wandb.init

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wandb.init
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Starts a new run to track and log to W&B.
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init(
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    job_type: Optional[str] = None,
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    dir=None,
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    config: Union[Dict, str, None] = None,
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    project: Optional[str] = None,
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    entity: Optional[str] = None,
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    reinit: bool = None,
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    tags: Optional[Sequence] = None,
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    group: Optional[str] = None,
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    name: Optional[str] = None,
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    notes: Optional[str] = None,
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    magic: Union[dict, str, bool] = None,
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    config_exclude_keys=None,
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    config_include_keys=None,
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    anonymous: Optional[str] = None,
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    mode: Optional[str] = None,
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    allow_val_change: Optional[bool] = None,
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    resume: Optional[Union[bool, str]] = None,
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    force: Optional[bool] = None,
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    tensorboard=None,
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    sync_tensorboard=None,
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    monitor_gym=None,
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    save_code=None,
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    id=None,
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    settings: Union[Settings, Dict[str, Any], None] = None
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) -> Union[Run, RunDisabled, None]
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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 wandb.ai by default so you can see live visualizations.
Call wandb.init() to start a run before logging data with wandb.log():
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import wandb
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​
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wandb.init()
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# ... calculate metrics, generate media
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wandb.log({"accuracy": 0.9})
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wandb.init() returns a run object, and you can also access the run object via wandb.run:
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import wandb
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​
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run = wandb.init()
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​
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assert run is wandb.run
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At the end of your script, we will automatically call wandb.finish to finalize and cleanup the run. However, if you call wandb.init from a child process, you must explicitly call wandb.finish at the end of the child process.
For more on using wandb.init(), including detailed examples, check out our guide and FAQs.
Arguments
Text
project
(str, optional) The name of the project where you're sending the new run. If the project is not specified, the run is put in an "Uncategorized" project.
entity
(str, optional) An entity is a username or team name where you're sending runs. This entity must exist before you can send runs there, so make sure to create your account or team in the UI before starting to log runs. If you don't specify an entity, the run will be sent to your default entity, which is usually your username. Change your default entity in your settings under "default location to create new projects".
config
(dict, argparse, absl.flags, str, optional) This sets wandb.config, a dictionary-like object for saving inputs to your job, like hyperparameters for a model or settings for a data preprocessing job. The config will show up in a table in the UI that you can use to group, filter, and sort runs. Keys should not contain . in their names, and values should be under 10 MB. If dict, argparse or absl.flags: will load the key value pairs into the wandb.config object. If str: will look for a yaml file by that name, and load config from that file into the wandb.config object.
save_code
(bool, optional) Turn this on to save the main script or notebook to W&B. This is valuable for improving experiment reproducibility and to diff code across experiments in the UI. By default this is off, but you can flip the default behavior to on in your settings page.
group
(str, optional) Specify a group to organize individual runs into a larger experiment. For example, you might be doing cross validation, or you might have multiple jobs that train and evaluate a model against different test sets. Group gives you a way to organize runs together into a larger whole, and you can toggle this on and off in the UI. For more details, see our guide to grouping runs.
job_type
(str, optional) Specify the type of run, which is useful when you're grouping runs together into larger experiments using group. For example, you might have multiple jobs in a group, with job types like train and eval. Setting this makes it easy to filter and group similar runs together in the UI so you can compare apples to apples.
tags
(list, optional) A list of strings, which will populate the list of tags on this run in the UI. Tags are useful for organizing runs together, or applying temporary labels like "baseline" or "production". It's easy to add and remove tags in the UI, or filter down to just runs with a specific tag.
name
(str, optional) A short display name for this run, which is how you'll identify this run in the UI. By default we generate a random two-word name that lets you easily cross-reference runs from the table to charts. Keeping these run names short makes the chart legends and tables easier to read. If you're looking for a place to save your hyperparameters, we recommend saving those in config.
notes
(str, optional) A longer description of the run, like a -m commit message in git. This helps you remember what you were doing when you ran this run.
dir
(str, optional) An absolute path to a directory where metadata will be stored. When you call download() on an artifact, this is the directory where downloaded files will be saved. By default this is the ./wandb directory.
resume
(bool, str, optional) Sets the resuming behavior. Options: "allow", "must", "never", "auto" or None. Defaults to None. Cases: - None (default): If the new run has the same ID as a previous run, this run overwrites that data. - "auto" (or True): if the preivous run on this machine crashed, automatically resume it. Otherwise, start a new run. - "allow": if id is set with init(id="UNIQUE_ID") or WANDB_RUN_ID="UNIQUE_ID" and it is identical to a previous run, wandb will automatically resume the run with that id. Otherwise, wandb will start a new run. - "never": if id is set with init(id="UNIQUE_ID") or WANDB_RUN_ID="UNIQUE_ID" and it is identical to a previous run, wandb will crash. - "must": if id is set with init(id="UNIQUE_ID") or WANDB_RUN_ID="UNIQUE_ID" and it is identical to a previous run, wandb will automatically resume the run with the id. Otherwise wandb will crash. See our guide to resuming runs for more.
reinit
(bool, optional) Allow multiple wandb.init() calls in the same process. (default: False)
magic
(bool, dict, or str, optional) The bool controls whether we try to auto-instrument your script, capturing basic details of your run without you having to add more wandb code. (default: False) You can also pass a dict, json string, or yaml filename.
config_exclude_keys
(list, optional) string keys to exclude from wandb.config.
config_include_keys
(list, optional) string keys to include in wandb.config.
anonymous
(str, optional) Controls anonymous data logging. Options: - "never" (default): 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.
mode
(str, optional) Can be "online", "offline" or "disabled". Defaults to online.
allow_val_change
(bool, optional) Whether to allow config values to change after setting the keys once. By default we throw an exception if a config value is overwritten. If you want to track something like a varying learning rate at multiple times during training, use wandb.log() instead. (default: False in scripts, True in Jupyter)
force
(bool, optional) If True, this crashes the script if a user isn't logged in to W&B. If False, this will let the script run in offline mode if a user isn't logged in to W&B. (default: False)
sync_tensorboard
(bool, optional) Synchronize wandb logs from tensorboard or tensorboardX and save the relevant events file. (default: False)
Examples:
Set where the run is logged
You can change where the run is logged, just like changing the organization, repository, and branch in git:
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import wandb
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​
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user = "geoff"
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project = "capsules"
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display_name = "experiment-2021-10-31"
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​
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wandb.init(entity=user, project=project, name=display_name)
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Add metadata about the run to the config
Pass a dictionary-style object as the config keyword argument to add metadata, like hyperparameters, to your run.
```python import wandb config = {"lr": 3e-4, "batch_size": 32} config.update({"architecture": "resnet", "depth": 34}) wandb.init(config=config) ``` | Raises | | | :--- | :--- | | `Exception` | if problem. | | Returns | | | :--- | :--- | | A `Run` object. |
wandb.finish
wandb.log
Last modified 1mo ago
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Contents
Set where the run is logged
Add metadata about the run to the config