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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.

2 - Api

View the source code

class Api

Used for querying the wandb server.

Examples: Most common way to initialize wandb.Api()

Args:

  • overrides: (dict) You can set base_url if you are using a wandb server

  • other than https: //api.wandb.ai. You can also set defaults for entity, project, and run.

method Api.__init__

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

property Api.api_key

property Api.client

property Api.default_entity

property Api.user_agent

property Api.viewer

method Api.artifact

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

Return a single artifact by parsing path in the form project/name or entity/project/name.

Args:

  • name: (str) An artifact name. May be prefixed with project/ or entity/project/. 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: (str, optional) 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.

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

Return a single artifact collection by type and parsing path in the form entity/project/name.

Args:

  • type_name: (str) The type of artifact collection to fetch.
  • name: (str) An artifact collection name. May be prefixed with entity/project.

Returns: An ArtifactCollection object.

method Api.artifact_collection_exists

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

Return whether an artifact collection exists within a specified project and entity.

Args:

  • name: (str) An artifact collection name. May be prefixed with entity/project. If entity or project is not specified, it will be inferred from the override params if populated. Otherwise, entity will be pulled from the user settings and project will default to “uncategorized”.
  • type: (str) The type of artifact collection

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

method Api.artifact_collections

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

Return a collection of matching artifact collections.

Args:

  • project_name: (str) The name of the project to filter on.
  • type_name: (str) The name of the artifact type to filter on.
  • per_page: (int, optional) 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

Return whether an artifact version exists within a specified project and entity.

Args:

  • name: (str) An artifact name. May be prefixed with entity/project. If entity or project is not specified, it will be inferred from the override params if populated. Otherwise, entity will be pulled from the user settings and project will default to “uncategorized”. Valid names can be in the following forms:
  • name: version
  • name: alias
  • type: (str, optional) The type of artifact

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

method Api.artifact_type

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

Return the matching ArtifactType.

Args:

  • type_name: (str) The name of the artifact type to retrieve.
  • project: (str, optional) 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

Return a collection of matching artifact types.

Args:

  • project: (str, optional) If given, a 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 artifacts(type_name, name) instead.

method Api.artifacts

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

Return an Artifacts collection from the given parameters.

Args:

  • type_name: (str) The type of artifacts to fetch.
  • name: (str) An artifact collection name. May be prefixed with entity/project.
  • per_page: (int, optional) Sets the page size for query pagination. None will use the default size. Usually there is no reason to change this.
  • tags: (list[str], optional) Only return artifacts with all of these tags.

Returns: An iterable Artifacts object.

method Api.create_project

create_project(name: str, entity: str) → None

Create a new project.

Args:

  • name: (str) The name of the new project.
  • entity: (str) The entity of the new project.

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: (str, optional) The ID to assign to the run, if given. The run ID is automatically generated by default, so in general, you do not need to specify this and should only do so at your own risk.
  • project: (str, optional) If given, the project of the new run.
  • entity: (str, optional) If given, the entity of the new run.

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 (launch).

Args:

  • name: (str) Name of the queue to create
  • type: (str) Type of resource to be used for the queue. One of “local-container”, “local-process”, “kubernetes”, “sagemaker”, or “gcp-vertex”.
  • entity: (str) Optional name of the entity to create the queue. If None, will use the configured or default entity.
  • prioritization_mode: (str) Optional version of prioritization to use. Either “V0” or None
  • config: (dict) Optional default resource configuration to be used for the queue. Use handlebars (eg. {{var}}) to specify template variables.
  • template_variables: (dict) A dictionary of template variable schemas to be used with the config. Expected format of: `{
  • "var-name": {
  • "schema": {
  • "type": (“string”, “number”, or “integer”),
  • "default": (optional value),
  • "minimum": (optional minimum),
  • "maximum": (optional maximum),
  • "enum": […"(options)"] } } }`

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, admin_username=None)

Create a new team.

Args:

  • 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 Api.create_user

create_user(email, admin=False)

Create a new user.

Args:

  • email: (str) The email address of the user
  • admin: (bool) Whether this user should be a global instance admin

Returns: A User object

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)

Return a run, sweep, project or report from a path.

Examples:

    project = api.from_path("my_project")
    team_project = api.from_path("my_team/my_project")
    run = api.from_path("my_team/my_project/runs/id")
    sweep = api.from_path("my_team/my_project/sweeps/id")
    report = api.from_path("my_team/my_project/reports/My-Report-Vm11dsdf")
   ``` 



**Args:**

- `path`:  (str) 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 

---

### <kbd>method</kbd> `Api.job`

```python
job(name: Optional[str], path: Optional[str] = None) → public.Job

Return a Job from the given parameters.

Args:

  • name: (str) The job name.
  • path: (str, optional) If given, the root path in which 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: (str) The entity for the listed job(s).
  • project: (str) The project for the listed job(s).

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: (str) The project name.
  • entity: (str) 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: Optional[int] = 200
) → public.Projects

Get projects for a given entity.

Args:

  • entity: (str) 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: (int) Sets the page size for query pagination. None will use the default size. Usually there is no reason to change this.

Returns: A Projects object which is an iterable collection of Project objects.

method Api.queued_run

queued_run(
    entity,
    project,
    queue_name,
    run_queue_item_id,
    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.reports

reports(
    path: str = '',
    name: Optional[str] = None,
    per_page: Optional[int] = 50
) → public.Reports

Get reports for a given project path.

WARNING: This api is in beta and will likely change in a future release

Args:

  • path: (str) path to project the report resides in, should be in the form: “entity/project”
  • name: (str, optional) optional name of the report requested.
  • per_page: (int) Sets the page size for query pagination. None will use the default size. Usually there is no reason to change this.

Returns: A Reports object which is an iterable collection of BetaReport objects.

method Api.run

run(path='')

Return a single run by parsing path in the form entity/project/run_id.

Args:

  • path: (str) 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, name)

Return the named RunQueue for entity.

To create a new RunQueue, use wandb.Api().create_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
)

Return a set of runs from a project that match the filters provided.

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

Examples: Find runs in my_project where config.experiment_name has been set to “foo” api.runs( path="my_entity/my_project", filters={"config.experiment_name": "foo"}, )

Find runs in my_project where config.experiment_name has been set to “foo” or “bar” api.runs( path="my_entity/my_project", filters={ "$or": [ {"config.experiment_name": "foo"}, {"config.experiment_name": "bar"}, ] }, )

Find runs in my_project where config.experiment_name matches a regex (anchors are not supported) api.runs( path="my_entity/my_project", filters={"config.experiment_name": {"$regex": "b.*"}}, )

Find runs in my_project where the run name matches a regex (anchors are not supported) api.runs( path="my_entity/my_project", filters={"display_name": {"$regex": "^foo.*"}}, )

Find runs in my_project where config.experiment contains a nested field “category” with value “testing” api.runs( path="my_entity/my_project", filters={"config.experiment.category": "testing"}, )

Find runs in my_project with a loss value of 0.5 nested in a dictionary under model1 in the summary metrics api.runs( path="my_entity/my_project", filters={"summary_metrics.model1.loss": 0.5}, )

Find runs in my_project sorted by ascending loss api.runs(path="my_entity/my_project", order="+summary_metrics.loss")

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. If you prepend order with a - order is descending (default). 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.

method Api.sweep

sweep(path='')

Return a sweep by parsing path in the form entity/project/sweep_id.

Args:

  • path: (str, optional) 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: (str) The name of the team.

Returns: A Team object.

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 (launch).

Args:

  • name: (str) Name of the queue to create
  • entity: (str) Optional name of the entity to create the queue. If None, will use the configured or default entity.
  • resource_config: (dict) Optional default resource configuration to be used for the queue. Use handlebars (eg. {{var}}) to specify template variables.
  • resource_type: (str) Type of resource to be used for the queue. One of “local-container”, “local-process”, “kubernetes”, “sagemaker”, or “gcp-vertex”.
  • template_variables: (dict) A dictionary of template variable schemas to be used with the config. Expected format of: `{
  • "var-name": {
  • "schema": {
  • "type": (“string”, “number”, or “integer”),
  • "default": (optional value),
  • "minimum": (optional minimum),
  • "maximum": (optional maximum),
  • "enum": […"(options)"] } } }`
  • external_links: (dict) Optional dictionary of external links to be used with the queue. Expected format of: `{
  • "name": “url” }`
  • prioritization_mode: (str) 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.

Note: This function only works for Local Admins, if you are trying to get your own user object, please use api.viewer.

Args:

  • username_or_email: (str) The username or email address of the user

Returns: A User object or None if a user couldn’t be found

method Api.users

users(username_or_email: str) → List[ForwardRef('public.User')]

Return all users from a partial username or email address query.

Note: This function only works for Local Admins, if you are trying to get your own user object, please use api.viewer.

Args:

  • username_or_email: (str) The prefix or suffix of the user you want to find

Returns: An array of User objects

3 - 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 wandb.log_artifact() to log it.

Args:

  • name: 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: 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.
  • description: 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: Additional information about an artifact. Specify metadata as a dictionary of key-value pairs. You can specify no more than 100 total keys.

Returns: An Artifact object.

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

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.

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.

property Artifact.commit_hash

The hash returned when this artifact was committed.

property Artifact.created_at

Timestamp when the artifact was created.

property Artifact.description

A description of the artifact.

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.

property Artifact.distributed_id

property Artifact.entity

The name of the entity of the secondary (portfolio) artifact collection.

property Artifact.file_count

The number of files (including references).

property Artifact.id

The artifact’s ID.

property Artifact.incremental

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.

property Artifact.metadata

User-defined artifact metadata.

Structured data associated with the artifact.

property Artifact.name

The artifact name and version in its secondary (portfolio) collection.

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

property Artifact.project

The name of the project of the secondary (portfolio) artifact collection.

property Artifact.qualified_name

The entity/project/name of the secondary (portfolio) collection.

property Artifact.size

The total size of the artifact in bytes.

Includes any references tracked by this artifact.

property Artifact.source_collection

The artifact’s primary (sequence) collection.

property Artifact.source_entity

The name of the entity of the primary (sequence) artifact collection.

property Artifact.source_name

The artifact name and version in its primary (sequence) collection.

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

property Artifact.source_project

The name of the project of the primary (sequence) artifact collection.

property Artifact.source_qualified_name

The entity/project/name of the primary (sequence) collection.

property Artifact.source_version

The artifact’s version in its primary (sequence) collection.

A string with the format v{number}.

property Artifact.state

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

property Artifact.tags

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

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

property Artifact.type

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

property Artifact.updated_at

The time when the artifact was last updated.

property Artifact.url

Constructs the URL of the artifact.

Returns:

  • str: The URL of the artifact.

property Artifact.use_as

property Artifact.version

The artifact’s version in its secondary (portfolio) collection.

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'
) → 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: “mutable” | “immutable”. 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.

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, W&B will 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 (i.e. a member of a portfolio collection): only the link is deleted, and the source artifact is unaffected.

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 (i.e. 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
) → 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).

Returns: The path to the downloaded contents.

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • RuntimeError: If the artifact is attempted to be downloaded in offline mode.

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 wandb.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) → None

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.

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.

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 will be 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 portfolio (a promoted collection of artifacts).

Raises:

  • ArtifactNotLoggedError: If the artifact is not logged.
  • ValueError: If the artifact is not linked, i.e. 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.

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.

4 - 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.

Usage: ```python import wandb

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

5 - define_metric

View the source code

function wandb.define_metric

wandb.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.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 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”, “best”, “copy” and “none”. “best” is used together with the goal parameter. “none” prevents a summary from being generated. “copy” is deprecated and should not be used.
  • goal: Specify how to interpret the “best” summary type. Supported options are “minimize” and “maximize”.
  • 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.

6 - Error

View the source code

class Error

Base W&B Error.

method Error.__init__

__init__(message, context: Optional[dict] = None) → None

7 - 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=...).

8 - 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'] | 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

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.

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: 

```python
     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 default entity. To change the default entity, 
 - `go to [your settings](https`: //wandb.ai/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. 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](https`: //docs.wandb.com/guides/runs/grouping). 
 - `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](https`: //docs.wandb.com/guides/runs/resuming). 
 - `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](https`: //wandb.ai/settings). 
 - `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](https`: //docs.wandb.com/guides/integrations/openai-gym). 
 - `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](https`: //docs.wandb.ai/ref/python/run) 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.

9 - link_model

View the source code 
wandb.link_model(
    path: 'StrPath',
    registered_model_name: 'str',
    name: 'str | None' = None,
    aliases: 'list[str] | None' = None
) → None

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

The linked model version will be visible in the UI for the specified registered model.

Steps: - 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: (str) - 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: (str, optional) - 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: (List[str], optional) - alias(es) 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.

Examples:

   run.link_model(
        path="/local/directory",
        registered_model_name="my_reg_model",
        name="my_model_artifact",
        aliases=["production"],
   )
   ``` 

Invalid usage ```python
   run.link_model(
        path="/local/directory",
        registered_model_name="my_entity/my_project/my_reg_model",
        name="my_model_artifact",
        aliases=["production"],
   )

   run.link_model(
        path="/local/directory",
        registered_model_name="my_reg_model",
        name="my_entity/my_project/my_model_artifact",
        aliases=["production"],
   )
   ``` 



**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:**
None

10 - log

View the source code

function wandb.log

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

Upload run data.

Use 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 run.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.

Logged values don’t have to be scalars. Logging any wandb object is supported. For example run.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. You can use wandb.Table to log structured data. See our guide to logging tables for details.

The W&B UI 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”:

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.

The W&B step

With basic usage, each call to log creates a new “step”. The step must always increase, and it is not possible to log to a previous step.

Note that you can use any metric as the X axis in charts. In many cases, it is better to treat the W&B step like you’d treat a timestamp rather than a training step.

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

See also define_metric.

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

# 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.
  • sync: This argument is deprecated and does nothing.

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

Basic usage ```python

import wandb

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

Incremental logging ```python

import wandb

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

Histogram ```python

import numpy as np
import wandb

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

Image from numpy ```python

import numpy as np
import wandb

run = 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)
run.log({"examples": examples})
```

Image from PIL ```python

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

run = 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)
run.log({"examples": examples})
```

Video from numpy ```python

import numpy as np
import wandb

run = wandb.init()
# 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 ```python

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

run = wandb.init()
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 ```python

import wandb

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

3D Object ```python

import wandb

run = wandb.init()
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

11 - log_artifact

View the source code

function wandb.log_artifact

wandb.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.

12 - log_model

View the source code

function wandb.log_model

wandb.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.

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: (str, optional) A name to assign to the model artifact that the file contents will be added to.
  • The string must contain only the following alphanumeric characters: dashes, underscores, and dots. This will default to the basename of the path prepended with the current run id if not specified.
  • aliases: (list, optional) Aliases to apply to the created model artifact, defaults to ["latest"]

Examples:

   run.log_model(
        path="/local/directory",
        name="my_model_artifact",
        aliases=["production"],
   )
   ``` 

Invalid usage ```python
   run.log_model(
        path="/local/directory",
        name="my_entity/my_project/my_model_artifact",
        aliases=["production"],
   )
   ``` 



**Raises:**

- `ValueError`:  if name has invalid special characters 



**Returns:**
None

13 - 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
) → 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: (string, optional) Can be “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”.
  • key: (string, optional) The API key to use.
  • relogin: (bool, optional) If true, will re-prompt for API key.
  • host: (string, optional) The host to connect to.
  • force: (bool, optional) If true, will force a relogin.
  • timeout: (int, optional) Number of seconds to wait for user input.
  • verify: (bool) Verify the credentials with the W&B server.

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

14 - 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.log().

Args:

  • vega_spec_name (str): The name or identifier of the Vega-Lite spec that defines the visualization structure.
  • data_table (wandb.Table): A wandb.Table object containing the data to be visualized.
  • fields (dict[str, Any]): A mapping between the fields in the Vega-Lite spec and the corresponding columns in the data table to be visualized.
  • string_fields (dict[str, Any] | None): A dictionary for providing values for any string constants required by the custom visualization.
  • split_table (bool): 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 data_table is not a wandb.Table object.

15 - save

View the source code

function wandb.save

wandb.save(
    glob_str: 'str | os.PathLike | None' = None,
    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. It’s best understood through

examples:

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

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

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

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

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

Note: 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.

16 - 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()
   ```

17 - 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.

Args:

  • sweep: The configuration of a hyperparameter search. (or configuration generator). See
  • [Sweep configuration structure](https: //docs.wandb.ai/guides/sweeps/define-sweep-configuration) for information on how to define your sweep. 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:

  • sweep_id: str. A unique identifier for the sweep.

18 - teardown

View the source code

function teardown

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

Waits for wandb 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.

19 - termerror

View the source code

function termerror

termerror(
    string: 'str',
    newline: 'bool' = True,
    repeat: 'bool' = True,
    prefix: 'bool' = True
) → None

Log an error to stderr.

The arguments are the same as for termlog().

20 - termlog

View the source code

function termlog

termlog(
    string: 'str' = '',
    newline: 'bool' = True,
    repeat: 'bool' = True,
    prefix: 'bool' = True
) → None

Log an informational message to stderr.

The message may contain ANSI color sequences and the \n character. Colors are stripped if stderr is not a TTY.

Args:

  • string: The message to display.
  • newline: Whether to add a newline to the end of the string.
  • repeat: If false, then the string is not printed if an exact match has already been printed through any of the other logging functions in this file.
  • prefix: Whether to include the ‘wandb:’ prefix.

21 - termsetup

View the source code

function termsetup

termsetup(
    settings: 'wandb.Settings',
    logger: 'SupportsLeveledLogging | None'
) → None

Configure the global logging functions.

Args:

  • settings: The settings object passed to wandb.setup() or wandb.init().
  • logger: A fallback logger to use for “silent” mode. In this mode, the logger is used instead of printing to stderr.

22 - termwarn

View the source code

function termwarn

termwarn(
    string: 'str',
    newline: 'bool' = True,
    repeat: 'bool' = True,
    prefix: 'bool' = True
) → None

Log a warning to stderr.

The arguments are the same as for termlog().

23 - unwatch

View the source code

function wandb.unwatch

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

Remove pytorch model topology, gradient and parameter hooks.

Args: models (torch.nn.Module | Sequence[torch.nn.Module]): Optional list of pytorch models that have had watch called on them

24 - use_artifact

View the source code

function wandb.use_artifact

wandb.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: (str or Artifact) An artifact name. May be prefixed with project/ or entity/project/. 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 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.

25 - use_model

View the source code

function wandb.use_model

wandb.use_model(name: 'str') → FilePathStr

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

Args:

  • name: (str) 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

Examples:

   run.use_model(
        name="my_model_artifact:latest",
   )

   run.use_model(
        name="my_project/my_model_artifact:v0",
   )

   run.use_model(
        name="my_entity/my_project/my_model_artifact:<digest>",
   )
   ``` 

Invalid usage ```python
   run.use_model(
        name="my_entity/my_project/my_model_artifact",
   )
   ``` 



**Raises:**

- `AssertionError`:  if model artifact 'name' is of a type that does not contain the substring 'model'. 



**Returns:**

- `path`:  (str) path to downloaded model artifact file(s).

26 - watch

View the source code

function wandb.watch

wandb.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

Hooks into the given PyTorch model(s) to monitor gradients and the model’s computational graph.

This function can track parameters, gradients, or both during training. It should be extended to support arbitrary machine learning models in the future.

Args: models (Union[torch.nn.Module, Sequence[torch.nn.Module]]): A single model or a sequence of models to be monitored. criterion (Optional[torch.F]): The loss function being optimized (optional). log (Optional[Literal[“gradients”, “parameters”, “all”]]): Specifies whether to log “gradients”, “parameters”, or “all”. Set to None to disable logging. (default=“gradients”) log_freq (int): Frequency (in batches) to log gradients and parameters. (default=1000) idx (Optional[int]): Index used when tracking multiple models with wandb.watch. (default=None) log_graph (bool): 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.