. The metrics are then aggregated across multiple samples using the aggregate method.

The following lists available metrics and their units:

  • Average Board Temperature (average board temp (C)): Temperature of the IPU board in Celsius.
  • Average Die Temperature (average die temp (C)): Temperature of the IPU die in Celsius.
  • Clock Speed (clock (MHz)): The clock speed of the IPU in MHz.
  • IPU Power (ipu power (W)): Power consumption of the IPU in Watts.
  • IPU Utilization (ipu utilisation (%)): Percentage of IPU utilization.
  • IPU Session Utilization (ipu utilisation (session) (%)): IPU utilization percentage specific to the current session.
  • Data Link Speed (speed (GT/s)): Speed of data transmission in Giga-transfers per second.

Google Cloud TPU

Tensor Processing Units (TPUs) are Google’s custom-developed ASICs (Application Specific Integrated Circuits) used to accelerate machine learning workloads.

TPU Memory usage

The current High Bandwidth Memory usage in bytes per TPU core.

W&B assigns a tpu.{tpu_index}.memoryUsageBytes tag to this metric.

TPU Memory usage percentage

The current High Bandwidth Memory usage in percent per TPU core.

W&B assigns a tpu.{tpu_index}.memoryUsageBytes tag to this metric.

TPU Duty cycle

TensorCore duty cycle percentage per TPU device. Tracks the percentage of time over the sample period during which the accelerator TensorCore was actively processing. A larger value means better TensorCore utilization.

W&B assigns a tpu.{tpu_index}.dutyCycle tag to this metric.

AWS Trainium

AWS Trainium is a specialized hardware platform offered by AWS that focuses on accelerating machine learning workloads. The neuron-monitor tool from AWS is used to capture the AWS Trainium metrics.

Trainium Neuron Core Utilization

The utilization percentage of each NeuronCore, reported on a per-core basis.

W&B assigns a trn.{core_index}.neuroncore_utilization tag to this metric.

Trainium Host Memory Usage, Total

The total memory consumption on the host in bytes.

W&B assigns a trn.host_total_memory_usage tag to this metric.

Trainium Neuron Device Total Memory Usage

The total memory usage on the Neuron device in bytes.

W&B assigns a trn.neuron_device_total_memory_usage) tag to this metric.

Trainium Host Memory Usage Breakdown:

The following is a breakdown of memory usage on the host:

  • Application Memory (trn.host_total_memory_usage.application_memory): Memory used by the application.
  • Constants (trn.host_total_memory_usage.constants): Memory used for constants.
  • DMA Buffers (trn.host_total_memory_usage.dma_buffers): Memory used for Direct Memory Access buffers.
  • Tensors (trn.host_total_memory_usage.tensors): Memory used for tensors.

Trainium Neuron Core Memory Usage Breakdown

Detailed memory usage information for each NeuronCore:

  • Constants (trn.{core_index}.neuroncore_memory_usage.constants)
  • Model Code (trn.{core_index}.neuroncore_memory_usage.model_code)
  • Model Shared Scratchpad (trn.{core_index}.neuroncore_memory_usage.model_shared_scratchpad)
  • Runtime Memory (trn.{core_index}.neuroncore_memory_usage.runtime_memory)
  • Tensors (trn.{core_index}.neuroncore_memory_usage.tensors)

OpenMetrics

Capture and log metrics from external endpoints that expose OpenMetrics / Prometheus-compatible data with support for custom regex-based metric filters to be applied to the consumed endpoints.

Refer to Monitoring GPU cluster performance in W&B for a detailed example of how to use this feature in a particular case of monitoring GPU cluster performance with the NVIDIA DCGM-Exporter.

4 - Automations

The W&B Automations API enables programmatic creation and management of automated workflows that respond to events in your ML pipeline. Configure actions to trigger when specific conditions are met, such as model performance thresholds or artifact creation.

Core Classes

Class Description
Automation Represents a saved automation instance with its configuration.
NewAutomation Builder class for creating new automations.

Events (Triggers)

Event Description
OnRunMetric Trigger when a run metric satisfies a defined condition (threshold, change, etc.).
OnCreateArtifact Trigger when a new artifact is created in a collection.
OnLinkArtifact Trigger when an artifact is linked to a registry.
OnAddArtifactAlias Trigger when an alias is added to an artifact.

Actions

Action Description
SendNotification Send notifications via Slack or other integrated channels.
SendWebhook Send HTTP webhook requests to external services.
DoNothing Placeholder action for testing automation configurations.

Filters

Filter Description
MetricThresholdFilter Filter runs based on metric value comparisons against thresholds.
MetricChangeFilter Filter runs based on metric value changes over time.

Common Use Cases

Model Performance Monitoring

  • Alert when model accuracy drops below a threshold
  • Notify team when training loss plateaus
  • Trigger retraining pipelines based on performance metrics

Artifact Management

  • Send notifications when new model versions are created
  • Trigger deployment workflows when artifacts are tagged
  • Automate downstream processing when datasets are updated

Experiment Tracking

  • Alert on failed or crashed runs
  • Notify when long-running experiments complete
  • Send daily summaries of experiment metrics

Integration Workflows

  • Update external tracking systems via webhooks
  • Sync model registry with deployment platforms
  • Trigger CI/CD pipelines based on W&B events

Example Usage

The following example creates an automation that sends a Slack notification whenever a metric called custom-metric exceeds 10. custom-metric is expected to be logged during training using wandb.Run.log({"custom-metric": value }).

import wandb
from wandb.automations import OnRunMetric, RunEvent, SendNotification

api = wandb.Api()

project = api.project("<my-project>", entity="<my-team>")

# Use the first Slack integration for the team
slack_hook = next(api.slack_integrations(entity="<my-team>"))

# Create a trigger event
event = OnRunMetric(
     scope=project,
     filter=RunEvent.metric("custom-metric") > 10,
)

# Create an action that responds to the event
action = SendNotification.from_integration(slack_hook)

# Create the automation
automation = api.create_automation(
     event >> action,
     name="my-automation",
     description="Send a Slack message whenever 'custom-metric' exceeds 10.",
)

For more information about using the Automations API, see the Automations Guide.

4.1 - Automation

View the source code

class Automation

A local instance of a saved W&B automation.

method Automation.__init__

__init__(
    typename__: 'Literal['Trigger']' = 'Trigger',
    id: 'str',
    created_at: 'datetime',
    updated_at: 'datetime | None' = None,
    name: 'str',
    description: 'str | None',
    enabled: 'bool',
    scope: '_ArtifactSequenceScope | _ArtifactPortfolioScope | ProjectScope',
    event: 'SavedEvent',
    action: 'SavedLaunchJobAction | SavedNotificationAction | SavedWebhookAction | SavedNoOpAction'
) → None

Args:

  • typename__ (Literal[‘Trigger’]):
  • id (str):
  • created_at (datetime): The date and time when this automation was created.
  • updated_at (Optional[datetime]): The date and time when this automation was last updated, if applicable.
  • name (str): The name of this automation.
  • description (Optional[str]): An optional description of this automation.
  • enabled (bool): Whether this automation is enabled. Only enabled automations will trigger.
  • scope (Union[_ArtifactSequenceScope, _ArtifactPortfolioScope, ProjectScope]): The scope in which the triggering event must occur.
  • event (SavedEvent): The event that will trigger this automation.
  • action (Union[SavedLaunchJobAction, SavedNotificationAction, SavedWebhookAction, SavedNoOpAction]): The action that will execute when this automation is triggered.

Returns: An Automation object.

4.2 - DoNothing

View the source code

class DoNothing

Defines an automation action that intentionally does nothing.

method DoNothing.__init__

__init__(
    no_op: 'bool' = True,
    action_type: 'Literal[NO_OP]' = NO_OP
) → None

Args:

  • no_op (bool): Placeholder field which exists only to satisfy backend schema requirements. There should never be a need to set this field explicitly, as its value is ignored.
  • action_type (Literal[NO_OP]):

Returns: An DoNothing object.

4.3 - MetricChangeFilter

View the source code

class MetricChangeFilter

Defines a filter that compares a change in a run metric against a user-defined threshold.

The change is calculated over “tumbling” windows, i.e. the difference between the current window and the non-overlapping prior window.

method MetricChangeFilter.__init__

__init__(
    name: 'str',
    agg: 'Agg | None' = None,
    window: 'int' = 1,
    cmp: 'None' = None,
    threshold: 'Annotated | Annotated',
    prior_window: 'int' = None,
    change_type: 'ChangeType',
    change_dir: 'ChangeDir'
) → None

Args:

  • name (str):
  • agg (Optional[Agg]):
  • window (int):
  • cmp (None): Ignored.
  • threshold (Union[Annotated, Annotated]):
  • prior_window (int): Size of the prior window over which the metric is aggregated (ignored if agg is None). If omitted, defaults to the size of the current window.
  • change_type (ChangeType):
  • change_dir (ChangeDir):

Returns: An MetricChangeFilter object.

4.4 - MetricThresholdFilter

View the source code

class MetricThresholdFilter

Defines a filter that compares a run metric against a user-defined threshold value.

method MetricThresholdFilter.__init__

__init__(
    name: 'str',
    agg: 'Agg | None' = None,
    window: 'int' = 1,
    cmp: 'Literal['$gte', '$gt', '$lt', '$lte']',
    threshold: 'Annotated | Annotated'
) → None

Args:

  • name (str):
  • agg (Optional[Agg]):
  • window (int):
  • cmp (Literal[’$gte’, ‘$gt’, ‘$lt’, ‘$lte’]): Comparison operator used to compare the metric value (left) vs. the threshold value (right).
  • threshold (Union[Annotated, Annotated]):

Returns: An MetricThresholdFilter object.

4.5 - NewAutomation

View the source code

class NewAutomation

A new automation to be created.

method NewAutomation.__init__

__init__(
    name: 'str | None' = None,
    description: 'str | None' = None,
    enabled: 'bool | None' = None,
    event: 'Annotated | None' = None,
    action: 'Annotated | None' = None
) → None

Args:

  • name (Optional[str]): The name of this automation.
  • description (Optional[str]): An optional description of this automation.
  • enabled (Optional[bool]): Whether this automation is enabled. Only enabled automations will trigger.
  • event (Optional[Annotated]): The event that will trigger this automation.
  • action (Optional[Annotated]): The action that will execute when this automation is triggered.

Returns: An NewAutomation object.

property NewAutomation.scope

The scope in which the triggering event must occur.

Returns:

  • Optional[AutomationScope]: The scope property value.

4.6 - OnAddArtifactAlias

View the source code

class OnAddArtifactAlias

A new alias is assigned to an artifact.

method OnAddArtifactAlias.__init__

__init__(
    event_type: 'Literal[ADD_ARTIFACT_ALIAS]' = ADD_ARTIFACT_ALIAS,
    scope: '_ArtifactSequenceScope | _ArtifactPortfolioScope | ProjectScope',
    filter: 'And | Or | Nor | Not | Lt | Gt | Lte | Gte | Eq | Ne | In | NotIn | Exists | Regex | Contains | dict[str, Any] | FilterExpr' = And([])
) → None

Args:

  • event_type (Literal[ADD_ARTIFACT_ALIAS]):
  • scope (Union[_ArtifactSequenceScope, _ArtifactPortfolioScope, ProjectScope]): The scope of the event.
  • filter (Union[And, Or, Nor, Not, Lt, Gt, Lte, Gte, Eq, Ne, In, NotIn, Exists, Regex, Contains, Dict[str, Any], FilterExpr]): Additional condition(s), if any, that must be met for this event to trigger an automation.

Returns: An OnAddArtifactAlias object.

4.7 - OnCreateArtifact

View the source code

class OnCreateArtifact

A new artifact is created.

method OnCreateArtifact.__init__

__init__(
    event_type: 'Literal[CREATE_ARTIFACT]' = CREATE_ARTIFACT,
    scope: '_ArtifactSequenceScope | _ArtifactPortfolioScope',
    filter: 'And | Or | Nor | Not | Lt | Gt | Lte | Gte | Eq | Ne | In | NotIn | Exists | Regex | Contains | dict[str, Any] | FilterExpr' = And([])
) → None

Args:

  • event_type (Literal[CREATE_ARTIFACT]):
  • scope (Union[_ArtifactSequenceScope, _ArtifactPortfolioScope]): The scope of the event: only artifact collections are valid scopes for this event.
  • filter (Union[And, Or, Nor, Not, Lt, Gt, Lte, Gte, Eq, Ne, In, NotIn, Exists, Regex, Contains, Dict[str, Any], FilterExpr]): Additional condition(s), if any, that must be met for this event to trigger an automation.

Returns: An OnCreateArtifact object.

4.8 - OnLinkArtifact

View the source code

class OnLinkArtifact

A new artifact is linked to a collection.

method OnLinkArtifact.__init__

__init__(
    event_type: 'Literal[LINK_ARTIFACT]' = LINK_ARTIFACT,
    scope: '_ArtifactSequenceScope | _ArtifactPortfolioScope | ProjectScope',
    filter: 'And | Or | Nor | Not | Lt | Gt | Lte | Gte | Eq | Ne | In | NotIn | Exists | Regex | Contains | dict[str, Any] | FilterExpr' = And([])
) → None

Args:

  • event_type (Literal[LINK_ARTIFACT]):
  • scope (Union[_ArtifactSequenceScope, _ArtifactPortfolioScope, ProjectScope]): The scope of the event.
  • filter (Union[And, Or, Nor, Not, Lt, Gt, Lte, Gte, Eq, Ne, In, NotIn, Exists, Regex, Contains, Dict[str, Any], FilterExpr]): Additional condition(s), if any, that must be met for this event to trigger an automation.

Returns: An OnLinkArtifact object.

4.9 - OnRunMetric

View the source code

class OnRunMetric

A run metric satisfies a user-defined condition.

method OnRunMetric.__init__

__init__(
    event_type: 'Literal[RUN_METRIC_THRESHOLD, RUN_METRIC_CHANGE]',
    scope: 'ProjectScope',
    filter: 'RunMetricFilter'
) → None

Args:

  • event_type (Literal[RUN_METRIC_THRESHOLD, RUN_METRIC_CHANGE]):
  • scope (ProjectScope): The scope of the event: only projects are valid scopes for this event.
  • filter (RunMetricFilter): Run and/or metric condition(s) that must be satisfied for this event to trigger an automation.

Returns: An OnRunMetric object.

4.10 - SendNotification

View the source code

class SendNotification

Defines an automation action that sends a (Slack) notification.

method SendNotification.__init__

__init__(
    integration_id: 'str',
    title: 'str' = '',
    message: 'str' = '',
    severity: 'AlertSeverity' = <AlertSeverity.INFO: 'INFO'>,
    action_type: 'Literal[NOTIFICATION]' = NOTIFICATION
) → None

Args:

  • integration_id (str): The ID of the Slack integration that will be used to send the notification.
  • title (str): The title of the sent notification.
  • message (str): The message body of the sent notification.
  • severity (AlertSeverity): The severity (INFO, WARN, ERROR) of the sent notification.
  • action_type (Literal[NOTIFICATION]):

Returns: An SendNotification object.

classmethod SendNotification.from_integration

from_integration(
    integration: 'SlackIntegration',
    title: 'str' = '',
    text: 'str' = '',
    level: 'AlertSeverity' = <AlertSeverity.INFO: 'INFO'>
) → Self

Define a notification action that sends to the given (Slack) integration.

4.11 - SendWebhook

View the source code

class SendWebhook

Defines an automation action that sends a webhook request.

method SendWebhook.__init__

__init__(
    integration_id: 'str',
    request_payload: 'Annotated | None' = None,
    action_type: 'Literal[GENERIC_WEBHOOK]' = GENERIC_WEBHOOK
) → None

Args:

  • integration_id (str): The ID of the webhook integration that will be used to send the request.
  • request_payload (Optional[Annotated]): The payload, possibly with template variables, to send in the webhook request.
  • action_type (Literal[GENERIC_WEBHOOK]):

Returns: An SendWebhook object.

classmethod SendWebhook.from_integration

from_integration(
    integration: 'WebhookIntegration',
    payload: 'Optional[SerializedToJson[dict[str, Any]]]' = None
) → Self

Define a webhook action that sends to the given (webhook) integration.

5 - Custom Charts

Custom charts in W&B are programmable through a group of functions in the wandb.plot namespace. These functions create interactive visualizations in W&B project dashboards, and support common ML visualizations such as confusion matrices, ROC curves, and distribution plots.

Available Chart Functions

Function Description
confusion_matrix() Generate confusion matrices for classification performance visualization.
roc_curve() Create Receiver Operating Characteristic curves for binary and multi-class classifiers.
pr_curve() Build Precision-Recall curves for classifier evaluation.
line() Construct line charts from tabular data.
scatter() Create scatter plots for variable relationships.
bar() Generate bar charts for categorical data.
histogram() Build histograms for data distribution analysis.
line_series() Plot multiple line series on a single chart.
plot_table() Create custom charts using Vega-Lite specifications.

Common Use Cases

Model Evaluation

  • Classification: confusion_matrix(), roc_curve(), and pr_curve() for classifier evaluation
  • Regression: scatter() for prediction vs. actual plots and histogram() for residual analysis
  • Vega-Lite Charts: plot_table() for domain-specific visualizations

Training Monitoring

  • Learning Curves: line() or line_series() for tracking metrics over epochs
  • Hyperparameter Comparison: bar() charts for comparing configurations

Data Analysis

  • Distribution Analysis: histogram() for feature distributions
  • Correlation Analysis: scatter() plots for variable relationships

Getting Started

Log a confusion matrix

import wandb

y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 2, 0, 1, 1]
class_names = ["class_0", "class_1", "class_2"]

# Initialize a run
with wandb.init(project="custom-charts-demo") as run:
    run.log({
        "conf_mat": wandb.plot.confusion_matrix(
            y_true=y_true, 
            preds=y_pred,
            class_names=class_names
        )
    })

Build a scatter plot for feature analysis

import numpy as np

# Generate synthetic data
data_table = wandb.Table(columns=["feature_1", "feature_2", "label"])

with wandb.init(project="custom-charts-demo") as run:

    for _ in range(100):
        data_table.add_data(
            np.random.randn(), 
            np.random.randn(), 
            np.random.choice(["A", "B"])
        )

    run.log({
        "feature_scatter": wandb.plot.scatter(
            data_table, x="feature_1", y="feature_2",
            title="Feature Distribution"
        )
    })

5.1 - bar()

View the source code

function bar

bar(
    table: 'wandb.Table',
    label: 'str',
    value: 'str',
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a bar chart from a wandb.Table of data.

Args:

  • table: A table containing the data for the bar chart.
  • label: The name of the column to use for the labels of each bar.
  • value: The name of the column to use for the values of each bar.
  • title: The title of the bar chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import random
import wandb

# Generate random data for the table
data = [
    ["car", random.uniform(0, 1)],
    ["bus", random.uniform(0, 1)],
    ["road", random.uniform(0, 1)],
    ["person", random.uniform(0, 1)],
]

# Create a table with the data
table = wandb.Table(data=data, columns=["class", "accuracy"])

# Initialize a W&B run and log the bar plot
with wandb.init(project="bar_chart") as run:
    # Create a bar plot from the table
    bar_plot = wandb.plot.bar(
         table=table,
         label="class",
         value="accuracy",
         title="Object Classification Accuracy",
    )

    # Log the bar chart to W&B
    run.log({"bar_plot": bar_plot})

5.2 - confusion_matrix()

View the source code

function confusion_matrix

confusion_matrix(
    probs: 'Sequence[Sequence[float]] | None' = None,
    y_true: 'Sequence[T] | None' = None,
    preds: 'Sequence[T] | None' = None,
    class_names: 'Sequence[str] | None' = None,
    title: 'str' = 'Confusion Matrix Curve',
    split_table: 'bool' = False
) → CustomChart

Constructs a confusion matrix from a sequence of probabilities or predictions.

Args:

  • probs: A sequence of predicted probabilities for each class. The sequence shape should be (N, K) where N is the number of samples and K is the number of classes. If provided, preds should not be provided.
  • y_true: A sequence of true labels.
  • preds: A sequence of predicted class labels. If provided, probs should not be provided.
  • class_names: Sequence of class names. If not provided, class names will be defined as “Class_1”, “Class_2”, etc.
  • title: Title of the confusion matrix chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Raises:

  • ValueError: If both probs and preds are provided or if the number of predictions and true labels are not equal. If the number of unique predicted classes exceeds the number of class names or if the number of unique true labels exceeds the number of class names.
  • wandb.Error: If numpy is not installed.

Examples: Logging a confusion matrix with random probabilities for wildlife classification:

import numpy as np
import wandb

# Define class names for wildlife
wildlife_class_names = ["Lion", "Tiger", "Elephant", "Zebra"]

# Generate random true labels (0 to 3 for 10 samples)
wildlife_y_true = np.random.randint(0, 4, size=10)

# Generate random probabilities for each class (10 samples x 4 classes)
wildlife_probs = np.random.rand(10, 4)
wildlife_probs = np.exp(wildlife_probs) / np.sum(
    np.exp(wildlife_probs),
    axis=1,
    keepdims=True,
)

# Initialize W&B run and log confusion matrix
with wandb.init(project="wildlife_classification") as run:
    confusion_matrix = wandb.plot.confusion_matrix(
         probs=wildlife_probs,
         y_true=wildlife_y_true,
         class_names=wildlife_class_names,
         title="Wildlife Classification Confusion Matrix",
    )
    run.log({"wildlife_confusion_matrix": confusion_matrix})

In this example, random probabilities are used to generate a confusion matrix.

Logging a confusion matrix with simulated model predictions and 85% accuracy:

import numpy as np
import wandb

# Define class names for wildlife
wildlife_class_names = ["Lion", "Tiger", "Elephant", "Zebra"]

# Simulate true labels for 200 animal images (imbalanced distribution)
wildlife_y_true = np.random.choice(
    [0, 1, 2, 3],
    size=200,
    p=[0.2, 0.3, 0.25, 0.25],
)

# Simulate model predictions with 85% accuracy
wildlife_preds = [
    y_t
    if np.random.rand() < 0.85
    else np.random.choice([x for x in range(4) if x != y_t])
    for y_t in wildlife_y_true
]

# Initialize W&B run and log confusion matrix
with wandb.init(project="wildlife_classification") as run:
    confusion_matrix = wandb.plot.confusion_matrix(
         preds=wildlife_preds,
         y_true=wildlife_y_true,
         class_names=wildlife_class_names,
         title="Simulated Wildlife Classification Confusion Matrix",
    )
    run.log({"wildlife_confusion_matrix": confusion_matrix})

In this example, predictions are simulated with 85% accuracy to generate a confusion matrix.

5.3 - histogram

View the source code

function histogram

histogram(
    table: 'wandb.Table',
    value: 'str',
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a histogram chart from a W&B Table.

Args:

  • table: The W&B Table containing the data for the histogram.
  • value: The label for the bin axis (x-axis).
  • title: The title of the histogram plot.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import math
import random
import wandb

# Generate random data
data = [[i, random.random() + math.sin(i / 10)] for i in range(100)]

# Create a W&B Table
table = wandb.Table(
    data=data,
    columns=["step", "height"],
)

# Create a histogram plot
histogram = wandb.plot.histogram(
    table,
    value="height",
    title="My Histogram",
)

# Log the histogram plot to W&B
with wandb.init(...) as run:
    run.log({"histogram-plot1": histogram})

5.4 - line_series()

View the source code

function line_series

line_series(
    xs: 'Iterable[Iterable[Any]] | Iterable[Any]',
    ys: 'Iterable[Iterable[Any]]',
    keys: 'Iterable[str] | None' = None,
    title: 'str' = '',
    xname: 'str' = 'x',
    split_table: 'bool' = False
) → CustomChart

Constructs a line series chart.

Args:

  • xs: Sequence of x values. If a singular array is provided, all y values are plotted against that x array. If an array of arrays is provided, each y value is plotted against the corresponding x array.
  • ys: Sequence of y values, where each iterable represents a separate line series.
  • keys: Sequence of keys for labeling each line series. If not provided, keys will be automatically generated as “line_1”, “line_2”, etc.
  • title: Title of the chart.
  • xname: Label for the x-axis.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Examples: Logging a single x array where all y series are plotted against the same x values:

import wandb

# Initialize W&B run
with wandb.init(project="line_series_example") as run:
    # x values shared across all y series
    xs = list(range(10))

    # Multiple y series to plot
    ys = [
         [i for i in range(10)],  # y = x
         [i**2 for i in range(10)],  # y = x^2
         [i**3 for i in range(10)],  # y = x^3
    ]

    # Generate and log the line series chart
    line_series_chart = wandb.plot.line_series(
         xs,
         ys,
         title="title",
         xname="step",
    )
    run.log({"line-series-single-x": line_series_chart})

In this example, a single xs series (shared x-values) is used for all ys series. This results in each y-series being plotted against the same x-values (0-9).

Logging multiple x arrays where each y series is plotted against its corresponding x array:

import wandb

# Initialize W&B run
with wandb.init(project="line_series_example") as run:
    # Separate x values for each y series
    xs = [
         [i for i in range(10)],  # x for first series
         [2 * i for i in range(10)],  # x for second series (stretched)
         [3 * i for i in range(10)],  # x for third series (stretched more)
    ]

    # Corresponding y series
    ys = [
         [i for i in range(10)],  # y = x
         [i**2 for i in range(10)],  # y = x^2
         [i**3 for i in range(10)],  # y = x^3
    ]

    # Generate and log the line series chart
    line_series_chart = wandb.plot.line_series(
         xs, ys, title="Multiple X Arrays Example", xname="Step"
    )
    run.log({"line-series-multiple-x": line_series_chart})

In this example, each y series is plotted against its own unique x series. This allows for more flexibility when the x values are not uniform across the data series.

Customizing line labels using keys:

import wandb

# Initialize W&B run
with wandb.init(project="line_series_example") as run:
    xs = list(range(10))  # Single x array
    ys = [
         [i for i in range(10)],  # y = x
         [i**2 for i in range(10)],  # y = x^2
         [i**3 for i in range(10)],  # y = x^3
    ]

    # Custom labels for each line
    keys = ["Linear", "Quadratic", "Cubic"]

    # Generate and log the line series chart
    line_series_chart = wandb.plot.line_series(
         xs,
         ys,
         keys=keys,  # Custom keys (line labels)
         title="Custom Line Labels Example",
         xname="Step",
    )
    run.log({"line-series-custom-keys": line_series_chart})

This example shows how to provide custom labels for the lines using the keys argument. The keys will appear in the legend as “Linear”, “Quadratic”, and “Cubic”.

5.5 - line()

View the source code

function line

line(
    table: 'wandb.Table',
    x: 'str',
    y: 'str',
    stroke: 'str | None' = None,
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a customizable line chart.

Args:

  • table: The table containing data for the chart.
  • x: Column name for the x-axis values.
  • y: Column name for the y-axis values.
  • stroke: Column name to differentiate line strokes (e.g., for grouping lines).
  • title: Title of the chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import math
import random
import wandb

# Create multiple series of data with different patterns
data = []
for i in range(100):
     # Series 1: Sinusoidal pattern with random noise
     data.append([i, math.sin(i / 10) + random.uniform(-0.1, 0.1), "series_1"])
     # Series 2: Cosine pattern with random noise
     data.append([i, math.cos(i / 10) + random.uniform(-0.1, 0.1), "series_2"])
     # Series 3: Linear increase with random noise
     data.append([i, i / 10 + random.uniform(-0.5, 0.5), "series_3"])

# Define the columns for the table
table = wandb.Table(data=data, columns=["step", "value", "series"])

# Initialize wandb run and log the line chart
with wandb.init(project="line_chart_example") as run:
     line_chart = wandb.plot.line(
         table=table,
         x="step",
         y="value",
         stroke="series",  # Group by the "series" column
         title="Multi-Series Line Plot",
     )
     run.log({"line-chart": line_chart})

5.6 - plot_table()

View the source code

function plot_table

plot_table(
    vega_spec_name: 'str',
    data_table: 'wandb.Table',
    fields: 'dict[str, Any]',
    string_fields: 'dict[str, Any] | None' = None,
    split_table: 'bool' = False
) → CustomChart

Creates a custom charts using a Vega-Lite specification and a wandb.Table.

This function creates a custom chart based on a Vega-Lite specification and a data table represented by a wandb.Table object. The specification needs to be predefined and stored in the W&B backend. The function returns a custom chart object that can be logged to W&B using wandb.Run.log().

Args:

  • vega_spec_name: The name or identifier of the Vega-Lite spec that defines the visualization structure.
  • data_table: A wandb.Table object containing the data to be visualized.
  • fields: A mapping between the fields in the Vega-Lite spec and the corresponding columns in the data table to be visualized.
  • string_fields: A dictionary for providing values for any string constants required by the custom visualization.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass the chart object as argument to wandb.Run.log().

Raises:

  • wandb.Error: If data_table is not a wandb.Table object.

Example:

# Create a custom chart using a Vega-Lite spec and the data table.
import wandb

data = [[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]
table = wandb.Table(data=data, columns=["x", "y"])
fields = {"x": "x", "y": "y", "title": "MY TITLE"}

with wandb.init() as run:
   # Training code goes here

   # Create a custom title with `string_fields`.
   my_custom_chart = wandb.plot_table(
        vega_spec_name="wandb/line/v0",
        data_table=table,
        fields=fields,
        string_fields={"title": "Title"},
   )

   run.log({"custom_chart": my_custom_chart})

5.7 - pr_curve()

View the source code

function pr_curve

pr_curve(
    y_true: 'Iterable[T] | None' = None,
    y_probas: 'Iterable[numbers.Number] | None' = None,
    labels: 'list[str] | None' = None,
    classes_to_plot: 'list[T] | None' = None,
    interp_size: 'int' = 21,
    title: 'str' = 'Precision-Recall Curve',
    split_table: 'bool' = False
) → CustomChart

Constructs a Precision-Recall (PR) curve.

The Precision-Recall curve is particularly useful for evaluating classifiers on imbalanced datasets. A high area under the PR curve signifies both high precision (a low false positive rate) and high recall (a low false negative rate). The curve provides insights into the balance between false positives and false negatives at various threshold levels, aiding in the assessment of a model’s performance.

Args:

  • y_true: True binary labels. The shape should be (num_samples,).
  • y_probas: Predicted scores or probabilities for each class. These can be probability estimates, confidence scores, or non-thresholded decision values. The shape should be (num_samples, num_classes).
  • labels: Optional list of class names to replace numeric values in y_true for easier plot interpretation. For example, labels = ['dog', 'cat', 'owl'] will replace 0 with ‘dog’, 1 with ‘cat’, and 2 with ‘owl’ in the plot. If not provided, numeric values from y_true will be used.
  • classes_to_plot: Optional list of unique class values from y_true to be included in the plot. If not specified, all unique classes in y_true will be plotted.
  • interp_size: Number of points to interpolate recall values. The recall values will be fixed to interp_size uniformly distributed points in the range [0, 1], and the precision will be interpolated accordingly.
  • title: Title of the plot. Defaults to “Precision-Recall Curve”.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Raises:

  • wandb.Error: If NumPy, pandas, or scikit-learn is not installed.

Example:

import wandb

# Example for spam detection (binary classification)
y_true = [0, 1, 1, 0, 1]  # 0 = not spam, 1 = spam
y_probas = [
    [0.9, 0.1],  # Predicted probabilities for the first sample (not spam)
    [0.2, 0.8],  # Second sample (spam), and so on
    [0.1, 0.9],
    [0.8, 0.2],
    [0.3, 0.7],
]

labels = ["not spam", "spam"]  # Optional class names for readability

with wandb.init(project="spam-detection") as run:
    pr_curve = wandb.plot.pr_curve(
         y_true=y_true,
         y_probas=y_probas,
         labels=labels,
         title="Precision-Recall Curve for Spam Detection",
    )
    run.log({"pr-curve": pr_curve})

5.8 - roc_curve()

View the source code

function roc_curve

roc_curve(
    y_true: 'Sequence[numbers.Number]',
    y_probas: 'Sequence[Sequence[float]] | None' = None,
    labels: 'list[str] | None' = None,
    classes_to_plot: 'list[numbers.Number] | None' = None,
    title: 'str' = 'ROC Curve',
    split_table: 'bool' = False
) → CustomChart

Constructs Receiver Operating Characteristic (ROC) curve chart.

Args:

  • y_true: The true class labels (ground truth) for the target variable. Shape should be (num_samples,).
  • y_probas: The predicted probabilities or decision scores for each class. Shape should be (num_samples, num_classes).
  • labels: Human-readable labels corresponding to the class indices in y_true. For example, if labels=['dog', 'cat'], class 0 will be displayed as ‘dog’ and class 1 as ‘cat’ in the plot. If None, the raw class indices from y_true will be used. Default is None.
  • classes_to_plot: A subset of unique class labels to include in the ROC curve. If None, all classes in y_true will be plotted. Default is None.
  • title: Title of the ROC curve plot. Default is “ROC Curve”.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Raises:

  • wandb.Error: If numpy, pandas, or scikit-learn are not found.

Example:

import numpy as np
import wandb

# Simulate a medical diagnosis classification problem with three diseases
n_samples = 200
n_classes = 3

# True labels: assign "Diabetes", "Hypertension", or "Heart Disease" to
# each sample
disease_labels = ["Diabetes", "Hypertension", "Heart Disease"]
# 0: Diabetes, 1: Hypertension, 2: Heart Disease
y_true = np.random.choice([0, 1, 2], size=n_samples)

# Predicted probabilities: simulate predictions, ensuring they sum to 1
# for each sample
y_probas = np.random.dirichlet(np.ones(n_classes), size=n_samples)

# Specify classes to plot (plotting all three diseases)
classes_to_plot = [0, 1, 2]

# Initialize a W&B run and log a ROC curve plot for disease classification
with wandb.init(project="medical_diagnosis") as run:
   roc_plot = wandb.plot.roc_curve(
        y_true=y_true,
        y_probas=y_probas,
        labels=disease_labels,
        classes_to_plot=classes_to_plot,
        title="ROC Curve for Disease Classification",
   )
   run.log({"roc-curve": roc_plot})

5.9 - scatter()

View the source code

function scatter

scatter(
    table: 'wandb.Table',
    x: 'str',
    y: 'str',
    title: 'str' = '',
    split_table: 'bool' = False
) → CustomChart

Constructs a scatter plot from a wandb.Table of data.

Args:

  • table: The W&B Table containing the data to visualize.
  • x: The name of the column used for the x-axis.
  • y: The name of the column used for the y-axis.
  • title: The title of the scatter chart.
  • split_table: Whether the table should be split into a separate section in the W&B UI. If True, the table will be displayed in a section named “Custom Chart Tables”. Default is False.

Returns:

  • CustomChart: A custom chart object that can be logged to W&B. To log the chart, pass it to wandb.log().

Example:

import math
import random
import wandb

# Simulate temperature variations at different altitudes over time
data = [
   [i, random.uniform(-10, 20) - 0.005 * i + 5 * math.sin(i / 50)]
   for i in range(300)
]

# Create W&B table with altitude (m) and temperature (°C) columns
table = wandb.Table(data=data, columns=["altitude (m)", "temperature (°C)"])

# Initialize W&B run and log the scatter plot
with wandb.init(project="temperature-altitude-scatter") as run:
   # Create and log the scatter plot
   scatter_plot = wandb.plot.scatter(
        table=table,
        x="altitude (m)",
        y="temperature (°C)",
        title="Altitude vs Temperature",
   )
   run.log({"altitude-temperature-scatter": scatter_plot})

6 - Public API

The W&B Public API provides programmatic access to query, export, and update data stored in W&B. Use this API for post-hoc analysis, data export, and programmatic management of runs, artifacts, and sweeps. While the main SDK handles real-time logging during training, the Public API enables you to retrieve historical data, update metadata, manage artifacts, and perform analysis on completed experiments. The main Api class serves as the entry point to most functionality.

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

Available Components

Component Description
Api Main entry point for the Public API. Query runs, projects, and artifacts across your organization.
Runs Access and manage individual training runs, including history, logs, and metrics.
Artifacts Query and download model artifacts, datasets, and other versioned files.
Sweeps Access hyperparameter sweep data and analyze optimization results.
Projects Manage projects and access project-level metadata and settings.
Reports Programmatically access and manage W&B Reports.
Teams Query team information and manage team-level resources.
Users Access user profiles and user-specific data.
Files Download and manage files associated with runs.
History Access detailed time-series metrics logged during training.
Automations Manage automated workflows and actions.
Integrations Configure and manage third-party integrations.

Common Use Cases

Data Export and Analysis

  • Export run history as DataFrames for analysis in Jupyter notebooks
  • Download metrics for custom visualization or reporting
  • Aggregate results across multiple experiments

Post-Hoc Updates

  • Update run metadata after completion
  • Add tags or notes to completed experiments
  • Modify run configurations or summaries

Artifact Management

  • Query artifacts by version or alias
  • Download model checkpoints programmatically
  • Track artifact lineage and dependencies

Sweep Analysis

  • Access sweep results and best performing runs
  • Export hyperparameter search results
  • Analyze parameter importance

Authentication

The Public API uses the same authentication mechanism as the Python SDK. You can authenticate in several ways:

Use the WANDB_API_KEY environment variable to set your API key:

export WANDB_API_KEY=your_api_key

Pass the API key directly when initializing the Api class:

api = Api(api_key="your_api_key")

Or use wandb.login() to authenticate the current session:

import wandb

wandb.login()
api = Api()

Example Usage

Download an Artifact by name and alias

The following example shows how to retrieve an artifact logged to W&B by its name and alias, and then download its contents.

import wandb

api = wandb.Api()
artifact = api.artifact("entity/project/artifact:alias")
artifact.download()

Download an Artifact from a registry

The following example shows how to retrieve a linked artifact from a W&B Registry

import wandb

REGISTRY = "<registry_name>"
COLLECTION = "<collection_name>"
VERSION = "<version>"

api = wandb.Api()
artifact_name = f"wandb-registry-{REGISTRY}/{COLLECTION}:{VERSION}"

# Fetch the artifact
fetched_artifact = api.artifact(name = artifact_name)

# Download artifact. Returns path to downloaded contents
downloaded_path = fetched_artifact.download()

Query W&B Registry

Use Mongo-like filters to query W&B Registries, Collections, and Artifacts. The following example demonstrates how to filter collections by name using a regular expression.

import wandb

# Initialize wandb API
api = wandb.Api()

# Filter all collections, independent of registry, that 
# contains the string `yolo` in the collection name
collection_filters = {
    "name": {"$regex": "yolo"}
}

# Returns an iterable of all collections that match the filters
collections = api.registries().collections(filter=collection_filters)

For more information on how to query a registry, collection, or artifact, see the Find registry items.

6.1 - Api

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Api

Used for querying the W&B server.

Examples:

import wandb

wandb.Api()

method Api.__init__

__init__(
    overrides: Optional[Dict[str, Any]] = None,
    timeout: Optional[int] = None,
    api_key: Optional[str] = None
) → None

Initialize the API.

Args:

  • overrides: You can set base_url if you are
  • using a W&B server other than https: //api.wandb.ai. You can also set defaults for entity, project, and run.
  • timeout: HTTP timeout in seconds for API requests. If not specified, the default timeout will be used.
  • api_key: API key to use for authentication. If not provided, the API key from the current environment or configuration will be used.

property Api.api_key

Returns W&B API key.

Returns:

  • str | None: The api_key property value.

property Api.client

Returns the client object.

Returns:

  • RetryingClient: The client property value.

property Api.default_entity

Returns the default W&B entity.

Returns:

  • str | None: The default_entity property value.

property Api.user_agent

Returns W&B public user agent.

Returns:

  • str: The user_agent property value.

property Api.viewer

Returns the viewer object.

Raises:

  • ValueError: If viewer data is not able to be fetched from W&B.
  • requests.RequestException: If an error occurs while making the graphql request.

Returns:

  • public.User: The viewer property value.

method Api.artifact

artifact(name: str, type: Optional[str] = None)

Returns a single artifact.

Args:

  • name: The artifact’s name. The name of an artifact resembles a filepath that consists, at a minimum, the name of the project the artifact was logged to, the name of the artifact, and the artifact’s version or alias. Optionally append the entity that logged the artifact as a prefix followed by a forward slash. If no entity is specified in the name, the Run or API setting’s entity is used.
  • type: The type of artifact to fetch.

Returns: An Artifact object.

Raises:

  • ValueError: If the artifact name is not specified.
  • ValueError: If the artifact type is specified but does not match the type of the fetched artifact.

Examples: In the proceeding code snippets “entity”, “project”, “artifact”, “version”, and “alias” are placeholders for your W&B entity, name of the project the artifact is in, the name of the artifact, and artifact’s version, respectively.

import wandb

# Specify the project, artifact's name, and the artifact's alias
wandb.Api().artifact(name="project/artifact:alias")

# Specify the project, artifact's name, and a specific artifact version
wandb.Api().artifact(name="project/artifact:version")

# Specify the entity, project, artifact's name, and the artifact's alias
wandb.Api().artifact(name="entity/project/artifact:alias")

# Specify the entity, project, artifact's name, and a specific artifact version
wandb.Api().artifact(name="entity/project/artifact:version")

Note:

This method is intended for external use only. Do not call api.artifact() within the wandb repository code.

method Api.artifact_collection

artifact_collection(type_name: str, name: str) → public.ArtifactCollection

Returns a single artifact collection by type.

You can use the returned ArtifactCollection object to retrieve information about specific artifacts in that collection, and more.

Args:

  • type_name: The type of artifact collection to fetch.
  • name: An artifact collection name. Optionally append the entity that logged the artifact as a prefix followed by a forward slash.

Returns: An ArtifactCollection object.

Examples: In the proceeding code snippet “type”, “entity”, “project”, and “artifact_name” are placeholders for the collection type, your W&B entity, name of the project the artifact is in, and the name of the artifact, respectively.

import wandb

collections = wandb.Api().artifact_collection(
    type_name="type", name="entity/project/artifact_name"
)

# Get the first artifact in the collection
artifact_example = collections.artifacts()[0]

# Download the contents of the artifact to the specified root directory.
artifact_example.download()

method Api.artifact_collection_exists

artifact_collection_exists(name: str, type: str) → bool

Whether an artifact collection exists within a specified project and entity.

Args:

  • name: An artifact collection name. Optionally append the entity that logged the artifact as a prefix followed by a forward slash. If entity or project is not specified, infer the collection from the override params if they exist. Otherwise, entity is pulled from the user settings and project will default to “uncategorized”.
  • type: The type of artifact collection.

Returns: True if the artifact collection exists, False otherwise.

Examples: In the proceeding code snippet “type”, and “collection_name” refer to the type of the artifact collection and the name of the collection, respectively.

import wandb

wandb.Api.artifact_collection_exists(type="type", name="collection_name")

method Api.artifact_collections

artifact_collections(
    project_name: str,
    type_name: str,
    per_page: int = 50
) → public.ArtifactCollections

Returns a collection of matching artifact collections.

Args:

  • project_name: The name of the project to filter on.
  • type_name: The name of the artifact type to filter on.
  • per_page: Sets the page size for query pagination. None will use the default size. Usually there is no reason to change this.

Returns: An iterable ArtifactCollections object.

method Api.artifact_exists

artifact_exists(name: str, type: Optional[str] = None) → bool

Whether an artifact version exists within the specified project and entity.

Args:

  • name: The name of artifact. Add the artifact’s entity and project as a prefix. Append the version or the alias of the artifact with a colon. If the entity or project is not specified, W&B uses override parameters if populated. Otherwise, the entity is pulled from the user settings and the project is set to “Uncategorized”.
  • type: The type of artifact.

Returns: True if the artifact version exists, False otherwise.

Examples: In the proceeding code snippets “entity”, “project”, “artifact”, “version”, and “alias” are placeholders for your W&B entity, name of the project the artifact is in, the name of the artifact, and artifact’s version, respectively.

import wandb

wandb.Api().artifact_exists("entity/project/artifact:version")
wandb.Api().artifact_exists("entity/project/artifact:alias")

method Api.artifact_type

artifact_type(
    type_name: str,
    project: Optional[str] = None
) → public.ArtifactType

Returns the matching ArtifactType.

Args:

  • type_name: The name of the artifact type to retrieve.
  • project: If given, a project name or path to filter on.

Returns: An ArtifactType object.

method Api.artifact_types

artifact_types(project: Optional[str] = None) → public.ArtifactTypes

Returns a collection of matching artifact types.

Args:

  • project: The project name or path to filter on.

Returns: An iterable ArtifactTypes object.

method Api.artifact_versions

artifact_versions(type_name, name, per_page=50)

Deprecated. Use Api.artifacts(type_name, name) method instead.

method Api.artifacts

artifacts(
    type_name: str,
    name: str,
    per_page: int = 50,
    tags: Optional[List[str]] = None
) → public.Artifacts

Return an Artifacts collection.

Args: type_name: The type of artifacts to fetch. name: The artifact’s collection name. Optionally append the entity that logged the artifact as a prefix followed by a forward slash. per_page: Sets the page size for query pagination. If set to None, use the default size. Usually there is no reason to change this. tags: Only return artifacts with all of these tags.

Returns: An iterable Artifacts object.

Examples: In the proceeding code snippet, “type”, “entity”, “project”, and “artifact_name” are placeholders for the artifact type, W&B entity, name of the project the artifact was logged to, and the name of the artifact, respectively.

import wandb

wandb.Api().artifacts(type_name="type", name="entity/project/artifact_name")

method Api.automation

automation(name: str, entity: Optional[str] = None) → Automation

Returns the only Automation matching the parameters.

Args:

  • name: The name of the automation to fetch.
  • entity: The entity to fetch the automation for.

Raises:

  • ValueError: If zero or multiple Automations match the search criteria.

Examples: Get an existing automation named “my-automation”:

import wandb

api = wandb.Api()
automation = api.automation(name="my-automation")

Get an existing automation named “other-automation”, from the entity “my-team”:

automation = api.automation(name="other-automation", entity="my-team")

method Api.automations

automations(
    entity: Optional[str] = None,
    name: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('Automation')]

Returns an iterator over all Automations that match the given parameters.

If no parameters are provided, the returned iterator will contain all Automations that the user has access to.

Args:

  • entity: The entity to fetch the automations for.
  • name: The name of the automation to fetch.
  • per_page: The number of automations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Returns: A list of automations.

Examples: Fetch all existing automations for the entity “my-team”:

import wandb

api = wandb.Api()
automations = api.automations(entity="my-team")

method Api.create_automation

create_automation(
    obj: 'NewAutomation',
    fetch_existing: bool = False,
    **kwargs: typing_extensions.Unpack[ForwardRef('WriteAutomationsKwargs')]
) → Automation

Create a new Automation.

Args: obj: The automation to create. fetch_existing: If True, and a conflicting automation already exists, attempt to fetch the existing automation instead of raising an error. **kwargs: Any additional values to assign to the automation before creating it. If given, these will override any values that may already be set on the automation: - name: The name of the automation. - description: The description of the automation. - enabled: Whether the automation is enabled. - scope: The scope of the automation. - event: The event that triggers the automation. - action: The action that is triggered by the automation.

Returns: The saved Automation.

Examples: Create a new automation named “my-automation” that sends a Slack notification when a run within a specific project logs a metric exceeding a custom threshold:

import wandb
from wandb.automations import OnRunMetric, RunEvent, SendNotification

api = wandb.Api()

project = api.project("my-project", entity="my-team")

# Use the first Slack integration for the team
slack_hook = next(api.slack_integrations(entity="my-team"))

event = OnRunMetric(
     scope=project,
     filter=RunEvent.metric("custom-metric") > 10,
)
action = SendNotification.from_integration(slack_hook)

automation = api.create_automation(
     event >> action,
     name="my-automation",
     description="Send a Slack message whenever 'custom-metric' exceeds 10.",
)

method Api.create_custom_chart

create_custom_chart(
    entity: str,
    name: str,
    display_name: str,
    spec_type: Literal['vega2'],
    access: Literal['private', 'public'],
    spec: Union[str, dict]
) → str

Create a custom chart preset and return its id.

Args:

  • entity: The entity (user or team) that owns the chart
  • name: Unique identifier for the chart preset
  • display_name: Human-readable name shown in the UI
  • spec_type: Type of specification. Must be “vega2” for Vega-Lite v2 specifications.
  • access: Access level for the chart: - “private”: Chart is only accessible to the entity that created it - “public”: Chart is publicly accessible
  • spec: The Vega/Vega-Lite specification as a dictionary or JSON string

Returns: The ID of the created chart preset in the format “entity/name”

Raises:

  • wandb.Error: If chart creation fails
  • UnsupportedError: If the server doesn’t support custom charts

Example:

   import wandb

   api = wandb.Api()

   # Define a simple bar chart specification
   vega_spec = {
        "$schema": "https://vega.github.io/schema/vega-lite/v6.json",
        "mark": "bar",
        "data": {"name": "wandb"},
        "encoding": {
            "x": {"field": "${field:x}", "type": "ordinal"},
            "y": {"field": "${field:y}", "type": "quantitative"},
        },
   }

   # Create the custom chart
   chart_id = api.create_custom_chart(
        entity="my-team",
        name="my-bar-chart",
        display_name="My Custom Bar Chart",
        spec_type="vega2",
        access="private",
        spec=vega_spec,
   )

   # Use with wandb.plot_table()
   chart = wandb.plot_table(
        vega_spec_name=chart_id,
        data_table=my_table,
        fields={"x": "category", "y": "value"},
   )
   ``` 

---

### <kbd>method</kbd> `Api.create_project`

```python
create_project(name: str, entity: str) → None

Create a new project.

Args:

  • name: The name of the new project.
  • entity: The entity of the new project.

method Api.create_registry

create_registry(
    name: str,
    visibility: Literal['organization', 'restricted'],
    organization: Optional[str] = None,
    description: Optional[str] = None,
    artifact_types: Optional[List[str]] = None
) → Registry

Create a new registry.

Args:

  • name: The name of the registry. Name must be unique within the organization.
  • visibility: The visibility of the registry.
  • organization: Anyone in the organization can view this registry. You can edit their roles later from the settings in the UI.
  • restricted: Only invited members via the UI can access this registry. Public sharing is disabled.
  • organization: The organization of the registry. If no organization is set in the settings, the organization will be fetched from the entity if the entity only belongs to one organization.
  • description: The description of the registry.
  • artifact_types: The accepted artifact types of the registry. A type is no
  • more than 128 characters and do not include characters /or ``:. If not specified, all types are accepted. Allowed types added to the registry cannot be removed later.

Returns: A registry object.

Examples:

import wandb

api = wandb.Api()
registry = api.create_registry(
   name="my-registry",
   visibility="restricted",
   organization="my-org",
   description="This is a test registry",
   artifact_types=["model"],
)

method Api.create_run

create_run(
    run_id: Optional[str] = None,
    project: Optional[str] = None,
    entity: Optional[str] = None
) → public.Run

Create a new run.

Args:

  • run_id: The ID to assign to the run. If not specified, W&B creates a random ID.
  • project: The project where to log the run to. If no project is specified, log the run to a project called “Uncategorized”.
  • entity: The entity that owns the project. If no entity is specified, log the run to the default entity.

Returns: The newly created Run.

method Api.create_run_queue

create_run_queue(
    name: str,
    type: 'public.RunQueueResourceType',
    entity: Optional[str] = None,
    prioritization_mode: Optional[ForwardRef('public.RunQueuePrioritizationMode')] = None,
    config: Optional[dict] = None,
    template_variables: Optional[dict] = None
) → public.RunQueue

Create a new run queue in W&B Launch.

Args:

  • name: Name of the queue to create
  • type: Type of resource to be used for the queue. One of “local-container”, “local-process”, “kubernetes”,“sagemaker”, or “gcp-vertex”.
  • entity: Name of the entity to create the queue. If None, use the configured or default entity.
  • prioritization_mode: Version of prioritization to use. Either “V0” or None.
  • config: Default resource configuration to be used for the queue. Use handlebars (eg. {{var}}) to specify template variables.
  • template_variables: A dictionary of template variable schemas to use with the config.

Returns: The newly created RunQueue.

Raises: ValueError if any of the parameters are invalid wandb.Error on wandb API errors

method Api.create_team

create_team(team: str, admin_username: Optional[str] = None) → public.Team

Create a new team.

Args:

  • team: The name of the team
  • admin_username: Username of the admin user of the team. Defaults to the current user.

Returns: A Team object.

method Api.create_user

create_user(email: str, admin: Optional[bool] = False)

Create a new user.

Args:

  • email: The email address of the user.
  • admin: Set user as a global instance administrator.

Returns: A User object.

method Api.delete_automation

delete_automation(obj: Union[ForwardRef('Automation'), str]) → Literal[True]

Delete an automation.

Args:

  • obj: The automation to delete, or its ID.

Returns: True if the automation was deleted successfully.

method Api.flush

flush()

Flush the local cache.

The api object keeps a local cache of runs, so if the state of the run may change while executing your script you must clear the local cache with api.flush() to get the latest values associated with the run.

method Api.from_path

from_path(path: str)

Return a run, sweep, project or report from a path.

Args:

  • path: The path to the project, run, sweep or report

Returns: A Project, Run, Sweep, or BetaReport instance.

Raises: wandb.Error if path is invalid or the object doesn’t exist.

Examples: In the proceeding code snippets “project”, “team”, “run_id”, “sweep_id”, and “report_name” are placeholders for the project, team, run ID, sweep ID, and the name of a specific report, respectively.

import wandb

api = wandb.Api()

project = api.from_path("project")
team_project = api.from_path("team/project")
run = api.from_path("team/project/runs/run_id")
sweep = api.from_path("team/project/sweeps/sweep_id")
report = api.from_path("team/project/reports/report_name")

method Api.integrations

integrations(
    entity: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('Integration')]

Return an iterator of all integrations for an entity.

Args:

  • entity: The entity (e.g. team name) for which to fetch integrations. If not provided, the user’s default entity will be used.
  • per_page: Number of integrations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Yields:

  • Iterator[SlackIntegration | WebhookIntegration]: An iterator of any supported integrations.

method Api.job

job(name: Optional[str], path: Optional[str] = None) → public.Job

Return a Job object.

Args:

  • name: The name of the job.
  • path: The root path to download the job artifact.

Returns: A Job object.

method Api.list_jobs

list_jobs(entity: str, project: str) → List[Dict[str, Any]]

Return a list of jobs, if any, for the given entity and project.

Args:

  • entity: The entity for the listed jobs.
  • project: The project for the listed jobs.

Returns: A list of matching jobs.

method Api.project

project(name: str, entity: Optional[str] = None) → public.Project

Return the Project with the given name (and entity, if given).

Args:

  • name: The project name.
  • entity: Name of the entity requested. If None, will fall back to the default entity passed to Api. If no default entity, will raise a ValueError.

Returns: A Project object.

method Api.projects

projects(entity: Optional[str] = None, per_page: int = 200) → public.Projects

Get projects for a given entity.

Args:

  • entity: Name of the entity requested. If None, will fall back to the default entity passed to Api. If no default entity, will raise a ValueError.
  • per_page: Sets the page size for query pagination. If set to None, use the default size. Usually there is no reason to change this.

Returns: A Projects object which is an iterable collection of Projectobjects.

method Api.queued_run

queued_run(
    entity: str,
    project: str,
    queue_name: str,
    run_queue_item_id: str,
    project_queue=None,
    priority=None
)

Return a single queued run based on the path.

Parses paths of the form entity/project/queue_id/run_queue_item_id.

method Api.registries

registries(
    organization: Optional[str] = None,
    filter: Optional[Dict[str, Any]] = None
) → Registries

Returns a lazy iterator of Registry objects.

Use the iterator to search and filter registries, collections, or artifact versions across your organization’s registry.

Args:

  • organization: (str, optional) The organization of the registry to fetch. If not specified, use the organization specified in the user’s settings.
  • filter: (dict, optional) MongoDB-style filter to apply to each object in the lazy registry iterator. Fields available to filter for registries are name, description, created_at, updated_at. Fields available to filter for collections are name, tag, description, created_at, updated_at Fields available to filter for versions are tag, alias, created_at, updated_at, metadata

Returns: A lazy iterator of Registry objects.

Examples: Find all registries with the names that contain “model”

import wandb

api = wandb.Api()  # specify an org if your entity belongs to multiple orgs
api.registries(filter={"name": {"$regex": "model"}})

Find all collections in the registries with the name “my_collection” and the tag “my_tag”

api.registries().collections(filter={"name": "my_collection", "tag": "my_tag"})

Find all artifact versions in the registries with a collection name that contains “my_collection” and a version that has the alias “best”

api.registries().collections(
    filter={"name": {"$regex": "my_collection"}}
).versions(filter={"alias": "best"})

Find all artifact versions in the registries that contain “model” and have the tag “prod” or alias “best”

api.registries(filter={"name": {"$regex": "model"}}).versions(
    filter={"$or": [{"tag": "prod"}, {"alias": "best"}]}
)

method Api.registry

registry(name: str, organization: Optional[str] = None) → Registry

Return a registry given a registry name.

Args:

  • name: The name of the registry. This is without the wandb-registry- prefix.
  • organization: The organization of the registry. If no organization is set in the settings, the organization will be fetched from the entity if the entity only belongs to one organization.

Returns: A registry object.

Examples: Fetch and update a registry

import wandb

api = wandb.Api()
registry = api.registry(name="my-registry", organization="my-org")
registry.description = "This is an updated description"
registry.save()

method Api.reports

reports(
    path: str = '',
    name: Optional[str] = None,
    per_page: int = 50
) → public.Reports

Get reports for a given project path.

Note: wandb.Api.reports() API is in beta and will likely change in future releases.

Args:

  • path: The path to the project the report resides in. Specify the entity that created the project as a prefix followed by a forward slash.
  • name: Name of the report requested.
  • per_page: Sets the page size for query pagination. If set to None, use the default size. Usually there is no reason to change this.

Returns: A Reports object which is an iterable collection of BetaReport objects.

Examples:

import wandb

wandb.Api.reports("entity/project")

method Api.run

run(path='')

Return a single run by parsing path in the form entity/project/run_id.

Args:

  • path: Path to run in the form entity/project/run_id. If api.entity is set, this can be in the form project/run_id and if api.project is set this can just be the run_id.

Returns: A Run object.

method Api.run_queue

run_queue(entity: str, name: str)

Return the named RunQueue for entity.

See Api.create_run_queue for more information on how to create a run queue.

method Api.runs

runs(
    path: Optional[str] = None,
    filters: Optional[Dict[str, Any]] = None,
    order: str = '+created_at',
    per_page: int = 50,
    include_sweeps: bool = True
)

Returns a Runs object, which lazily iterates over Run objects.

Fields you can filter by include:

  • createdAt: The timestamp when the run was created. (in ISO 8601 format, e.g. “2023-01-01T12:00:00Z”)
  • displayName: The human-readable display name of the run. (e.g. “eager-fox-1”)
  • duration: The total runtime of the run in seconds.
  • group: The group name used to organize related runs together.
  • host: The hostname where the run was executed.
  • jobType: The type of job or purpose of the run.
  • name: The unique identifier of the run. (e.g. “a1b2cdef”)
  • state: The current state of the run.
  • tags: The tags associated with the run.
  • username: The username of the user who initiated the run

Additionally, you can filter by items in the run config or summary metrics. Such as config.experiment_name, summary_metrics.loss, etc.

For more complex filtering, you can use MongoDB query operators. For details, see: https://docs.mongodb.com/manual/reference/operator/query The following operations are supported:

  • $and
  • $or
  • $nor
  • $eq
  • $ne
  • $gt
  • $gte
  • $lt
  • $lte
  • $in
  • $nin
  • $exists
  • $regex

Args:

  • path: (str) path to project, should be in the form: “entity/project”
  • filters: (dict) queries for specific runs using the MongoDB query language. You can filter by run properties such as config.key, summary_metrics.key, state, entity, createdAt, etc.
  • For example: {"config.experiment_name": "foo"} would find runs with a config entry of experiment name set to “foo”
  • order: (str) Order can be created_at, heartbeat_at, config.*.value, or summary_metrics.*. If you prepend order with a + order is ascending (default). If you prepend order with a - order is descending. The default order is run.created_at from oldest to newest.
  • per_page: (int) Sets the page size for query pagination.
  • include_sweeps: (bool) Whether to include the sweep runs in the results.

Returns: A Runs object, which is an iterable collection of Run objects.

Examples:

# Find runs in project where config.experiment_name has been set to "foo"
api.runs(path="my_entity/project", filters={"config.experiment_name": "foo"})

# Find runs in project where config.experiment_name has been set to "foo" or "bar"
api.runs(
    path="my_entity/project",
    filters={
         "$or": [
             {"config.experiment_name": "foo"},
             {"config.experiment_name": "bar"},
         ]
    },
)

# Find runs in project where config.experiment_name matches a regex
# (anchors are not supported)
api.runs(
    path="my_entity/project",
    filters={"config.experiment_name": {"$regex": "b.*"}},
)

# Find runs in project where the run name matches a regex
# (anchors are not supported)
api.runs(
    path="my_entity/project", filters={"display_name": {"$regex": "^foo.*"}}
)

# Find runs in project sorted by ascending loss
api.runs(path="my_entity/project", order="+summary_metrics.loss")

method Api.slack_integrations

slack_integrations(
    entity: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('SlackIntegration')]

Returns an iterator of Slack integrations for an entity.

Args:

  • entity: The entity (e.g. team name) for which to fetch integrations. If not provided, the user’s default entity will be used.
  • per_page: Number of integrations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Yields:

  • Iterator[SlackIntegration]: An iterator of Slack integrations.

Examples: Get all registered Slack integrations for the team “my-team”:

import wandb

api = wandb.Api()
slack_integrations = api.slack_integrations(entity="my-team")

Find only Slack integrations that post to channel names starting with “team-alerts-”:

slack_integrations = api.slack_integrations(entity="my-team")
team_alert_integrations = [
    ig
    for ig in slack_integrations
    if ig.channel_name.startswith("team-alerts-")
]

method Api.sweep

sweep(path='')

Return a sweep by parsing path in the form entity/project/sweep_id.

Args:

  • path: Path to sweep in the form entity/project/sweep_id. If api.entity is set, this can be in the form project/sweep_id and if api.project is set this can just be the sweep_id.

Returns: A Sweep object.

method Api.sync_tensorboard

sync_tensorboard(root_dir, run_id=None, project=None, entity=None)

Sync a local directory containing tfevent files to wandb.

method Api.team

team(team: str) → public.Team

Return the matching Team with the given name.

Args:

  • team: The name of the team.

Returns: A Team object.

method Api.update_automation

update_automation(
    obj: 'Automation',
    create_missing: bool = False,
    **kwargs: typing_extensions.Unpack[ForwardRef('WriteAutomationsKwargs')]
) → Automation

Update an existing automation.

Args:

  • obj: The automation to update. Must be an existing automation. create_missing (bool): If True, and the automation does not exist, create it. **kwargs: Any additional values to assign to the automation before updating it. If given, these will override any values that may already be set on the automation: - name: The name of the automation. - description: The description of the automation. - enabled: Whether the automation is enabled. - scope: The scope of the automation. - event: The event that triggers the automation. - action: The action that is triggered by the automation.

Returns: The updated automation.

Examples: Disable and edit the description of an existing automation (“my-automation”):

import wandb

api = wandb.Api()

automation = api.automation(name="my-automation")
automation.enabled = False
automation.description = "Kept for reference, but no longer used."

updated_automation = api.update_automation(automation)

OR

import wandb

api = wandb.Api()

automation = api.automation(name="my-automation")

updated_automation = api.update_automation(
    automation,
    enabled=False,
    description="Kept for reference, but no longer used.",
)

method Api.upsert_run_queue

upsert_run_queue(
    name: str,
    resource_config: dict,
    resource_type: 'public.RunQueueResourceType',
    entity: Optional[str] = None,
    template_variables: Optional[dict] = None,
    external_links: Optional[dict] = None,
    prioritization_mode: Optional[ForwardRef('public.RunQueuePrioritizationMode')] = None
)

Upsert a run queue in W&B Launch.

Args:

  • name: Name of the queue to create
  • entity: Optional name of the entity to create the queue. If None, use the configured or default entity.
  • resource_config: Optional default resource configuration to be used for the queue. Use handlebars (eg. {{var}}) to specify template variables.
  • resource_type: Type of resource to be used for the queue. One of “local-container”, “local-process”, “kubernetes”, “sagemaker”, or “gcp-vertex”.
  • template_variables: A dictionary of template variable schemas to be used with the config.
  • external_links: Optional dictionary of external links to be used with the queue.
  • prioritization_mode: Optional version of prioritization to use. Either “V0” or None

Returns: The upserted RunQueue.

Raises: ValueError if any of the parameters are invalid wandb.Error on wandb API errors

method Api.user

user(username_or_email: str) → Optional[ForwardRef('public.User')]

Return a user from a username or email address.

This function only works for local administrators. Use api.viewer to get your own user object.

Args:

  • username_or_email: The username or email address of the user.

Returns: A User object or None if a user is not found.

method Api.users

users(username_or_email: str) → List[ForwardRef('public.User')]

Return all users from a partial username or email address query.

This function only works for local administrators. Use api.viewer to get your own user object.

Args:

  • username_or_email: The prefix or suffix of the user you want to find.

Returns: An array of User objects.

method Api.webhook_integrations

webhook_integrations(
    entity: Optional[str] = None,
    per_page: int = 50
) → Iterator[ForwardRef('WebhookIntegration')]

Returns an iterator of webhook integrations for an entity.

Args:

  • entity: The entity (e.g. team name) for which to fetch integrations. If not provided, the user’s default entity will be used.
  • per_page: Number of integrations to fetch per page. Defaults to 50. Usually there is no reason to change this.

Yields:

  • Iterator[WebhookIntegration]: An iterator of webhook integrations.

Examples: Get all registered webhook integrations for the team “my-team”:

import wandb

api = wandb.Api()
webhook_integrations = api.webhook_integrations(entity="my-team")

Find only webhook integrations that post requests to “https://my-fake-url.com”:

webhook_integrations = api.webhook_integrations(entity="my-team")
my_webhooks = [
    ig
    for ig in webhook_integrations
    if ig.url_endpoint.startswith("https://my-fake-url.com")
]

6.2 - ArtifactCollection

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class ArtifactCollection

An artifact collection that represents a group of related artifacts.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifact collections.
  • name: The name of the artifact collection.
  • type: The type of the artifact collection (e.g., “dataset”, “model”).
  • organization: Optional organization name if applicable.
  • attrs: Optional mapping of attributes to initialize the artifact collection. If not provided, the object will load its attributes from W&B upon initialization.

property ArtifactCollection.aliases

Artifact Collection Aliases.

Returns:

  • list[str]: The aliases property value.

property ArtifactCollection.created_at

The creation date of the artifact collection.

Returns:

  • str: The created_at property value.

property ArtifactCollection.description

A description of the artifact collection.

Returns:

  • str: The description property value.

property ArtifactCollection.id

The unique identifier of the artifact collection.

Returns:

  • str: The id property value.

property ArtifactCollection.name

The name of the artifact collection.

Returns:

  • str: The name property value.

property ArtifactCollection.tags

The tags associated with the artifact collection.

Returns:

  • list[str]: The tags property value.

property ArtifactCollection.type

Returns the type of the artifact collection.

method ArtifactCollection.artifacts

artifacts(per_page: 'int' = 50) → Artifacts

Get all artifacts in the collection.

method ArtifactCollection.change_type

change_type(new_type: 'str') → None

Deprecated, change type directly with save instead.

method ArtifactCollection.delete

delete() → None

Delete the entire artifact collection.

method ArtifactCollection.is_sequence

is_sequence() → bool

Return whether the artifact collection is a sequence.

method ArtifactCollection.save

save() → None

Persist any changes made to the artifact collection.

6.3 - ArtifactCollections

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class ArtifactCollections

Artifact collections of a specific type in a project.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifact collections.
  • type_name: The name of the artifact type for which to fetch collections.
  • per_page: The number of artifact collections to fetch per page. Default is 50.

property ArtifactCollections.length

6.4 - ArtifactFiles

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class ArtifactFiles

A paginator for files in an artifact.

property ArtifactFiles.length

property ArtifactFiles.path

Returns the path of the artifact.

Returns:

  • list[str]: The path property value.

6.5 - Artifacts

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Artifacts

An iterable collection of artifact versions associated with a project.

Optionally pass in filters to narrow down the results based on specific criteria.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifacts.
  • collection_name: The name of the artifact collection to query.
  • type: The type of the artifacts to query. Common examples include “dataset” or “model”.
  • filters: Optional mapping of filters to apply to the query.
  • order: Optional string to specify the order of the results.
  • per_page: The number of artifact versions to fetch per page. Default is 50.
  • tags: Optional string or list of strings to filter artifacts by tags.

property Artifacts.length

6.6 - ArtifactType

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class ArtifactType

An artifact object that satisfies query based on the specified type.

Args:

  • client: The client instance to use for querying W&B.
  • entity: The entity (user or team) that owns the project.
  • project: The name of the project to query for artifact types.
  • type_name: The name of the artifact type.
  • attrs: Optional mapping of attributes to initialize the artifact type. If not provided, the object will load its attributes from W&B upon initialization.

property ArtifactType.id

The unique identifier of the artifact type.

Returns:

  • str: The id property value.

property ArtifactType.name

The name of the artifact type.

Returns:

  • str: The name property value.

method ArtifactType.collection

collection(name: 'str') → ArtifactCollection

Get a specific artifact collection by name.

Args:

  • name (str): The name of the artifact collection to retrieve.

method ArtifactType.collections

collections(per_page: 'int' = 50) → ArtifactCollections

Get all artifact collections associated with this artifact type.

Args:

  • per_page (int): The number of artifact collections to fetch per page. Default is 50.

6.7 - ArtifactTypes

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class ArtifactTypes

An lazy iterator of ArtifactType objects for a specific project.

6.8 - Automations

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Automations

An lazy iterator of Automation objects.

6.9 - BetaReport

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class BetaReport

BetaReport is a class associated with reports created in W&B.

Provides access to report attributes (name, description, user, spec, timestamps) and methods for retrieving associated runs, sections, and for rendering the report as HTML.

Attributes:

  • id (string): Unique identifier of the report.
  • display_name (string): Human-readable display name of the report.
  • name (string): The name of the report. Use display_name for a more user-friendly name.
  • description (string): Description of the report.
  • user (User): Dictionary containing user info (username, email) who created the report.
  • spec (dict): The spec of the report.
  • url (string): The URL of the report.
  • updated_at (string): Timestamp of last update.
  • created_at (string): Timestamp when the report was created.

method BetaReport.__init__

__init__(client, attrs, entity=None, project=None)

property BetaReport.created_at

property BetaReport.description

property BetaReport.display_name

property BetaReport.id

property BetaReport.name

property BetaReport.sections

Get the panel sections (groups) from the report.

property BetaReport.spec

property BetaReport.updated_at

property BetaReport.url

property BetaReport.user

method BetaReport.runs

runs(section, per_page=50, only_selected=True)

Get runs associated with a section of the report.

method BetaReport.to_html

to_html(height=1024, hidden=False)

Generate HTML containing an iframe displaying this report.

6.10 - Files

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Files

A lazy iterator over a collection of File objects.

Access and manage files uploaded to W&B during a run. Handles pagination automatically when iterating through large collections of files.

Example:

from wandb.apis.public.files import Files
from wandb.apis.public.api import Api

# Example run object
run = Api().run("entity/project/run-id")

# Create a Files object to iterate over files in the run
files = Files(api.client, run)

# Iterate over files
for file in files:
    print(file.name)
    print(file.url)
    print(file.size)

    # Download the file
    file.download(root="download_directory", replace=True)

method Files.__init__

__init__(
    client: 'RetryingClient',
    run: 'Run',
    names: 'list[str] | None' = None,
    per_page: 'int' = 50,
    upload: 'bool' = False,
    pattern: 'str | None' = None
)

Initialize a lazy iterator over a collection of File objects.

Files are retrieved in pages from the W&B server as needed.

Args: client: The run object that contains the files run: The run object that contains the files names (list, optional): A list of file names to filter the files per_page (int, optional): The number of files to fetch per page upload (bool, optional): If True, fetch the upload URL for each file pattern (str, optional): Pattern to match when returning files from W&B This pattern uses mySQL’s LIKE syntax, so matching all files that end with .json would be “%.json”. If both names and pattern are provided, a ValueError will be raised.

property Files.length

6.11 - Member

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Member

A member of a team.

method Member.__init__

__init__(client, team, attrs)

Args:

  • client (wandb.apis.internal.Api): The client instance to use
  • team (str): The name of the team this member belongs to
  • attrs (dict): The member attributes

method Member.delete

delete()

Remove a member from a team.

Returns: Boolean indicating success

6.12 - Project

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Project

A project is a namespace for runs.

method Project.__init__

__init__(
    client: wandb.apis.public.api.RetryingClient,
    entity: str,
    project: str,
    attrs: dict
) → Project

Args:

  • client: W&B API client instance.
  • name (str): The name of the project.
  • entity (str): The entity name that owns the project.

A single project associated with an entity.

Args:

  • client: The API client used to query W&B.
  • entity: The entity which owns the project.
  • project: The name of the project to query.
  • attrs: The attributes of the project.

property Project.id

property Project.path

Returns the path of the project. The path is a list containing the entity and project name.

property Project.url

Returns the URL of the project.

method Project.artifacts_types

artifacts_types(per_page=50)

Returns all artifact types associated with this project.

method Project.sweeps

sweeps(per_page=50)

Return a paginated collection of sweeps in this project.

Args:

  • per_page: The number of sweeps to fetch per request to the API.

Returns: A Sweeps object, which is an iterable collection of Sweep objects.

6.13 - Projects

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Projects

An lazy iterator of Project objects.

An iterable interface to access projects created and saved by the entity.

method Projects.__init__

__init__(
    client: wandb.apis.public.api.RetryingClient,
    entity: str,
    per_page: int = 50
) → Projects

Args:

  • client (wandb.apis.internal.Api): The API client instance to use.
  • entity (str): The entity name (username or team) to fetch projects for.
  • per_page (int): Number of projects to fetch per request (default is 50).

Example:

from wandb.apis.public.api import Api

# Find projects that belong to this entity
projects = Api().projects(entity="entity")

# Iterate over files
for project in projects:
   print(f"Project: {project.name}")
   print(f"- URL: {project.url}")
   print(f"- Created at: {project.created_at}")
   print(f"- Is benchmark: {project.is_benchmark}")

An iterable collection of Project objects.

Args:

  • client: The API client used to query W&B.
  • entity: The entity which owns the projects.
  • per_page: The number of projects to fetch per request to the API.

6.14 - Registry

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Registry

A single registry in the Registry.

method Registry.__init__

__init__(
    client: 'Client',
    organization: str,
    entity: str,
    name: str,
    attrs: Optional[Dict[str, Any]] = None
)

property Registry.allow_all_artifact_types

Returns whether all artifact types are allowed in the registry.

If True then artifacts of any type can be added to this registry. If False then artifacts are restricted to the types in artifact_types for this registry.

property Registry.artifact_types

Returns the artifact types allowed in the registry.

If allow_all_artifact_types is True then artifact_types reflects the types previously saved or currently used in the registry. If allow_all_artifact_types is False then artifacts are restricted to the types in artifact_types.

Note:

Previously saved artifact types cannot be removed.

Example:

    registry.artifact_types.append("model")
    registry.save()  # once saved, the artifact type `model` cannot be removed
    registry.artifact_types.append("accidentally_added")
    registry.artifact_types.remove(
        "accidentally_added"
    )  # Types can only be removed if it has not been saved yet
   ``` 



**Returns:**
- `AddOnlyArtifactTypesList`: The artifact_types property value.
---

### <kbd>property</kbd> Registry.created_at

Timestamp of when the registry was created. 



**Returns:**
- `str`: The created_at property value.
---

### <kbd>property</kbd> Registry.description

Description of the registry. 



**Returns:**
- `str`: The description property value.
---

### <kbd>property</kbd> Registry.entity

Organization entity of the registry. 



**Returns:**
- `str`: The entity property value.
---

### <kbd>property</kbd> Registry.full_name

Full name of the registry including the `wandb-registry-` prefix. 



**Returns:**
- `str`: The full_name property value.
---

### <kbd>property</kbd> Registry.name

Name of the registry without the `wandb-registry-` prefix. 



**Returns:**
- `str`: The name property value.
---

### <kbd>property</kbd> Registry.organization

Organization name of the registry. 



**Returns:**
- `str`: The organization property value.
---

### <kbd>property</kbd> Registry.path





---

### <kbd>property</kbd> Registry.updated_at

Timestamp of when the registry was last updated. 



**Returns:**
- `str`: The updated_at property value.
---

### <kbd>property</kbd> Registry.visibility

Visibility of the registry. 



**Returns:**

- `Literal["organization", "restricted"]`:  The visibility level. 
       - "organization": Anyone in the organization can view this registry.  You can edit their roles later from the settings in the UI. 
       - "restricted": Only invited members via the UI can access this registry.  Public sharing is disabled. 





**Returns:**
- `Literal`: The visibility property value.
---

### <kbd>method</kbd> `Registry.collections`

```python
collections(filter: Optional[Dict[str, Any]] = None) → Collections

Returns the collections belonging to the registry.

classmethod Registry.create

create(
    client: 'Client',
    organization: str,
    name: str,
    visibility: Literal['organization', 'restricted'],
    description: Optional[str] = None,
    artifact_types: Optional[List[str]] = None
)

Create a new registry.

The registry name must be unique within the organization. This function should be called using api.create_registry()

Args:

  • client: The GraphQL client.
  • organization: The name of the organization.
  • name: The name of the registry (without the wandb-registry- prefix).
  • visibility: The visibility level (‘organization’ or ‘restricted’).
  • description: An optional description for the registry.
  • artifact_types: An optional list of allowed artifact types.

Returns:

  • Registry: The newly created Registry object.

Raises:

  • ValueError: If a registry with the same name already exists in the organization or if the creation fails.

method Registry.delete

delete() → None

Delete the registry. This is irreversible.

method Registry.load

load() → None

Load the registry attributes from the backend to reflect the latest saved state.

method Registry.save

save() → None

Save registry attributes to the backend.

method Registry.versions

versions(filter: Optional[Dict[str, Any]] = None) → Versions

Returns the versions belonging to the registry.

6.15 - Reports

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Reports

Reports is a lazy iterator of BetaReport objects.

method Reports.__init__

__init__(client, project, name=None, entity=None, per_page=50)

Args:

  • client (wandb.apis.internal.Api): The API client instance to use.
  • project (wandb.sdk.internal.Project): The project to fetch reports from.
  • name (str, optional): The name of the report to filter by. If None, fetches all reports.
  • entity (str, optional): The entity name for the project. Defaults to the project entity.
  • per_page (int): Number of reports to fetch per page (default is 50).

property Reports.length

method Reports.convert_objects

convert_objects()

Converts GraphQL edges to File objects.

method Reports.update_variables

update_variables()

Updates the GraphQL query variables for pagination.

6.16 - Run

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Run

A single run associated with an entity and project.

method Run.__init__

__init__(
    client: 'RetryingClient',
    entity: 'str',
    project: 'str',
    run_id: 'str',
    attrs: 'Mapping | None' = None,
    include_sweeps: 'bool' = True
)

Args:

  • client: The W&B API client.
  • entity: The entity associated with the run.
  • project: The project associated with the run.
  • run_id: The unique identifier for the run.
  • attrs: The attributes of the run.
  • include_sweeps: Whether to include sweeps in the run.

Attributes:

  • tags ([str]): a list of tags associated with the run
  • url (str): the url of this run
  • id (str): unique identifier for the run (defaults to eight characters)
  • name (str): the name of the run
  • state (str): one of: running, finished, crashed, killed, preempting, preempted
  • config (dict): a dict of hyperparameters associated with the run
  • created_at (str): ISO timestamp when the run was started
  • system_metrics (dict): the latest system metrics recorded for the run
  • summary (dict): A mutable dict-like property that holds the current summary. Calling update will persist any changes.
  • project (str): the project associated with the run
  • entity (str): the name of the entity associated with the run
  • project_internal_id (int): the internal id of the project
  • user (str): the name of the user who created the run
  • path (str): Unique identifier [entity]/[project]/[run_id]
  • notes (str): Notes about the run
  • read_only (boolean): Whether the run is editable
  • history_keys (str): Keys of the history metrics that have been logged
  • with wandb.log({key: value})
  • metadata (str): Metadata about the run from wandb-metadata.json

Initialize a Run object.

Run is always initialized by calling api.runs() where api is an instance of wandb.Api.

property Run.entity

The entity associated with the run.

property Run.id

The unique identifier for the run.

property Run.lastHistoryStep

Returns the last step logged in the run’s history.

property Run.metadata

Metadata about the run from wandb-metadata.json.

Metadata includes the run’s description, tags, start time, memory usage and more.

property Run.name

The name of the run.

property Run.path

The path of the run. The path is a list containing the entity, project, and run_id.

property Run.state

The state of the run. Can be one of: Finished, Failed, Crashed, or Running.

property Run.storage_id

The unique storage identifier for the run.

property Run.summary

A mutable dict-like property that holds summary values associated with the run.

property Run.url

The URL of the run.

The run URL is generated from the entity, project, and run_id. For SaaS users, it takes the form of https://wandb.ai/entity/project/run_id.

property Run.username

This API is deprecated. Use entity instead.

classmethod Run.create

create(
    api: 'public.Api',
    run_id: 'str | None' = None,
    project: 'str | None' = None,
    entity: 'str | None' = None,
    state: "Literal['running', 'pending']" = 'running'
)

Create a run for the given project.

method Run.delete

delete(delete_artifacts=False)

Delete the given run from the wandb backend.

Args:

  • delete_artifacts (bool, optional): Whether to delete the artifacts associated with the run.

method Run.file

file(name)

Return the path of a file with a given name in the artifact.

Args:

  • name (str): name of requested file.

Returns: A File matching the name argument.

method Run.files

files(
    names: 'list[str] | None' = None,
    pattern: 'str | None' = None,
    per_page: 'int' = 50
)

Returns a Files object for all files in the run which match the given criteria.

You can specify a list of exact file names to match, or a pattern to match against. If both are provided, the pattern will be ignored.

Args:

  • names (list): names of the requested files, if empty returns all files
  • pattern (str, optional): Pattern to match when returning files from W&B. This pattern uses mySQL’s LIKE syntax, so matching all files that end with .json would be “%.json”. If both names and pattern are provided, a ValueError will be raised.
  • per_page (int): number of results per page.

Returns: A Files object, which is an iterator over File objects.

method Run.history

history(samples=500, keys=None, x_axis='_step', pandas=True, stream='default')

Return sampled history metrics for a run.

This is simpler and faster if you are ok with the history records being sampled.

Args:

  • samples : (int, optional) The number of samples to return
  • pandas : (bool, optional) Return a pandas dataframe
  • keys : (list, optional) Only return metrics for specific keys
  • x_axis : (str, optional) Use this metric as the xAxis defaults to _step
  • stream : (str, optional) “default” for metrics, “system” for machine metrics

Returns:

  • pandas.DataFrame: If pandas=True returns a pandas.DataFrame of history metrics.
  • list of dicts: If pandas=False returns a list of dicts of history metrics.

method Run.load

load(force=False)

method Run.log_artifact

log_artifact(
    artifact: 'wandb.Artifact',
    aliases: 'Collection[str] | None' = None,
    tags: 'Collection[str] | None' = None
)

Declare an artifact as output of a run.

Args:

  • artifact (Artifact): An artifact returned from wandb.Api().artifact(name).
  • aliases (list, optional): Aliases to apply to this artifact.
  • tags: (list, optional) Tags to apply to this artifact, if any.

Returns: A Artifact object.

method Run.logged_artifacts

logged_artifacts(per_page: 'int' = 100) → public.RunArtifacts

Fetches all artifacts logged by this run.

Retrieves all output artifacts that were logged during the run. Returns a paginated result that can be iterated over or collected into a single list.

Args:

  • per_page: Number of artifacts to fetch per API request.

Returns: An iterable collection of all Artifact objects logged as outputs during this run.

Example:

import wandb
import tempfile

with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as tmp:
   tmp.write("This is a test artifact")
   tmp_path = tmp.name
run = wandb.init(project="artifact-example")
artifact = wandb.Artifact("test_artifact", type="dataset")
artifact.add_file(tmp_path)
run.log_artifact(artifact)
run.finish()

api = wandb.Api()

finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")

for logged_artifact in finished_run.logged_artifacts():
   print(logged_artifact.name)

method Run.save

save()

Persist changes to the run object to the W&B backend.

method Run.scan_history

scan_history(keys=None, page_size=1000, min_step=None, max_step=None)

Returns an iterable collection of all history records for a run.

Args:

  • keys ([str], optional): only fetch these keys, and only fetch rows that have all of keys defined.
  • page_size (int, optional): size of pages to fetch from the api.
  • min_step (int, optional): the minimum number of pages to scan at a time.
  • max_step (int, optional): the maximum number of pages to scan at a time.

Returns: An iterable collection over history records (dict).

Example: Export all the loss values for an example run

run = api.run("entity/project-name/run-id")
history = run.scan_history(keys=["Loss"])
losses = [row["Loss"] for row in history]

method Run.to_html

to_html(height=420, hidden=False)

Generate HTML containing an iframe displaying this run.

method Run.update

update()

Persist changes to the run object to the wandb backend.

method Run.upload_file

upload_file(path, root='.')

Upload a local file to W&B, associating it with this run.

Args:

  • path (str): Path to the file to upload. Can be absolute or relative.
  • root (str): The root path to save the file relative to. For example, if you want to have the file saved in the run as “my_dir/file.txt” and you’re currently in “my_dir” you would set root to “../”. Defaults to current directory (".").

Returns: A File object representing the uploaded file.

method Run.use_artifact

use_artifact(artifact, use_as=None)

Declare an artifact as an input to a run.

Args:

  • artifact (Artifact): An artifact returned from wandb.Api().artifact(name)
  • use_as (string, optional): A string identifying how the artifact is used in the script. Used to easily differentiate artifacts used in a run, when using the beta wandb launch feature’s artifact swapping functionality.

Returns: An Artifact object.

method Run.used_artifacts

used_artifacts(per_page: 'int' = 100) → public.RunArtifacts

Fetches artifacts explicitly used by this run.

Retrieves only the input artifacts that were explicitly declared as used during the run, typically via run.use_artifact(). Returns a paginated result that can be iterated over or collected into a single list.

Args:

  • per_page: Number of artifacts to fetch per API request.

Returns: An iterable collection of Artifact objects explicitly used as inputs in this run.

Example:

import wandb

run = wandb.init(project="artifact-example")
run.use_artifact("test_artifact:latest")
run.finish()

api = wandb.Api()
finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")
for used_artifact in finished_run.used_artifacts():
   print(used_artifact.name)
test_artifact

method Run.wait_until_finished

wait_until_finished()

Check the state of the run until it is finished.

6.17 - RunArtifacts

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class RunArtifacts

An iterable collection of artifacts associated with a specific run.

property RunArtifacts.length

6.18 - Runs

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Runs

A lazy iterator of Run objects associated with a project and optional filter.

Runs are retrieved in pages from the W&B server as needed.

This is generally used indirectly using the Api.runs namespace.

method Runs.__init__

__init__(
    client: 'RetryingClient',
    entity: 'str',
    project: 'str',
    filters: 'dict[str, Any] | None' = None,
    order: 'str' = '+created_at',
    per_page: 'int' = 50,
    include_sweeps: 'bool' = True
)

Args:

  • client: (wandb.apis.public.RetryingClient) The API client to use for requests.
  • entity: (str) The entity (username or team) that owns the project.
  • project: (str) The name of the project to fetch runs from.
  • filters: (Optional[Dict[str, Any]]) A dictionary of filters to apply to the runs query.
  • order: (str) Order can be created_at, heartbeat_at, config.*.value, or summary_metrics.*. If you prepend order with a + order is ascending (default). If you prepend order with a - order is descending. The default order is run.created_at from oldest to newest.
  • per_page: (int) The number of runs to fetch per request (default is 50).
  • include_sweeps: (bool) Whether to include sweep information in the runs. Defaults to True.

Examples:

from wandb.apis.public.runs import Runs
from wandb.apis.public import Api

# Get all runs from a project that satisfy the filters
filters = {"state": "finished", "config.optimizer": "adam"}

runs = Api().runs(
   client=api.client,
   entity="entity",
   project="project_name",
   filters=filters,
)

# Iterate over runs and print details
for run in runs:
   print(f"Run name: {run.name}")
   print(f"Run ID: {run.id}")
   print(f"Run URL: {run.url}")
   print(f"Run state: {run.state}")
   print(f"Run config: {run.config}")
   print(f"Run summary: {run.summary}")
   print(f"Run history (samples=5): {run.history(samples=5)}")
   print("----------")

# Get histories for all runs with specific metrics
histories_df = runs.histories(
   samples=100,  # Number of samples per run
   keys=["loss", "accuracy"],  # Metrics to fetch
   x_axis="_step",  # X-axis metric
   format="pandas",  # Return as pandas DataFrame
)

property Runs.length

method Runs.histories

histories(
    samples: 'int' = 500,
    keys: 'list[str] | None' = None,
    x_axis: 'str' = '_step',
    format: "Literal['default', 'pandas', 'polars']" = 'default',
    stream: "Literal['default', 'system']" = 'default'
)

Return sampled history metrics for all runs that fit the filters conditions.

Args:

  • samples: The number of samples to return per run
  • keys: Only return metrics for specific keys
  • x_axis: Use this metric as the xAxis defaults to _step
  • format: Format to return data in, options are “default”, “pandas”, “polars”
  • stream: “default” for metrics, “system” for machine metrics

Returns:

  • pandas.DataFrame: If format="pandas", returns a pandas.DataFrame of history metrics.
  • polars.DataFrame: If format="polars", returns a polars.DataFrame of history metrics.
  • list of dicts: If format="default", returns a list of dicts containing history metrics with a run_id key.

6.19 - Sweep

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Sweep

The set of runs associated with the sweep.

Attributes:

  • runs (Runs): List of runs
  • id (str): Sweep ID
  • project (str): The name of the project the sweep belongs to
  • config (dict): Dictionary containing the sweep configuration
  • state (str): The state of the sweep. Can be “Finished”, “Failed”, “Crashed”, or “Running”.
  • expected_run_count (int): The number of expected runs for the sweep

method Sweep.__init__

__init__(client, entity, project, sweep_id, attrs=None)

property Sweep.config

The sweep configuration used for the sweep.

property Sweep.entity

The entity associated with the sweep.

property Sweep.expected_run_count

Return the number of expected runs in the sweep or None for infinite runs.

Returns:

  • int | None: The expected_run_count property value.

property Sweep.name

The name of the sweep.

Returns the first name that exists in the following priority order:

  1. User-edited display name 2. Name configured at creation time 3. Sweep ID

property Sweep.order

Return the order key for the sweep.

property Sweep.path

Returns the path of the project.

The path is a list containing the entity, project name, and sweep ID.

property Sweep.url

The URL of the sweep.

The sweep URL is generated from the entity, project, the term “sweeps”, and the sweep ID.run_id. For SaaS users, it takes the form of https://wandb.ai/entity/project/sweeps/sweeps_ID.

property Sweep.username

Deprecated. Use Sweep.entity instead.

method Sweep.best_run

best_run(order=None)

Return the best run sorted by the metric defined in config or the order passed in.

classmethod Sweep.get

get(
    client: 'RetryingClient',
    entity: Optional[str] = None,
    project: Optional[str] = None,
    sid: Optional[str] = None,
    order: Optional[str] = None,
    query: Optional[str] = None,
    **kwargs
)

Execute a query against the cloud backend.

Args:

  • client: The client to use to execute the query.
  • entity: The entity (username or team) that owns the project.
  • project: The name of the project to fetch sweep from.
  • sid: The sweep ID to query.
  • order: The order in which the sweep’s runs are returned.
  • query: The query to use to execute the query.
  • **kwargs: Additional keyword arguments to pass to the query.

method Sweep.to_html

to_html(height=420, hidden=False)

Generate HTML containing an iframe displaying this sweep.

6.20 - Sweeps

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Sweeps

A lazy iterator over a collection of Sweep objects.

Examples:

from wandb.apis.public import Api

sweeps = Api().project(name="project_name", entity="entity").sweeps()

# Iterate over sweeps and print details
for sweep in sweeps:
    print(f"Sweep name: {sweep.name}")
    print(f"Sweep ID: {sweep.id}")
    print(f"Sweep URL: {sweep.url}")
    print("----------")

method Sweeps.__init__

__init__(
    client: wandb.apis.public.api.RetryingClient,
    entity: str,
    project: str,
    per_page: int = 50
) → Sweeps

An iterable collection of Sweep objects.

Args:

  • client: The API client used to query W&B.
  • entity: The entity which owns the sweeps.
  • project: The project which contains the sweeps.
  • per_page: The number of sweeps to fetch per request to the API.

property Sweeps.length

6.21 - Team

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class Team

A class that represents a W&B team.

This class provides methods to manage W&B teams, including creating teams, inviting members, and managing service accounts. It inherits from Attrs to handle team attributes.

method Team.__init__

__init__(client, name, attrs=None)

Args:

  • client (wandb.apis.public.Api): The api instance to use
  • name (str): The name of the team
  • attrs (dict): Optional dictionary of team attributes

Note:

Team management requires appropriate permissions.

classmethod Team.create

create(api, team, admin_username=None)

Create a new team.

Args:

  • api: (Api) The api instance to use
  • team: (str) The name of the team
  • admin_username: (str) optional username of the admin user of the team, defaults to the current user.

Returns: A Team object

method Team.create_service_account

create_service_account(description)

Create a service account for the team.

Args:

  • description: (str) A description for this service account

Returns: The service account Member object, or None on failure

method Team.invite

invite(username_or_email, admin=False)

Invite a user to a team.

Args:

  • username_or_email: (str) The username or email address of the user you want to invite.
  • admin: (bool) Whether to make this user a team admin. Defaults to False.

Returns: True on success, False if user was already invited or didn’t exist.

6.22 - User

Training and fine-tuning models is done elsewhere in the W&B Python SDK. Use the Public API for querying and managing data after it has been logged to W&B.

View the source code

class User

A class representing a W&B user with authentication and management capabilities.

This class provides methods to manage W&B users, including creating users, managing API keys, and accessing team memberships. It inherits from Attrs to handle user attributes.

method User.__init__

__init__(client, attrs)

Args:

  • client: (wandb.apis.internal.Api) The client instance to use
  • attrs: (dict) The user attributes

Note:

Some operations require admin privileges

property User.api_keys

List of API key names associated with the user.

Returns:

  • list[str]: Names of API keys associated with the user. Empty list if user has no API keys or if API key data hasn’t been loaded.

property User.teams

List of team names that the user is a member of.

Returns:

  • list (list): Names of teams the user belongs to. Empty list if user has no team memberships or if teams data hasn’t been loaded.

property User.user_api

An instance of the api using credentials from the user.

classmethod User.create

create(api, email, admin=False)

Create a new user.

Args:

  • api (Api): The api instance to use
  • email (str): The name of the team
  • admin (bool): Whether this user should be a global instance admin

Returns: A User object

method User.delete_api_key

delete_api_key(api_key)

Delete a user’s api key.

Args:

  • api_key (str): The name of the API key to delete. This should be one of the names returned by the api_keys property.

Returns: Boolean indicating success

Raises: ValueError if the api_key couldn’t be found

method User.generate_api_key

generate_api_key(description=None)

Generate a new api key.

Args:

  • description (str, optional): A description for the new API key. This can be used to identify the purpose of the API key.

Returns: The new api key, or None on failure