Run

View source on GitHub

A unit of computation logged by wandb. Typically, this is an ML experiment.

Run(
 settings: Settings,
 config: Optional[Dict[str, Any]] = None,
 sweep_config: Optional[Dict[str, Any]] = None,
 launch_config: Optional[Dict[str, Any]] = None
) -> None

Create a run with wandb.init():

import wandb

run = wandb.init()

There is only ever at most one active wandb.Run in any process, and it is accessible as wandb.run:

import wandb

assert wandb.run is None

wandb.init()

assert wandb.run is not None

anything you log with wandb.log will be sent to that run.

If you want to start more runs in the same script or notebook, you'll need to finish the run that is in-flight. Runs can be finished with wandb.finish or by using them in a with block:

import wandb

wandb.init()
wandb.finish()

assert wandb.run is None

with wandb.init() as run:
 pass # log data here

assert wandb.run is None

See the documentation for wandb.init for more on creating runs, or check out our guide to wandb.init.

In distributed training, you can either create a single run in the rank 0 process and then log information only from that process, or you can create a run in each process, logging from each separately, and group the results together with the group argument to wandb.init. For more details on distributed training with W&B, check out our guide.

Currently, there is a parallel Run object in the wandb.Api. Eventually these two objects will be merged.

Attributes
summary	(Summary) Single values set for each wandb.log() key. By default, summary is set to the last value logged. You can manually set summary to the best value, like max accuracy, instead of the final value.
config	Config object associated with this run.
dir	The directory where files associated with the run are saved.
entity	The name of the W&B entity associated with the run. Entity can be a user name or the name of a team or organization.
group	Name of the group associated with the run. Setting a group helps the W&B UI organize runs in a sensible way. If you are doing a distributed training you should give all of the runs in the training the same group. If you are doing crossvalidation you should give all the crossvalidation folds the same group.
id	Identifier for this run.
mode	For compatibility with 0.9.x and earlier, deprecate eventually.
name	Display name of the run. Display names are not guaranteed to be unique and may be descriptive. By default, they are randomly generated.
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 $x + 3$.
path	Path to the run. Run paths include entity, project, and run ID, in the format entity/project/run_id.
project	Name of the W&B project associated with the run.
resumed	True if the run was resumed, False otherwise.
settings	A frozen copy of run's Settings object.
start_time	Unix timestamp (in seconds) of when the run started.
starting_step	The first step of the run.
step	Current value of the step. This counter is incremented by wandb.log.
sweep_id	ID of the sweep associated with the run, if there is one.
tags	Tags associated with the run, if there are any.
url	The W&B url associated with the run.

Methods

alert

View source

alert(
 title: str,
 text: str,
 level: Optional[Union[str, 'AlertLevel']] = None,
 wait_duration: Union[int, float, timedelta, None] = None
) -> None

Launch an alert with the given title and text.

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

define_metric

View source

define_metric(
 name: str,
 step_metric: Union[str, wandb_metric.Metric, None] = None,
 step_sync: Optional[bool] = None,
 hidden: Optional[bool] = None,
 summary: Optional[str] = None,
 goal: Optional[str] = None,
 overwrite: Optional[bool] = None,
 **kwargs
) -> wandb_metric.Metric

Define metric properties which will later be logged with wandb.log().

Arguments
name	Name of the metric.
step_metric	Independent variable associated with the metric.
step_sync	Automatically add step_metric to history if needed. Defaults to True if step_metric is specified.
hidden	Hide this metric from automatic plots.
summary	Specify aggregate metrics added to summary. Supported aggregations: "min,max,mean,best,last,none" Default aggregation is copy Aggregation best defaults to goal==minimize
goal	Specify direction for optimizing the metric. Supported directions: "minimize,maximize"

Returns
A metric object is returned that can be further specified.

detach

View source

detach() -> None

display

View source

display(
 height: int = 420,
 hidden: bool = (False)
) -> bool

Display this run in jupyter.

finish

View source

finish(
 exit_code: Optional[int] = None,
 quiet: Optional[bool] = None
) -> None

Mark a run as finished, and finish uploading all data.

This is used when creating multiple runs in the same process. We automatically call this method when your script exits or if you use the run context manager.

Arguments
exit_code	Set to something other than 0 to mark a run as failed
quiet	Set to true to minimize log output

finish_artifact

View source

finish_artifact(
 artifact_or_path: Union[wandb_artifacts.Artifact, str],
 name: Optional[str] = None,
 type: Optional[str] = None,
 aliases: Optional[List[str]] = None,
 distributed_id: Optional[str] = None
) -> wandb_artifacts.Artifact

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

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

Arguments
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. 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	(str) The type of artifact to log, examples include dataset, model
aliases	(list, optional) Aliases to apply to this artifact, defaults to ["latest"]
distributed_id	(string, optional) Unique string that all distributed jobs share. If None, defaults to the run's group name.

Returns
An Artifact object.

get_project_url

View source

get_project_url() -> Optional[str]

Return the url for the W&B project associated with the run, if there is one.

Offline runs will not have a project url.

get_sweep_url

View source

get_sweep_url() -> Optional[str]

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

get_url

View source

get_url() -> Optional[str]

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

Offline runs will not have a url.

join

View source

join(
 exit_code: Optional[int] = None
) -> None

Deprecated alias for finish() - use finish instead.

link_artifact

View source

link_artifact(
 artifact: Union[public.Artifact, Artifact],
 target_path: str,
 aliases: Optional[List[str]] = None
) -> None

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

The linked artifact will be visible in the UI for the specified portfolio.

Arguments
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
None

log

View source

log(
 data: Dict[str, Any],
 step: Optional[int] = None,
 commit: Optional[bool] = None,
 sync: Optional[bool] = None
) -> None

Log a dictonary of data to the current run's history.

Use wandb.log to log data from runs, such as scalars, images, video, histograms, plots, and tables.

See our guides to logging for live examples, code snippets, best practices, and more.

The most basic usage is wandb.log({"train-loss": 0.5, "accuracy": 0.9}). This will save the loss and accuracy to the run's history and update the summary values for these metrics.

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

In the UI, summary values show up in the run table to compare single values across runs. Summary values can also be set directly with wandb.run.summary["key"] = value.

Logged values don't have to be scalars. Logging any wandb object is supported. For example wandb.log({"example": wandb.Image("myimage.jpg")}) will log an example image which will be displayed nicely in the W&B UI. See the reference documentation for all of the different supported types or check out our guides to logging for examples, from 3D molecular structures and segmentation masks to PR curves and histograms. wandb.Tables can be used to logged structured data. See our guide to logging tables for details.

Logging nested metrics is encouraged and is supported in the W&B UI. If you log with a nested dictionary like wandb.log({"train": {"acc": 0.9}, "val": {"acc": 0.8}}), the metrics will be organized into train and val sections in the W&B UI.

wandb keeps track of a global step, which by default increments with each call to wandb.log, so logging related metrics together is encouraged. If it's inconvenient to log related metrics together calling wandb.log({"train-loss": 0.5}, commit=False) and then wandb.log({"accuracy": 0.9}) is equivalent to calling wandb.log({"train-loss": 0.5, "accuracy": 0.9}).

wandb.log is not intended to be called more than a few times per second. If you want to log more frequently than that it's better to aggregate the data on the client side or you may get degraded performance.

Arguments
data	(dict, optional) A dict of serializable python objects i.e str, ints, floats, Tensors, dicts, or any of the wandb.data_types.
commit	(boolean, optional) Save the metrics dict to the wandb server and increment the step. If false wandb.log just updates the current metrics dict with the data argument and metrics won't be saved until wandb.log is called with commit=True.
step	(integer, optional) The global step in processing. This persists any non-committed earlier steps but defaults to not committing the specified step.
sync	(boolean, True) This argument is deprecated and currently doesn't change the behaviour of wandb.log.

Examples:

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

Basic usage

import wandb

wandb.init()
wandb.log({"accuracy": 0.9, "epoch": 5})

Incremental logging

import wandb

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

Histogram

import numpy as np
import wandb

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

Image from numpy

import numpy as np
import wandb

wandb.init()
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)
wandb.log({"examples": examples})

Image from PIL

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

wandb.init()
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)
wandb.log({"examples": examples})

Video from numpy

import numpy as np
import wandb

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

Matplotlib Plot

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

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

PR Curve

wandb.log({"pr": wandb.plots.precision_recall(y_test, y_probas, labels)})

3D Object

wandb.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

log_artifact

View source

log_artifact(
 artifact_or_path: Union[wandb_artifacts.Artifact, StrPath],
 name: Optional[str] = None,
 type: Optional[str] = None,
 aliases: Optional[List[str]] = None
) -> wandb_artifacts.Artifact

Declare an artifact as an output of a run.

Arguments
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. 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	(str) The type of artifact to log, examples include dataset, model
aliases	(list, optional) Aliases to apply to this artifact, defaults to ["latest"]

Returns
An Artifact object.

log_code

View source

log_code(
 root: Optional[str] = ".",
 name: Optional[str] = None,
 include_fn: Callable[[str], bool] = _is_py_path,
 exclude_fn: Callable[[str], bool] = filenames.exclude_wandb_fn
) -> Optional[Artifact]

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.

Arguments
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 returns True when it should be included and False otherwise. This defaults to: lambda path: path.endswith(".py")
exclude_fn	A callable that accepts a file path and returns True when it should be excluded and False otherwise. This defaults to: lambda path: False

Examples:

Basic usage

run.log_code()

Advanced usage

run.log_code(
 "../", include_fn=lambda path: path.endswith(".py") or path.endswith(".ipynb")
)

Returns
An Artifact object if code was logged

mark_preempting

View source

mark_preempting() -> None

Mark this run as preempting.

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

plot_table

View source

@staticmethod
plot_table(
 vega_spec_name: str,
 data_table: "wandb.Table",
 fields: Dict[str, Any],
 string_fields: Optional[Dict[str, Any]] = None
) -> CustomChart

Create a custom plot on a table.

Arguments
vega_spec_name	the name of the spec for the plot
data_table	a wandb.Table object containing the data to be used on the visualization
fields	a dict mapping from table keys to fields that the custom visualization needs
string_fields	a dict that provides values for any string constants the custom visualization needs

project_name

View source

project_name() -> str

restore

View source

restore(
 name: str,
 run_path: Optional[str] = None,
 replace: bool = (False),
 root: Optional[str] = None
) -> Union[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.

Arguments
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
wandb.CommError	if we can't connect to the wandb backend
ValueError	if the file is not found or can't find run_path

save

View source

save(
 glob_str: Optional[str] = None,
 base_path: Optional[str] = None,
 policy: "PolicyName" = "live"
) -> Union[bool, List[str]]

Ensure all files matching glob_str are synced to wandb with the policy specified.

Arguments
glob_str	(string) a relative or absolute path to a unix glob or regular path. If this isn't specified the method is a noop.
base_path	(string) the base path to run the glob relative to
policy	(string) on of live, now, or end - live: upload the file as it changes, overwriting the previous version - now: upload the file once now - end: only upload file when the run ends

status

View source

status() -> RunStatus

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

to_html

View source

to_html(
 height: int = 420,
 hidden: bool = (False)
) -> str

Generate HTML containing an iframe displaying the current run.

unwatch

View source

unwatch(
 models=None
) -> None

upsert_artifact

View source

upsert_artifact(
 artifact_or_path: Union[wandb_artifacts.Artifact, str],
 name: Optional[str] = None,
 type: Optional[str] = None,
 aliases: Optional[List[str]] = None,
 distributed_id: Optional[str] = None
) -> wandb_artifacts.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.

Arguments
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. 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	(str) The type of artifact to log, examples include dataset, model
aliases	(list, optional) Aliases to apply to this artifact, defaults to ["latest"]
distributed_id	(string, optional) Unique string that all distributed jobs share. If None, defaults to the run's group name.

Returns
An Artifact object.

use_artifact

View source

use_artifact(
 artifact_or_name: Union[str, public.Artifact, Artifact],
 type: Optional[str] = None,
 aliases: Optional[List[str]] = None,
 use_as: Optional[str] = None
) -> Union[public.Artifact, Artifact]

Declare an artifact as an input to a run.

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

Arguments
artifact_or_name	(str or Artifact) An artifact name. May be prefixed with entity/project/. Valid names can be in the following forms: - name:version - name:alias - digest You can also pass an Artifact object created by calling wandb.Artifact
type	(str, optional) The type of artifact to use.
aliases	(list, optional) Aliases to apply to this artifact
use_as	(string, optional) Optional string indicating what purpose the artifact was used with. Will be shown in UI.

Returns
An Artifact object.

watch

View source

watch(
 models, criterion=None, log="gradients", log_freq=100, idx=None,
 log_graph=(False)
) -> None

__enter__

View source

__enter__() -> "Run"

__exit__

View source

__exit__(
 exc_type: Type[BaseException],
 exc_val: BaseException,
 exc_tb: TracebackType
) -> bool