init

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

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'] | None) = None,
    force: (bool | None) = None,
    anonymous: (Literal['never', 'allow', 'must'] | None) = None,
    reinit: (bool | None) = 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

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.

Call wandb.init() to start a run before logging data with wandb.log(). When you're done logging data, call wandb.finish() to end the run. If you don't call wandb.finish(), the run will end when your script exits.

For more on using wandb.init(), including detailed examples, check out our guide and FAQs.

Examples:

Explicitly set the entity and project and choose a name for the run:

import wandb

run = wandb.init(
    entity="geoff",
    project="capsules",
    name="experiment-2021-10-31",
)

# ... your training code here ...

run.finish()

Add metadata about the run using the config argument:

import wandb

config = {"lr": 0.01, "batch_size": 32}
with wandb.init(config=config) as run:
    run.config.update({"architecture": "resnet", "depth": 34})

    # ... your training code here ...

Note that you can use wandb.init() as a context manager to automatically call wandb.finish() at the end of the block.

Args
entity	The username or team name under which the runs will be logged. The entity must already exist, so ensure you’ve created your account or team in the UI before starting to log runs. If not specified, the run will default your defualt 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	An absolute path to the directory where metadata and downloaded files will be stored. When calling download() on an artifact, files will be saved to this directory. If not specified, this defaults to the ./wandb directory.
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. The identifier must not contain any of the following special characters: / \ # ? % :. 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. For more information, refer to our guide to grouping runs.
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.
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	Determines if multiple wandb.init() calls can start new runs within the same process. By default (False), if an active run exists, calling wandb.init() returns the existing run instead of creating a new one. When reinit=True, the active run is finished before a new run is initialized. In notebook environments, runs are reinitialized by default unless reinit is explicitly set to False.
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. Note: If resume is set, fork_from and resume_from cannot be used. When resume is unset, the system will always start a new run. For more details, see our guide to resuming runs.
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: 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: 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. For additional details, see our guide for gym integration.
settings	Specifies a dictionary or wandb.Settings object with advanced settings for the run.

Returns
A Run object, which is a handle to the current run. Use this object to perform operations like logging data, saving files, and finishing the run. See the Run API for more details.

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 W&B server.
UsageError	If the user provided invalid arguments to the function.
KeyboardInterrupt	If the user interrupts the run initialization process. If the user interrupts the run initialization process.