update metadata or metrics for an experiment after it has been completed,
pull down your results as a dataframe for post-hoc analysis in a Jupyter notebook, or
check your saved model artifacts for those tagged as ready-to-deploy.

For more on using the Public API, check out our guide.

`class` `RetryingClient`

`method` `RetryingClient.init`

__init__(client: wandb_gql.client.Client)

`property` RetryingClient.app_url

`property` RetryingClient.server_info

`method` `RetryingClient.execute`

execute(*args, **kwargs)

`method` `RetryingClient.version_supported`

version_supported(min_version: str) → bool

`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: 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) Sets the page size for query pagination. 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: 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) Sets the page size for query pagination. 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: 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. 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.registries`

registries(
    organization: Optional[str] = None,
    filter: Optional[Dict[str, Any]] = None
) → Registries

Returns a Registry iterator.

Use the iterator to search and filter registries, collections, or artifact versions across your organization’s registry.

Examples: Find all registries with the names that contain “model” ```python import wandb

 api = wandb.Api()  # specify an org if your entity belongs to multiple orgs
 api.registries(filter={"name": {"$regex": "model"}})
```

Find all collections in the registries with the name “my_collection” and the tag “my_tag” python api.registries().collections(filter={"name": "my_collection", "tag": "my_tag"})

Find all artifact versions in the registries with a collection name that contains “my_collection” and a version that has the alias “best” python api.registries().collections( filter={"name": {"$regex": "my_collection"}} ).versions(filter={"alias": "best"})

Find all artifact versions in the registries that contain “model” and have the tag “prod” or alias “best” python api.registries(filter={"name": {"$regex": "model"}}).versions( filter={"$or": [{"tag": "prod"}, {"alias": "best"}]} )

Args:

organization: (str, optional) The organization of the registry to fetch. If not specified, use the organization specified in the user’s settings.
filter: (dict, optional) MongoDB-style filter to apply to each object in the registry iterator. Fields available to filter for collections are name, description, created_at, updated_at. Fields available to filter for collections are name, tag, description, created_at, updated_at Fields available to filter for versions are tag, alias, created_at, updated_at, metadata

Returns: A registry iterator.

`method` `Api.reports`

reports(
    path: str = '',
    name: Optional[str] = None,
    per_page: 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. 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

1.2 - artifacts

View the source code

`module` `wandb.apis.public`

W&B Public API for Artifact Management.

This module provides classes for interacting with W&B artifacts and their collections. Classes include:

ArtifactTypes: A paginated collection of artifact types in a project

List and query artifact types
Access type metadata
Create new types

ArtifactCollection: A collection of related artifacts

Manage artifact collections
Update metadata and descriptions
Work with tags and aliases
Change collection types

Artifacts: A paginated collection of artifact versions

Filter and query artifacts
Access artifact metadata
Download artifacts

ArtifactFiles: A paginated collection of files within an artifact

List and query artifact files
Access file metadata
Download individual files

`function` `server_supports_artifact_collections_gql_edges`

server_supports_artifact_collections_gql_edges(
    client: 'RetryingClient',
    warn: bool = False
) → bool

Check if W&B server supports GraphQL edges for artifact collections.

`function` `artifact_collection_edge_name`

artifact_collection_edge_name(server_supports_artifact_collections: bool) → str

Return the GraphQL edge name for artifact collections or sequences.

`function` `artifact_collection_plural_edge_name`

artifact_collection_plural_edge_name(
    server_supports_artifact_collections: bool
) → str

Return the GraphQL edge name for artifact collections or sequences.

`class` `ArtifactTypes`

An iterable collection of artifact types associated with a project.

Args:

client: The client instance to use for querying W&B.
entity: The entity (user or team) that owns the project.
project: The name of the project to query for artifact types.
per_page: The number of artifact types to fetch per page. Default is 50.

`method` `ArtifactTypes.init`

__init__(
    client: wandb_gql.client.Client,
    entity: str,
    project: str,
    per_page: int = 50
)

`property` ArtifactTypes.cursor

Returns the cursor position for pagination of file results.

`property` ArtifactTypes.length

Returns None.

`property` ArtifactTypes.more

Returns True if there are more artifacts to fetch. Returns False if there are no more files to fetch.

`method` `ArtifactTypes.convert_objects`

convert_objects()

Converts GraphQL edges to ArtifactType objects.

`method` `ArtifactTypes.update_variables`

update_variables()

Updates the variables dictionary with the cursor.

`class` `ArtifactType`

An artifact object that satisfies query based on the specified type.

Args:

client: The client instance to use for querying W&B.
entity: The entity (user or team) that owns the project.
project: The name of the project to query for artifact types.
type_name: The name of the artifact type.
attrs: Optional mapping of attributes to initialize the artifact type. If not provided, the object will load its attributes from W&B upon initialization.

`method` `ArtifactType.init`

__init__(
    client: wandb_gql.client.Client,
    entity: str,
    project: str,
    type_name: str,
    attrs: Optional[Mapping[str, Any]] = None
)

`property` ArtifactType.id

The unique identifier of the artifact type.

`property` ArtifactType.name

The name of the artifact type.

`method` `ArtifactType.collection`

collection(name)

Get a specific artifact collection by name.

Args:

name (str): The name of the artifact collection to retrieve.

`method` `ArtifactType.collections`

collections(per_page=50)

Get all artifact collections associated with this artifact type.

Args:

per_page (int): The number of artifact collections to fetch per page. Default is 50.

`method` `ArtifactType.load`

load()

Load the artifact type metadata.

`class` `ArtifactCollections`

An iterable collection of artifact collections associated with a project and artifact type.

Args:

client: The client instance to use for querying W&B.
entity: The entity (user or team) that owns the project.
project: The name of the project to query for artifact collections.
type_name: The name of the artifact type for which to fetch collections.
per_page: The number of artifact collections to fetch per page. Default is 50.

`method` `ArtifactCollections.init`

__init__(
    client: wandb_gql.client.Client,
    entity: str,
    project: str,
    type_name: str,
    per_page: int = 50
)

`property` ArtifactCollections.cursor

Returns the cursor position for pagination of file results.

`property` ArtifactCollections.length

Returns the number of artifact collections.

`property` ArtifactCollections.more

Returns True if there are more artifact collections to fetch.

Returns False if there are no more files to fetch.

`method` `ArtifactCollections.convert_objects`

convert_objects()

Converts GraphQL edges to ArtifactCollection objects.

`method` `ArtifactCollections.update_variables`

update_variables()

Updates the variables dictionary with the cursor.

`class` `ArtifactCollection`

An artifact collection that represents a group of related artifacts.

Args:

client: The client instance to use for querying W&B.
entity: The entity (user or team) that owns the project.
project: The name of the project to query for artifact collections.
name: The name of the artifact collection.
type: The type of the artifact collection (e.g., “dataset”, “model”).
organization: Optional organization name if applicable.
attrs: Optional mapping of attributes to initialize the artifact collection. If not provided, the object will load its attributes from W&B upon initialization.

`method` `ArtifactCollection.init`

__init__(
    client: wandb_gql.client.Client,
    entity: str,
    project: str,
    name: str,
    type: str,
    organization: Optional[str] = None,
    attrs: Optional[Mapping[str, Any]] = None
)

`property` ArtifactCollection.aliases

The aliases associated with the artifact collection.

`property` ArtifactCollection.created_at

The creation timestamp of the artifact collection.

`property` ArtifactCollection.description

A description of the artifact collection.

`property` ArtifactCollection.id

The unique identifier of the artifact collection.

`property` ArtifactCollection.name

The name of the artifact collection.

`property` ArtifactCollection.tags

The tags associated with the artifact collection.

`property` ArtifactCollection.type

Returns the type of the artifact collection.

`method` `ArtifactCollection.artifacts`

artifacts(per_page=50)

Get all artifact versions associated with this artifact collection.

`method` `ArtifactCollection.change_type`

change_type(new_type: str) → None

Deprecated, change type directly with save instead.

`method` `ArtifactCollection.delete`

delete()

Delete the entire artifact collection.

`method` `ArtifactCollection.is_sequence`

is_sequence() → bool

Return whether the artifact collection is a sequence.

`method` `ArtifactCollection.load`

load()

Load the artifact collection metadata.

`method` `ArtifactCollection.save`

save() → None

Persist any changes made to the artifact collection.

`class` `Artifacts`

An iterable collection of artifact versions associated with a project.

Optionally pass in filters to narrow down the results based on specific criteria.

Args:

client: The client instance to use for querying W&B.
entity: The entity (user or team) that owns the project.
project: The name of the project to query for artifacts.
collection_name: The name of the artifact collection to query.
type: The type of the artifacts to query. Common examples include “dataset” or “model”.
filters: Optional mapping of filters to apply to the query.
order: Optional string to specify the order of the results.
per_page: The number of artifact versions to fetch per page. Default is 50.
tags: Optional string or list of strings to filter artifacts by tags.

`method` `Artifacts.init`

__init__(
    client: wandb_gql.client.Client,
    entity: str,
    project: str,
    collection_name: str,
    type: str,
    filters: Optional[Mapping[str, Any]] = None,
    order: Optional[str] = None,
    per_page: int = 50,
    tags: Optional[str, List[str]] = None
)

`property` Artifacts.cursor

Returns the cursor position for pagination of file results.

`property` Artifacts.length

Returns the number of artifact versions.

`property` Artifacts.more

Returns True if there are more artifact versions to fetch.

`method` `Artifacts.convert_objects`

convert_objects()

Converts GraphQL edges to Artifact objects.

`class` `RunArtifacts`

An iterable collection of artifacts associated with a run.

Args:

client: The client instance to use for querying W&B.
run: The run object to query for artifacts.
mode: The mode of artifacts to fetch, either “logged” (output artifacts) or “used” (input artifacts). Default is “logged”.
per_page: The number of artifacts to fetch per page. Default is 50.

`method` `RunArtifacts.init`

__init__(
    client: wandb_gql.client.Client,
    run: 'Run',
    mode='logged',
    per_page: int = 50
)

`property` RunArtifacts.cursor

Returns the cursor position for pagination of file results.

`property` RunArtifacts.length

Returns the number of artifacts associated with the run.

`property` RunArtifacts.more

Returns True if there are more artifacts to fetch.

`method` `RunArtifacts.convert_objects`

convert_objects()

Converts GraphQL edges to Artifact objects.

`class` `ArtifactFiles`

An iterable collection of files associated with an artifact version.

Args:

client: The client instance to use for querying W&B.
artifact: The artifact object to query for files.
names: Optional sequence of file names to filter the results by. If None, all files will be returned.
per_page: The number of files to fetch per page. Default is 50.

`method` `ArtifactFiles.init`

__init__(
    client: wandb_gql.client.Client,
    artifact: 'wandb.Artifact',
    names: Optional[Sequence[str]] = None,
    per_page: int = 50
)

`property` ArtifactFiles.cursor

Returns the cursor position for pagination of file results.

`property` ArtifactFiles.length

Returns the number of files in the artifact.

`property` ArtifactFiles.more

Returns True if there are more files to fetch. Returns

`property` ArtifactFiles.path

Returns the path of the artifact.

The path is a list containingthe entity, project name, and artifact name.

`method` `ArtifactFiles.convert_objects`

convert_objects()

Converts GraphQL edges to File objects.

`method` `ArtifactFiles.update_variables`

update_variables()

Updates the variables dictionary with the cursor and limit.

1.3 - files

View the source code

`module` `wandb.apis.public`

W&B Public API for File Management.

This module provides classes for interacting with files stored in W&B. Classes include:

Files: A paginated collection of files associated with a run

Iterate through files with automatic pagination
Filter files by name
Access file metadata and properties
Download multiple files

File: A single file stored in W&B

Access file metadata (size, mimetype, URLs)
Download files to local storage
Delete files from W&B
Work with S3 URIs for direct access

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get files from a specific run
run = api.run("entity/project/run_id")
files = run.files()

# Work with files
for file in files:
    print(f"File: {file.name}")
    print(f"Size: {file.size} bytes")
    print(f"Type: {file.mimetype}")

    # Download file
    if file.size < 1000000:  # Less than 1MB
        file.download(root="./downloads")

    # Get S3 URI for large files
    if file.size >= 1000000:
        print(f"S3 URI: {file.path_uri}")

Note:

This module is part of the W&B Public API and provides methods to access, download, and manage files stored in W&B. Files are typically associated with specific runs and can include model weights, datasets, visualizations, and other artifacts.

`class` `Files`

An iterable collection of File objects.

Access and manage files uploaded to W&B during a run. Handles pagination automatically when iterating through large collections of files.

Args:

client: The run object that contains the files
run: The run object that contains the files
names (list, optional): A list of file names to filter the files
per_page (int, optional): The number of files to fetch per page
upload (bool, optional): If True, fetch the upload URL for each file

Example:

from wandb.apis.public.files import Files
from wandb.apis.public.api import Api

# Initialize the API client
api = Api()

# Example run object
run = api.run("entity/project/run-id")

# Create a Files object to iterate over files in the run
files = Files(api.client, run)

# Iterate over files
for file in files:
   print(file.name)
   print(file.url)
   print(file.size)

   # Download the file
   file.download(root="download_directory", replace=True)

`method` `Files.init`

__init__(client, run, names=None, per_page=50, upload=False)

`property` Files.cursor

Returns the cursor position for pagination of file results.

`property` Files.length

The number of files saved to the specified run.

`property` Files.more

Returns True if there are more files to fetch. Returns False if there are no more files to fetch.

`method` `Files.convert_objects`

convert_objects()

Converts GraphQL edges to File objects.

`method` `Files.update_variables`

update_variables()

Updates the GraphQL query variables for pagination.

`class` `File`

File saved to W&B.

Represents a single file stored in W&B. Includes access to file metadata. Files are associated with a specific run and can include text files, model weights, datasets, visualizations, and other artifacts. You can download the file, delete the file, and access file properties.

Specify one or more attributes in a dictionary to fine a specific file logged to a specific run. You can search using the following keys:

id (str): The ID of the run that contains the file
name (str): Name of the file
url (str): path to file
direct_url (str): path to file in the bucket
sizeBytes (int): size of file in bytes
md5 (str): md5 of file
mimetype (str): mimetype of file
updated_at (str): timestamp of last update
path_uri (str): path to file in the bucket, currently only available for files stored in S3

Args:

client: The run object that contains the file
attrs (dict): A dictionary of attributes that define the file
run: The run object that contains the file

Example:

from wandb.apis.public.files import File
from wandb.apis.public.api import Api

# Initialize the API client
api = Api()

# Example attributes dictionary
file_attrs = {
   "id": "file-id",
   "name": "example_file.txt",
   "url": "https://example.com/file",
   "direct_url": "https://example.com/direct_file",
   "sizeBytes": 1024,
   "mimetype": "text/plain",
   "updated_at": "2025-03-25T21:43:51Z",
   "md5": "d41d8cd98f00b204e9800998ecf8427e",
}

# Example run object
run = api.run("entity/project/run-id")

# Create a File object
file = File(api.client, file_attrs, run)

# Access some of the attributes
print("File ID:", file.id)
print("File Name:", file.name)
print("File URL:", file.url)
print("File MIME Type:", file.mimetype)
print("File Updated At:", file.updated_at)

# Access File properties
print("File Size:", file.size)
print("File Path URI:", file.path_uri)

# Download the file
file.download(root="download_directory", replace=True)

# Delete the file
file.delete()

`method` `File.init`

__init__(client, attrs, run=None)

`property` File.path_uri

Returns the URI path to the file in the storage bucket.

`property` File.size

Returns the size of the file in bytes.

`method` `File.delete`

delete()

Deletes the file from the W&B server.

`method` `File.download`

download(
    root: str = '.',
    replace: bool = False,
    exist_ok: bool = False,
    api: Optional[wandb.apis.public.api.Api] = None
) → TextIOWrapper

Downloads a file previously saved by a run from the wandb server.

Args:

root: Local directory to save the file. Defaults to “.”.
replace: If True, download will overwrite a local file if it exists. Defaults to False.
exist_ok: If True, will not raise ValueError if file already exists and will not re-download unless replace=True. Defaults to False.
api: If specified, the Api instance used to download the file.

Raises: ValueError if file already exists, replace=False and exist_ok=False.

1.4 - history

View the source code

`module` `wandb.apis.public`

W&B Public API for Run History.

This module provides classes for efficiently scanning and sampling run history data. Classes include:

HistoryScan: Iterator for scanning complete run history

Paginated access to all metrics
Configure step ranges and page sizes
Raw access to all logged data

SampledHistoryScan: Iterator for sampling run history data

Efficient access to downsampled metrics
Filter by specific keys
Control sample size and step ranges

Note:

This module is part of the W&B Public API and provides methods to access run history data. It handles pagination automatically and offers both complete and sampled access to metrics logged during training runs.

`class` `HistoryScan`

Iterator for scanning complete run history.

Args:

client: (wandb.apis.internal.Api) The client instance to use
run: (wandb.sdk.internal.Run) The run object to scan history for
min_step: (int) The minimum step to start scanning from
max_step: (int) The maximum step to scan up to
page_size: (int) Number of samples per page (default is 1000)

`method` `HistoryScan.init`

__init__(client, run, min_step, max_step, page_size=1000)

`class` `SampledHistoryScan`

Iterator for sampling run history data.

Args:

client: (wandb.apis.internal.Api) The client instance to use
run: (wandb.sdk.internal.Run) The run object to sample history from
keys: (list) List of keys to sample from the history
min_step: (int) The minimum step to start sampling from
max_step: (int) The maximum step to sample up to
page_size: (int) Number of samples per page (default is 1000)

`method` `SampledHistoryScan.init`

__init__(client, run, keys, min_step, max_step, page_size=1000)

1.5 - jobs

View the source code

`module` `wandb.apis.public`

W&B Public API for Job Management and Queuing.

This module provides classes for managing W&B jobs, queued runs, and run queues. Classes include:

Job: Manage W&B job definitions and execution

Load and configure jobs from artifacts
Set entrypoints and runtime configurations
Execute jobs with different resource types
Handle notebook and container-based jobs

QueuedRun: Track and manage individual queued runs

Monitor run state and execution
Wait for run completion
Access run results and artifacts
Delete queued runs

RunQueue: Manage job queues and execution resources

Create and configure run queues
Set resource types and configurations
Monitor queue items and status
Control queue access and priorities

`class` `Job`

`method` `Job.init`

__init__(api: 'Api', name, path: Optional[str] = None) → None

`property` Job.name

The name of the job.

`method` `Job.call`

call(
    config,
    project=None,
    entity=None,
    queue=None,
    resource='local-container',
    resource_args=None,
    template_variables=None,
    project_queue=None,
    priority=None
)

Call the job with the given configuration.

Args:

config (dict): The configuration to pass to the job. This should be a dictionary containing key-value pairs that match the input types defined in the job.
project (str, optional): The project to log the run to. Defaults to the job’s project.
entity (str, optional): The entity to log the run under. Defaults to the job’s entity.
queue (str, optional): The name of the queue to enqueue the job to. Defaults to None.
resource (str, optional): The resource type to use for execution. Defaults to “local-container”.
resource_args (dict, optional): Additional arguments for the resource type. Defaults to None.
template_variables (dict, optional): Template variables to use for the job. Defaults to None.
project_queue (str, optional): The project that manages the queue. Defaults to None.
priority (int, optional): The priority of the queued run. Defaults to None.

`method` `Job.set_entrypoint`

set_entrypoint(entrypoint: List[str])

Set the entrypoint for the job.

`class` `QueuedRun`

A single queued run associated with an entity and project.

Args:

entity: The entity associated with the queued run.
project (str): The project where runs executed by the queue are logged to.
queue_name (str): The name of the queue.
run_queue_item_id (int): The id of the run queue item.
project_queue (str): The project that manages the queue.
priority (str): The priority of the queued run.

Call run = queued_run.wait_until_running() or run = queued_run.wait_until_finished() to access the run.

`method` `QueuedRun.init`

__init__(
    client,
    entity,
    project,
    queue_name,
    run_queue_item_id,
    project_queue='model-registry',
    priority=None
)

`property` QueuedRun.entity

The entity associated with the queued run.

`property` QueuedRun.id

The id of the queued run.

`property` QueuedRun.project

The project associated with the queued run.

`property` QueuedRun.queue_name

The name of the queue.

`property` QueuedRun.state

The state of the queued run.

`method` `QueuedRun.delete`

delete(delete_artifacts=False)

Delete the given queued run from the wandb backend.

`method` `QueuedRun.wait_until_finished`

wait_until_finished()

Wait for the queued run to complete and return the finished run.

`method` `QueuedRun.wait_until_running`

wait_until_running()

Wait until the queued run is running and return the run.

`class` `RunQueue`

Class that represents a run queue in W&B.

Args:

client: W&B API client instance.
name: Name of the run queue
entity: The entity (user or team) that owns this queue
prioritization_mode: Queue priority mode Can be “DISABLED” or “V0”. Defaults to None.
_access: Access level for the queue Can be “project” or “user”. Defaults to None.
_default_resource_config_id: ID of default resource config
_default_resource_config: Default resource configuration

`method` `RunQueue.init`

__init__(
    client: 'RetryingClient',
    name: str,
    entity: str,
    prioritization_mode: Optional[Literal['DISABLED', 'V0']] = None,
    _access: Optional[Literal['project', 'user']] = None,
    _default_resource_config_id: Optional[int] = None,
    _default_resource_config: Optional[dict] = None
) → None

`property` RunQueue.access

The access level of the queue.

`property` RunQueue.default_resource_config

The default configuration for resources.

`property` RunQueue.entity

The entity that owns the queue.

`property` RunQueue.external_links

External resource links for the queue.

`property` RunQueue.id

The id of the queue.

`property` RunQueue.items

Up to the first 100 queued runs. Modifying this list will not modify the queue or any enqueued items!

`property` RunQueue.name

The name of the queue.

`property` RunQueue.prioritization_mode

The prioritization mode of the queue.

Can be set to “DISABLED” or “V0”.

`property` RunQueue.template_variables

Variables for resource templates.

`property` RunQueue.type

The resource type for execution.

`classmethod` `RunQueue.create`

create(
    name: str,
    resource: 'RunQueueResourceType',
    entity: Optional[str] = None,
    prioritization_mode: Optional[ForwardRef('RunQueuePrioritizationMode')] = None,
    config: Optional[dict] = None,
    template_variables: Optional[dict] = None
) → RunQueue

Create a RunQueue.

Args:

name: The name of the run queue to create.
resource: The resource type for execution.
entity: The entity (user or team) that will own the queue. Defaults to the default entity of the API client.
prioritization_mode: The prioritization mode for the queue. Can be “DISABLED” or “V0”. Defaults to None.
config: Optional dictionary for the default resource configuration. Defaults to None.
template_variables: Optional dictionary for template variables used in the resource configuration.

`method` `RunQueue.delete`

delete()

Delete the run queue from the wandb backend.

1.6 - projects

View the source code

`module` `wandb.apis.public`

W&B Public API for Projects.

This module provides classes for interacting with W&B projects and their associated data. Classes include:

Projects: A paginated collection of projects associated with an entity

Iterate through all projects
Access project metadata
Query project information

Project: A single project that serves as a namespace for runs

Access project properties
Work with artifacts and their types
Manage sweeps
Generate HTML representations for Jupyter

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get all projects for an entity
projects = api.projects("entity")

# Access project data
for project in projects:
    print(f"Project: {project.name}")
    print(f"URL: {project.url}")

    # Get artifact types
    for artifact_type in project.artifacts_types():
        print(f"Artifact Type: {artifact_type.name}")

    # Get sweeps
    for sweep in project.sweeps():
        print(f"Sweep ID: {sweep.id}")
        print(f"State: {sweep.state}")

Note:

This module is part of the W&B Public API and provides methods to access and manage projects. For creating new projects, use wandb.init() with a new project name.

`class` `Projects`

An iterable collection of Project objects.

An iterable interface to access projects created and saved by the entity.

Args:

client (wandb.apis.internal.Api): The API client instance to use.
entity (str): The entity name (username or team) to fetch projects for.
per_page (int): Number of projects to fetch per request (default is 50).

Example:

from wandb.apis.public.api import Api

# Initialize the API client
api = Api()

# Find projects that belong to this entity
projects = api.projects(entity="entity")

# Iterate over files
for project in projects:
   print(f"Project: {project.name}")
   print(f"- URL: {project.url}")
   print(f"- Created at: {project.created_at}")
   print(f"- Is benchmark: {project.is_benchmark}")

`method` `Projects.init`

__init__(client, entity, per_page=50)

`property` Projects.cursor

Returns the cursor position for pagination of project results.

`property` Projects.length

Returns the total number of projects.

Note: This property is not available for projects.

`property` Projects.more

Returns True if there are more projects to fetch. Returns False if there are no more projects to fetch.

`method` `Projects.convert_objects`

convert_objects()

Converts GraphQL edges to File objects.

`class` `Project`

A project is a namespace for runs.

Args:

client: W&B API client instance.
name (str): The name of the project.
entity (str): The entity name that owns the project.

`method` `Project.init`

__init__(client, entity, project, attrs)

`property` Project.path

Returns the path of the project. The path is a list containing the entity and project name.

`property` Project.url

Returns the URL of the project.

`method` `Project.artifacts_types`

artifacts_types(per_page=50)

Returns all artifact types associated with this project.

`method` `Project.sweeps`

sweeps()

Fetches all sweeps associated with the project.

`method` `Project.to_html`

to_html(height=420, hidden=False)

Generate HTML containing an iframe displaying this project.

1.7 - query_generator

View the source code

`module` `wandb.apis.public`

`method` `QueryGenerator.filter_to_mongo`

filter_to_mongo(filter)

Returns dictionary with filter format converted to MongoDB filter.

`classmethod` `QueryGenerator.format_order_key`

format_order_key(key: str)

Format a key for sorting.

`method` `QueryGenerator.key_to_server_path`

key_to_server_path(key)

Convert a key dictionary to the corresponding server path string.

`method` `QueryGenerator.keys_to_order`

keys_to_order(keys)

Convert a list of key dictionaries to an order string.

`method` `QueryGenerator.mongo_to_filter`

mongo_to_filter(filter)

Returns dictionary with MongoDB filter converted to filter format.

`method` `QueryGenerator.order_to_keys`

order_to_keys(order)

Convert an order string to a list of key dictionaries.

`method` `QueryGenerator.server_path_to_key`

server_path_to_key(path)

Convert a server path string to the corresponding key dictionary.

1.8 - reports

View the source code

`module` `wandb.apis.public`

Public API: reports.

`class` `Reports`

Reports is an iterable collection of BetaReport objects.

Args:

client (wandb.apis.internal.Api): The API client instance to use.
project (wandb.sdk.internal.Project): The project to fetch reports from.
name (str, optional): The name of the report to filter by. If None, fetches all reports.
entity (str, optional): The entity name for the project. Defaults to the project entity.
per_page (int): Number of reports to fetch per page (default is 50).

`method` `Reports.init`

__init__(client, project, name=None, entity=None, per_page=50)

`property` Reports.cursor

Returns the cursor position for pagination of file results.

`property` Reports.length

The number of reports in the project.

`property` Reports.more

Returns True if there are more files to fetch. Returns False if there are no more files to fetch.

`method` `Reports.convert_objects`

convert_objects()

Converts GraphQL edges to File objects.

`method` `Reports.update_variables`

update_variables()

Updates the GraphQL query variables for pagination.

`class` `BetaReport`

BetaReport is a class associated with reports created in wandb.

WARNING: this API will likely change in a future release

Attributes:

name (string): report name
description (string): report description
user (User): the user that created the report
spec (dict): the spec off the report
updated_at (string): timestamp of last update

`method` `BetaReport.init`

__init__(client, attrs, entity=None, project=None)

`property` BetaReport.sections

Get the panel sections (groups) from the report.

`property` BetaReport.updated_at

Timestamp of last update

`property` BetaReport.url

URL of the report.

Contains the entity, project, display name, and id.

`method` `BetaReport.runs`

runs(section, per_page=50, only_selected=True)

Get runs associated with a section of the report.

`method` `BetaReport.to_html`

to_html(height=1024, hidden=False)

Generate HTML containing an iframe displaying this report.

1.9 - runs

View the source code

`module` `wandb.apis.public`

W&B Public API for ML Runs.

This module provides classes for interacting with W&B runs and their associated data. Classes include:

Runs: A paginated collection of runs associated with a project

Filter and query runs
Access run histories and metrics
Export data in various formats (pandas, polars)

Run: A single machine learning training run

Access run metadata, configs, and metrics
Upload and download files
Work with artifacts
Query run history
Update run information

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get runs matching filters
runs = api.runs(
    path="entity/project", filters={"state": "finished", "config.batch_size": 32}
)

# Access run data
for run in runs:
    print(f"Run: {run.name}")
    print(f"Config: {run.config}")
    print(f"Metrics: {run.summary}")

    # Get history with pandas
    history_df = run.history(keys=["loss", "accuracy"], pandas=True)

    # Work with artifacts
    for artifact in run.logged_artifacts():
        print(f"Artifact: {artifact.name}")

Note:

This module is part of the W&B Public API and provides read/write access to run data. For logging new runs, use the wandb.init() function from the main wandb package.

`class` `Runs`

An iterable collection of runs associated with a project and optional filter.

This is generally used indirectly using the Api.runs namespace.

Args:

client: (wandb.apis.public.RetryingClient) The API client to use for requests.
entity: (str) The entity (username or team) that owns the project.
project: (str) The name of the project to fetch runs from.
filters: (Optional[Dict[str, Any]]) A dictionary of filters to apply to the runs query.
order: (Optional[str]) The order of the runs, can be “asc” or “desc” Defaults to “desc”.
per_page: (int) The number of runs to fetch per request (default is 50).
include_sweeps: (bool) Whether to include sweep information in the runs. Defaults to True.

Examples:

from wandb.apis.public.runs import Runs
from wandb.apis.public import Api

# Initialize the API client
api = Api()

# Get all runs from a project that satisfy the filters
filters = {"state": "finished", "config.optimizer": "adam"}

runs = Runs(
   client=api.client,
   entity="entity",
   project="project_name",
   filters=filters,
)

# Iterate over runs and print details
for run in runs:
   print(f"Run name: {run.name}")
   print(f"Run ID: {run.id}")
   print(f"Run URL: {run.url}")
   print(f"Run state: {run.state}")
   print(f"Run config: {run.config}")
   print(f"Run summary: {run.summary}")
   print(f"Run history (samples=5): {run.history(samples=5)}")
   print("----------")

# Get histories for all runs with specific metrics
histories_df = runs.histories(
   samples=100,  # Number of samples per run
   keys=["loss", "accuracy"],  # Metrics to fetch
   x_axis="_step",  # X-axis metric
   format="pandas",  # Return as pandas DataFrame
)

`method` `Runs.init`

__init__(
    client: 'RetryingClient',
    entity: str,
    project: str,
    filters: Optional[Dict[str, Any]] = None,
    order: Optional[str] = None,
    per_page: int = 50,
    include_sweeps: bool = True
)

`property` Runs.cursor

Returns the cursor position for pagination of runs results.

`property` Runs.length

Returns the total number of runs.

`property` Runs.more

Returns True if there are more runs to fetch. Returns False if there are no more runs to fetch.

`method` `Runs.convert_objects`

convert_objects()

Converts GraphQL edges to Runs objects.

`method` `Runs.histories`

histories(
    samples: int = 500,
    keys: Optional[List[str]] = None,
    x_axis: str = '_step',
    format: Literal['default', 'pandas', 'polars'] = 'default',
    stream: Literal['default', 'system'] = 'default'
)

Return sampled history metrics for all runs that fit the filters conditions.

Args:

samples: The number of samples to return per run
keys: Only return metrics for specific keys
x_axis: Use this metric as the xAxis defaults to _step
format: Format to return data in, options are “default”, “pandas”, “polars”
stream: “default” for metrics, “system” for machine metrics

Returns:

pandas.DataFrame: If format="pandas", returns a pandas.DataFrame of history metrics.
polars.DataFrame: If format="polars", returns a polars.DataFrame of history metrics.
list of dicts: If format="default", returns a list of dicts containing history metrics with a run_id key.

`class` `Run`

A single run associated with an entity and project.

Args:

client: The W&B API client.
entity: The entity associated with the run.
project: The project associated with the run.
run_id: The unique identifier for the run.
attrs: The attributes of the run.
include_sweeps: Whether to include sweeps in the run.

Attributes:

tags ([str]): a list of tags associated with the run
url (str): the url of this run
id (str): unique identifier for the run (defaults to eight characters)
name (str): the name of the run
state (str): one of: running, finished, crashed, killed, preempting, preempted
config (dict): a dict of hyperparameters associated with the run
created_at (str): ISO timestamp when the run was started
system_metrics (dict): the latest system metrics recorded for the run
summary (dict): A mutable dict-like property that holds the current summary. Calling update will persist any changes.
project (str): the project associated with the run
entity (str): the name of the entity associated with the run
project_internal_id (int): the internal id of the project
user (str): the name of the user who created the run
path (str): Unique identifier [entity]/[project]/[run_id]
notes (str): Notes about the run
read_only (boolean): Whether the run is editable
history_keys (str): Keys of the history metrics that have been logged
with wandb.log({key: value})
metadata (str): Metadata about the run from wandb-metadata.json

`method` `Run.init`

__init__(
    client: 'RetryingClient',
    entity: str,
    project: str,
    run_id: str,
    attrs: Optional[Mapping] = None,
    include_sweeps: bool = True
)

Initialize a Run object.

Run is always initialized by calling api.runs() where api is an instance of wandb.Api.

`property` Run.entity

The entity associated with the run.

`property` Run.id

The unique identifier for the run.

`property` Run.json_config

`property` Run.lastHistoryStep

Returns the last step logged in the run’s history.

`property` Run.metadata

Metadata about the run from wandb-metadata.json.

Metadata includes the run’s description, tags, start time, memory usage and more.

`property` Run.name

The name of the run.

`property` Run.path

The path of the run. The path is a list containing the entity, project, and run_id.

`property` Run.state

The state of the run. Can be one of: Finished, Failed, Crashed, or Running.

`property` Run.storage_id

The unique storage identifier for the run.

`property` Run.summary

A mutable dict-like property that holds summary values associated with the run.

`property` Run.url

The URL of the run.

The run URL is generated from the entity, project, and run_id. For SaaS users, it takes the form of https://wandb.ai/entity/project/run_id.

`property` Run.username

This API is deprecated. Use entity instead.

`classmethod` `Run.create`

create(api, run_id=None, project=None, entity=None)

Create a run for the given project.

`method` `Run.delete`

delete(delete_artifacts=False)

Delete the given run from the wandb backend.

Args:

delete_artifacts (bool, optional): Whether to delete the artifacts associated with the run.

`method` `Run.file`

file(name)

Return the path of a file with a given name in the artifact.

Args:

name (str): name of requested file.

Returns: A File matching the name argument.

`method` `Run.files`

files(names=None, per_page=50)

Return a file path for each file named.

Args:

names (list): names of the requested files, if empty returns all files
per_page (int): number of results per page.

Returns: A Files object, which is an iterator over File objects.

`method` `Run.history`

history(samples=500, keys=None, x_axis='_step', pandas=True, stream='default')

Return sampled history metrics for a run.

This is simpler and faster if you are ok with the history records being sampled.

Args:

samples : (int, optional) The number of samples to return
pandas : (bool, optional) Return a pandas dataframe
keys : (list, optional) Only return metrics for specific keys
x_axis : (str, optional) Use this metric as the xAxis defaults to _step
stream : (str, optional) “default” for metrics, “system” for machine metrics

Returns:

pandas.DataFrame: If pandas=True returns a pandas.DataFrame of history metrics.
list of dicts: If pandas=False returns a list of dicts of history metrics.

`method` `Run.load`

load(force=False)

Fetch and update run data from GraphQL database.

Ensures run data is up to date.

Args:

force (bool): Whether to force a refresh of the run data.

`method` `Run.log_artifact`

log_artifact(
    artifact: 'wandb.Artifact',
    aliases: Optional[Collection[str]] = None,
    tags: Optional[Collection[str]] = None
)

Declare an artifact as output of a run.

Args:

artifact (Artifact): An artifact returned from wandb.Api().artifact(name).
aliases (list, optional): Aliases to apply to this artifact.
tags: (list, optional) Tags to apply to this artifact, if any.

Returns: A Artifact object.

`method` `Run.logged_artifacts`

logged_artifacts(per_page: int = 100) → RunArtifacts

Fetches all artifacts logged by this run.

Retrieves all output artifacts that were logged during the run. Returns a paginated result that can be iterated over or collected into a single list.

Args:

per_page: Number of artifacts to fetch per API request.

Returns: An iterable collection of all Artifact objects logged as outputs during this run.

Example:

import wandb
import tempfile

with tempfile.NamedTemporaryFile(mode="w", delete=False, suffix=".txt") as tmp:
   tmp.write("This is a test artifact")
   tmp_path = tmp.name
run = wandb.init(project="artifact-example")
artifact = wandb.Artifact("test_artifact", type="dataset")
artifact.add_file(tmp_path)
run.log_artifact(artifact)
run.finish()

api = wandb.Api()

finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")

for logged_artifact in finished_run.logged_artifacts():
   print(logged_artifact.name)

`method` `Run.save`

save()

Persist changes to the run object to the W&B backend.

`method` `Run.scan_history`

scan_history(keys=None, page_size=1000, min_step=None, max_step=None)

Returns an iterable collection of all history records for a run.

Args:

keys ([str], optional): only fetch these keys, and only fetch rows that have all of keys defined.
page_size (int, optional): size of pages to fetch from the api.
min_step (int, optional): the minimum number of pages to scan at a time.
max_step (int, optional): the maximum number of pages to scan at a time.

Returns: An iterable collection over history records (dict).

Example: Export all the loss values for an example run

run = api.run("entity/project-name/run-id")
history = run.scan_history(keys=["Loss"])
losses = [row["Loss"] for row in history]

`method` `Run.to_html`

to_html(height=420, hidden=False)

Generate HTML containing an iframe displaying this run.

`method` `Run.update`

update()

Persist changes to the run object to the wandb backend.

`method` `Run.upload_file`

upload_file(path, root='.')

Uploads a local file to W&B, associating it with this run.

Args:

path (str): Path to the file to upload. Can be absolute or relative.
root (str): The root path to save the file relative to. For example, if you want to have the file saved in the run as “my_dir/file.txt” and you’re currently in “my_dir” you would set root to “../”. Defaults to current directory (".").

Returns: A File object representing the uploaded file.

`method` `Run.use_artifact`

use_artifact(artifact, use_as=None)

Declare an artifact as an input to a run.

Args:

artifact (Artifact): An artifact returned from wandb.Api().artifact(name)
use_as (string, optional): A string identifying how the artifact is used in the script. Used to easily differentiate artifacts used in a run, when using the beta wandb launch feature’s artifact swapping functionality.

Returns: A Artifact object.

`method` `Run.used_artifacts`

used_artifacts(per_page: int = 100) → RunArtifacts

Fetches artifacts explicitly used by this run.

Retrieves only the input artifacts that were explicitly declared as used during the run, typically via run.use_artifact(). Returns a paginated result that can be iterated over or collected into a single list.

Args:

per_page: Number of artifacts to fetch per API request.

Returns: An iterable collection of Artifact objects explicitly used as inputs in this run.

Example:

import wandb

run = wandb.init(project="artifact-example")
run.use_artifact("test_artifact:latest")
run.finish()

api = wandb.Api()
finished_run = api.run(f"{run.entity}/{run.project}/{run.id}")
for used_artifact in finished_run.used_artifacts():
   print(used_artifact.name)
test_artifact

`method` `Run.wait_until_finished`

wait_until_finished()

Check the state of the run until it is finished.

1.10 - sweeps

View the source code

`module` `wandb.apis.public`

W&B Public API for Hyperparameter Sweeps.

This module provides classes for interacting with W&B hyperparameter optimization sweeps. Classes include:

Sweep: Represents a hyperparameter optimization sweep, providing access to:

Sweep configuration and state
Associated runs and their metrics
Best performing runs
URLs for visualization

Example:

from wandb.apis.public import Api

# Initialize API
api = Api()

# Get a specific sweep
sweep = api.sweep("entity/project/sweep_id")

# Access sweep properties
print(f"Sweep: {sweep.name}")
print(f"State: {sweep.state}")
print(f"Best Loss: {sweep.best_loss}")

# Get best performing run
best_run = sweep.best_run()
print(f"Best Run: {best_run.name}")
print(f"Metrics: {best_run.summary}")

Note:

This module is part of the W&B Public API and provides read-only access to sweep data. For creating and controlling sweeps, use the wandb.sweep() and wandb.agent() functions from the main wandb package.

`class` `Sweep`

The set of runs associated with the sweep.

Attributes:

runs (Runs): List of runs
id (str): Sweep ID
project (str): The name of the project the sweep belongs to
config (dict): Dictionary containing the sweep configuration
state (str): The state of the sweep. Can be “Finished”, “Failed”, “Crashed”, or “Running”.
expected_run_count (int): The number of expected runs for the sweep

`method` `Sweep.init`

__init__(client, entity, project, sweep_id, attrs=None)

`property` Sweep.config

The sweep configuration used for the sweep.

`property` Sweep.entity

The entity associated with the sweep.

`property` Sweep.expected_run_count

Return the number of expected runs in the sweep or None for infinite runs.

`property` Sweep.name

The name of the sweep.

If the sweep has a name, it will be returned. Otherwise, the sweep ID will be returned.

`property` Sweep.order

Return the order key for the sweep.

`property` Sweep.path

Returns the path of the project.

The path is a list containing the entity, project name, and sweep ID.

`property` Sweep.url

The URL of the sweep.

The sweep URL is generated from the entity, project, the term “sweeps”, and the sweep ID.run_id. For SaaS users, it takes the form of https://wandb.ai/entity/project/sweeps/sweeps_ID.

`property` Sweep.username

Note: Deprecated. Use entity instead.

`method` `Sweep.best_run`

best_run(order=None)

Return the best run sorted by the metric defined in config or the order passed in.

`classmethod` `Sweep.get`

get(
    client,
    entity=None,
    project=None,
    sid=None,
    order=None,
    query=None,
    **kwargs
)

Execute a query against the cloud backend.

`method` `Sweep.load`

load(force: bool = False)

Fetch and update sweep data logged to the run from GraphQL database.

`method` `Sweep.to_html`

to_html(height=420, hidden=False)

Generate HTML containing an iframe displaying this sweep.

1.11 - teams

View the source code

`module` `wandb.apis.public`

W&B Public API for managing teams and team members.

This module provides classes for managing W&B teams and their members. Classes include:

Team: Manage W&B teams and their settings

Create new teams
Invite team members
Create service accounts
Manage team permissions and settings

Member: Represent and manage team members

Access member information
Delete members
Manage member permissions

Note:

This module is part of the W&B Public API and provides methods to manage teams and their members. Team management operations require appropriate permissions.

`class` `Member`

A member of a team.

Args:

client (wandb.apis.internal.Api): The client instance to use
team (str): The name of the team this member belongs to
attrs (dict): The member attributes

`method` `Member.init`

__init__(client, team, attrs)

`method` `Member.delete`

delete()

Remove a member from a team.

Returns: Boolean indicating success

`class` `Team`

A class that represents a W&B team.

This class provides methods to manage W&B teams, including creating teams, inviting members, and managing service accounts. It inherits from Attrs to handle team attributes.

Args:

client (wandb.apis.public.Api): The api instance to use
name (str): The name of the team
attrs (dict): Optional dictionary of team attributes

Note:

Team management requires appropriate permissions.

`method` `Team.init`

__init__(client, name, attrs=None)

`classmethod` `Team.create`

create(api, team, admin_username=None)

Create a new team.

Args:

api: (Api) The api instance to use
team: (str) The name of the team
admin_username: (str) optional username of the admin user of the team, defaults to the current user.

Returns: A Team object

`method` `Team.create_service_account`

create_service_account(description)

Create a service account for the team.

Args:

description: (str) A description for this service account

Returns: The service account Member object, or None on failure

`method` `Team.invite`

invite(username_or_email, admin=False)

Invite a user to a team.

Args:

username_or_email: (str) The username or email address of the user you want to invite
admin: (bool) Whether to make this user a team admin, defaults to False

Returns: True on success, False if user was already invited or didn’t exist

`method` `Team.load`

load(force=False)

Return members that belong to a team.

1.12 - users

View the source code

`module` `wandb.apis.public`

W&B Public API for User Management.

This module provides classes for managing W&B users and their API keys. Classes include:

User: Manage W&B user accounts and authentication

Create new users
Generate and manage API keys
Access team memberships
Handle user properties and permissions

Note:

This module is part of the W&B Public API and provides methods to manage users and their authentication. Some operations require admin privileges.

`class` `User`

A class representing a W&B user with authentication and management capabilities.

This class provides methods to manage W&B users, including creating users, managing API keys, and accessing team memberships. It inherits from Attrs to handle user attributes.

Args:

client: (wandb.apis.internal.Api) The client instance to use
attrs: (dict) The user attributes

Note:

Some operations require admin privileges

`method` `User.init`

__init__(client, attrs)

`property` User.api_keys

List of API key names associated with the user.

Returns:

list[str]: Names of API keys associated with the user. Empty list if user has no API keys or if API key data hasn’t been loaded.

`property` User.teams

List of team names that the user is a member of.

Returns:

list (list): Names of teams the user belongs to. Empty list if user has no team memberships or if teams data hasn’t been loaded.

`property` User.user_api

An instance of the api using credentials from the user.

`classmethod` `User.create`

create(api, email, admin=False)

Create a new user.

Args:

api (Api): The api instance to use
email (str): The name of the team
admin (bool): Whether this user should be a global instance admin

Returns: A User object

`method` `User.delete_api_key`

delete_api_key(api_key)

Delete a user’s api key.

Args:

api_key (str): The name of the API key to delete. This should be one of the names returned by the api_keys property.

Returns: Boolean indicating success

Raises: ValueError if the api_key couldn’t be found

`method` `User.generate_api_key`

generate_api_key(description=None)

Generate a new api key.

Args:

description (str, optional): A description for the new API key. This can be used to identify the purpose of the API key.

Returns: The new api key, or None on failure

2 - Data Types

Defines Data Types for logging interactive visualizations to W&B.

2.1 - Audio

View the source code

`class` `Audio`

W&B class for audio clips.

Attributes:

data_or_path (string or numpy array): A path to an audio file or a numpy array of audio data.
sample_rate (int): Sample rate, required when passing in raw numpy array of audio data.
caption (string): Caption to display with audio.

`method` `Audio.init`

__init__(data_or_path, sample_rate=None, caption=None)

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

2.2 - box3d

View the source code

`function` `box3d`

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

Returns a Box3D.

Args:

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

2.3 - Histogram

View the source code

`class` `Histogram`

W&B class for histograms.

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

Args:

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

Attributes:

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

Examples: Generate histogram from a sequence.

import wandb

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

Efficiently initialize from np.histogram.

import numpy as np
import wandb

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

`method` `Histogram.init`

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

2.4 - Html

View the source code

`class` `Html`

W&B class for arbitrary html.

Args:

data: HTML to display in wandb
inject: Add a stylesheet to the HTML object. If set to False the HTML will pass through unchanged.

`method` `Html.init`

__init__(data: Union[str, ForwardRef('TextIO')], inject: bool = True) → None

2.5 - Image

View the source code

`class` `Image`

Format images for logging to W&B.

See https://pillow.readthedocs.io/en/stable/handbook/concepts.html#modes for more information on modes.

Args:

data_or_path: Accepts numpy array of image data, or a PIL image. The class attempts to infer the data format and converts it.
mode: The PIL mode for an image. Most common are “L”, “RGB”, “RGBA”.
caption: Label for display of image.

When logging a torch.Tensor as a wandb.Image, images are normalized. If you do not want to normalize your images, convert your tensors to a PIL Image.

Examples:

# Create a wandb.Image from a numpy array
import numpy as np
import wandb

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

# Create a wandb.Image from a PILImage
import numpy as np
from PIL import Image as PILImage
import wandb

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

# log .jpg rather than .png (default)
import numpy as np
import wandb

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

`method` `Image.init`

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

`method` `Image.guess_mode`

guess_mode(
    data: Union[ForwardRef('np.ndarray'), ForwardRef('torch.Tensor')],
    file_type: Optional[str] = None
) → str

Guess what type of image the np.array is representing.

2.6 - Molecule

View the source code

`class` `Molecule`

W&B class for 3D Molecular data.

Args:

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

`method` `Molecule.init`

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

2.7 - Object3D

View the source code

`class` `Object3D`

W&B class for 3D point clouds.

Args:

data_or_path: Object3D can be initialized from a file or a NumPy array. You can pass a path to a file or an io object and a file_type which must be one of SUPPORTED_TYPES.

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

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

`method` `Object3D.init`

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

2.8 - Plotly

View the source code

`class` `Plotly`

W&B class for Plotly plots.

Args:

val: Matplotlib or Plotly figure.

`method` `Plotly.init`

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

`classmethod` `Plotly.get_media_subdir`

get_media_subdir() → str

`classmethod` `Plotly.make_plot_media`

make_plot_media(
    val: Union[ForwardRef('plotly.Figure'), ForwardRef('matplotlib.artist.Artist')]
) → Union[wandb.sdk.data_types.image.Image, ForwardRef('Plotly')]

`method` `Plotly.to_json`

to_json(
    run_or_artifact: Union[ForwardRef('LocalRun'), ForwardRef('Artifact')]
) → dict

2.9 - Table

View the source code

`class` `Table`

The Table class used to display and analyze tabular data.

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

This class is the primary class used to generate the Table Visualizer in the UI: https://docs.wandb.ai/guides/data-vis/tables.

Attributes:

columns (List[str]): Names of the columns in the table. Defaults to [“Input”, “Output”, “Expected”].
data: (List[List[any]]) 2D row-oriented array of values.
dataframe (pandas.DataFrame): DataFrame object used to create the table. When set, data and columns arguments are ignored.
optional (Union[bool,List[bool]]): Determines if None values are allowed. Default to True. - If a singular bool value, then the optionality is enforced for all columns specified at construction time. - If a list of bool values, then the optionality is applied to each column - should be the same length as columns. applies to all columns. A list of bool values applies to each respective column.
allow_mixed_types (bool): Determines if columns are allowed to have mixed types (disables type validation). Defaults to False.

`method` `Table.init`

__init__(
    columns=None,
    data=None,
    rows=None,
    dataframe=None,
    dtype=None,
    optional=True,
    allow_mixed_types=False
)

Initializes a Table object.

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

`method` `Table.add_column`

add_column(name, data, optional=False)

Adds a column of data to the table.

Args:

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

`method` `Table.add_computed_columns`

add_computed_columns(fn)

Adds one or more computed columns based on existing data.

Args:

fn: A function which accepts one or two parameters, ndx (int) and row (dict), which is expected to return a dict representing new columns for that row, keyed by the new column names.

ndx is an integer representing the index of the row. Only included if include_ndx is set to True.

row is a dictionary keyed by existing columns

`method` `Table.add_data`

add_data(*data)

Adds a new row of data to the table.

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

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

`method` `Table.add_row`

add_row(*row)

Deprecated; use add_data instead.

`method` `Table.cast`

cast(col_name, dtype, optional=False)

Casts a column to a specific data type.

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

Args:

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

`method` `Table.get_column`

get_column(name, convert_to=None)

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

Args:

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

`method` `Table.get_dataframe`

get_dataframe()

Returns a pandas.DataFrame of the table.

`method` `Table.get_index`

get_index()

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

2.10 - Video

View the source code

`class` `Video`

Format a video for logging to W&B.

Args:

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

Examples: Log a numpy array as a video

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)})

`method` `Video.init`

__init__(
    data_or_path: Union[ForwardRef('np.ndarray'), str, ForwardRef('TextIO'), ForwardRef('BytesIO')],
    caption: Optional[str] = None,
    fps: Optional[int] = None,
    format: Optional[str] = None
)

3 - Launch Library

A collection of launch APIs for W&B.

3.1 - create_and_run_agent

View the source code

`function` `create_and_run_agent`

create_and_run_agent(
    api: wandb.apis.internal.Api,
    config: Dict[str, Any]
) → None

3.2 - launch

View the source code

`function` `launch`

launch(
    api: wandb.apis.internal.Api,
    job: Optional[str] = None,
    entry_point: Optional[List[str]] = None,
    version: Optional[str] = None,
    name: Optional[str] = None,
    resource: Optional[str] = None,
    resource_args: Optional[Dict[str, Any]] = None,
    project: Optional[str] = None,
    entity: Optional[str] = None,
    docker_image: Optional[str] = None,
    config: Optional[Dict[str, Any]] = None,
    synchronous: Optional[bool] = True,
    run_id: Optional[str] = None,
    repository: Optional[str] = None
) → AbstractRun

Launch a W&B launch experiment.

Arguments:

job: string reference to a wandb.Job eg: wandb/test/my-job:latest
api: An instance of a wandb Api from wandb.apis.internal.
entry_point: Entry point to run within the project. Defaults to using the entry point used in the original run for wandb URIs, or main.py for git repository URIs.
version: For Git-based projects, either a commit hash or a branch name.
name: Name run under which to launch the run.
resource: Execution backend for the run.
resource_args: Resource related arguments for launching runs onto a remote backend. Will be stored on the constructed launch config under resource_args.
project: Target project to send launched run to
entity: Target entity to send launched run to
config: A dictionary containing the configuration for the run. May also contain resource specific arguments under the key “resource_args”.
synchronous: Whether to block while waiting for a run to complete. Defaults to True. Note that if synchronous is False and backend is “local-container”, this method will return, but the current process will block when exiting until the local run completes. If the current process is interrupted, any asynchronous runs launched via this method will be terminated. If synchronous is True and the run fails, the current process will error out as well.
run_id: ID for the run (To ultimately replace the :name: field)
repository: string name of repository path for remote registry

Example:

   from wandb.sdk.launch import launch

   job = "wandb/jobs/Hello World:latest"
   params = {"epochs": 5}
   # Run W&B project and create a reproducible docker environment
   # on a local host
   api = wandb.apis.internal.Api()
   launch(api, job, parameters=params)
   ``` 





**Returns:**
an instance of`wandb.launch.SubmittedRun` exposing information (e.g. run ID) about the launched run. 



**Raises:**
`wandb.exceptions.ExecutionError` If a run launched in blocking mode is unsuccessful.

3.3 - launch_add

View the source code

`function` `launch_add`

launch_add(
    uri: Optional[str] = None,
    job: Optional[str] = None,
    config: Optional[Dict[str, Any]] = None,
    template_variables: Optional[Dict[str, Union[float, int, str]]] = None,
    project: Optional[str] = None,
    entity: Optional[str] = None,
    queue_name: Optional[str] = None,
    resource: Optional[str] = None,
    entry_point: Optional[List[str]] = None,
    name: Optional[str] = None,
    version: Optional[str] = None,
    docker_image: Optional[str] = None,
    project_queue: Optional[str] = None,
    resource_args: Optional[Dict[str, Any]] = None,
    run_id: Optional[str] = None,
    build: Optional[bool] = False,
    repository: Optional[str] = None,
    sweep_id: Optional[str] = None,
    author: Optional[str] = None,
    priority: Optional[int] = None
) → public.QueuedRun

Enqueue a W&B launch experiment. With either a source uri, job or docker_image.

Arguments:

uri: URI of experiment to run. A wandb run uri or a Git repository URI.
job: string reference to a wandb.Job eg: wandb/test/my-job:latest
config: A dictionary containing the configuration for the run. May also contain resource specific arguments under the key “resource_args”
template_variables: A dictionary containing values of template variables for a run queue.
Expected format of {“VAR_NAME”: VAR_VALUE}
project: Target project to send launched run to
entity: Target entity to send launched run to
queue: the name of the queue to enqueue the run to
priority: the priority level of the job, where 1 is the highest priority
resource: Execution backend for the run: W&B provides built-in support for “local-container” backend
entry_point: Entry point to run within the project. Defaults to using the entry point used in the original run for wandb URIs, or main.py for git repository URIs.
name: Name run under which to launch the run.
version: For Git-based projects, either a commit hash or a branch name.
docker_image: The name of the docker image to use for the run.
resource_args: Resource related arguments for launching runs onto a remote backend. Will be stored on the constructed launch config under resource_args.
run_id: optional string indicating the id of the launched run
build: optional flag defaulting to false, requires queue to be set if build, an image is created, creates a job artifact, pushes a reference to that job artifact to queue
repository: optional string to control the name of the remote repository, used when pushing images to a registry
project_queue: optional string to control the name of the project for the queue. Primarily used for back compatibility with project scoped queues

Example:

from wandb.sdk.launch import launch_add

project_uri = "https://github.com/wandb/examples"
params = {"alpha": 0.5, "l1_ratio": 0.01}
# Run W&B project and create a reproducible docker environment
# on a local host
api = wandb.apis.internal.Api()
launch_add(uri=project_uri, parameters=params)

Returns: an instance ofwandb.api.public.QueuedRun which gives information about the queued run, or if wait_until_started or wait_until_finished are called, gives access to the underlying Run information.

Raises: wandb.exceptions.LaunchError if unsuccessful

3.4 - LaunchAgent

View the source code

`class` `LaunchAgent`

Launch agent class which polls run given run queues and launches runs for wandb launch.

`method` `LaunchAgent.init`

__init__(api: wandb.apis.internal.Api, config: Dict[str, Any])

Initialize a launch agent.

Arguments:

api: Api object to use for making requests to the backend.
config: Config dictionary for the agent.

`property` LaunchAgent.num_running_jobs

Return the number of jobs not including schedulers.

`property` LaunchAgent.num_running_schedulers

Return just the number of schedulers.

`property` LaunchAgent.thread_ids

Returns a list of keys running thread ids for the agent.

`method` `LaunchAgent.check_sweep_state`

check_sweep_state(
    launch_spec: Dict[str, Any],
    api: wandb.apis.internal.Api
) → None

Check the state of a sweep before launching a run for the sweep.

`method` `LaunchAgent.fail_run_queue_item`

fail_run_queue_item(
    run_queue_item_id: str,
    message: str,
    phase: str,
    files: Optional[List[str]] = None
) → None

`method` `LaunchAgent.finish_thread_id`

finish_thread_id(
    thread_id: int,
    exception: Optional[Exception, wandb.sdk.launch.errors.LaunchDockerError] = None
) → None

Removes the job from our list for now.

`method` `LaunchAgent.get_job_and_queue`

get_job_and_queue() → Optional[wandb.sdk.launch.agent.agent.JobSpecAndQueue]

`classmethod` `LaunchAgent.initialized`

initialized() → bool

Return whether the agent is initialized.

`method` `LaunchAgent.loop`

loop() → None

Loop infinitely to poll for jobs and run them.

Raises:

KeyboardInterrupt: if the agent is requested to stop.

`classmethod` `LaunchAgent.name`

name() → str

Return the name of the agent.

`method` `LaunchAgent.pop_from_queue`

pop_from_queue(queue: str) → Any

Pops an item off the runqueue to run as a job.

Arguments:

queue: Queue to pop from.

Returns: Item popped off the queue.

Raises:

Exception: if there is an error popping from the queue.

`method` `LaunchAgent.print_status`

print_status() → None

Prints the current status of the agent.

`method` `LaunchAgent.run_job`

run_job(
    job: Dict[str, Any],
    queue: str,
    file_saver: wandb.sdk.launch.agent.run_queue_item_file_saver.RunQueueItemFileSaver
) → None

Set up project and run the job.

Arguments:

job: Job to run.

`method` `LaunchAgent.task_run_job`

task_run_job(
    launch_spec: Dict[str, Any],
    job: Dict[str, Any],
    default_config: Dict[str, Any],
    api: wandb.apis.internal.Api,
    job_tracker: wandb.sdk.launch.agent.job_status_tracker.JobAndRunStatusTracker
) → None

`method` `LaunchAgent.update_status`

update_status(status: str) → None

Update the status of the agent.

Arguments:

status: Status to update the agent to.

3.5 - load_wandb_config

View the source code

`function` `load_wandb_config`

load_wandb_config() → Config

Load wandb config from WANDB_CONFIG environment variable(s).

The WANDB_CONFIG environment variable is a json string that can contain multiple config keys. The WANDB_CONFIG_[0-9]+ environment variables are used for environments where there is a limit on the length of environment variables. In that case, we shard the contents of WANDB_CONFIG into multiple environment variables numbered from 0.

Returns: A dictionary of wandb config values.

3.6 - manage_config_file

View the source code

`function` `manage_config_file`

manage_config_file(
    path: str,
    include: Optional[List[str]] = None,
    exclude: Optional[List[str]] = None,
    schema: Optional[Any] = None
)

Declare an overridable configuration file for a launch job.

If a new job version is created from the active run, the configuration file will be added to the job’s inputs. If the job is launched and overrides have been provided for the configuration file, this function will detect the overrides from the environment and update the configuration file on disk. Note that these overrides will only be applied in ephemeral containers. include and exclude are lists of dot separated paths with the config. The paths are used to filter subtrees of the configuration file out of the job’s inputs.

For example, given the following configuration file: yaml model: name: resnet layers: 18 training: epochs: 10 batch_size: 32

Passing include=['model'] will only include the model subtree in the job’s inputs. Passing exclude=['model.layers'] will exclude the layers key from the model subtree. Note that exclude takes precedence over include.

. is used as a separator for nested keys. If a key contains a ., it should be escaped with a backslash, e.g. include=[r'model\.layers']. Note the use of r to denote a raw string when using escape chars.

Args:

path (str): The path to the configuration file. This path must be relative and must not contain backwards traversal, i.e. ...
include (List[str]): A list of keys to include in the configuration file.
exclude (List[str]): A list of keys to exclude from the configuration file.
schema (dict | Pydantic model): A JSON Schema or Pydantic model describing describing which attributes will be editable from the Launch drawer. Accepts both an instance of a Pydantic BaseModel class or the BaseModel class itself.

Raises:

LaunchError: If the path is not valid, or if there is no active run.

3.7 - manage_wandb_config

View the source code

`function` `manage_wandb_config`

manage_wandb_config(
    include: Optional[List[str]] = None,
    exclude: Optional[List[str]] = None,
    schema: Optional[Any] = None
)

Declare wandb.config as an overridable configuration for a launch job.

If a new job version is created from the active run, the run config (wandb.config) will become an overridable input of the job. If the job is launched and overrides have been provided for the run config, the overrides will be applied to the run config when wandb.init is called. include and exclude are lists of dot separated paths with the config. The paths are used to filter subtrees of the configuration file out of the job’s inputs.

For example, given the following run config contents: yaml model: name: resnet layers: 18 training: epochs: 10 batch_size: 32 Passing include=['model'] will only include the model subtree in the job’s inputs. Passing exclude=['model.layers'] will exclude the layers key from the model subtree. Note that exclude takes precedence over include. . is used as a separator for nested keys. If a key contains a ., it should be escaped with a backslash, e.g. include=[r'model\.layers']. Note the use of r to denote a raw string when using escape chars.

Args:

include (List[str]): A list of subtrees to include in the configuration.
exclude (List[str]): A list of subtrees to exclude from the configuration.
schema (dict | Pydantic model): A JSON Schema or Pydantic model describing describing which attributes will be editable from the Launch drawer. Accepts both an instance of a Pydantic BaseModel class or the BaseModel class itself.

Raises:

LaunchError: If there is no active run.

4 - SDK

Use during training to log experiments, track metrics, and save model artifacts.

4.1 - 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.

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

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: 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, do not copy files to the cache after uploading.
policy: By default, set to “mutable”. If set to “mutable”, create a temporary copy of the file to prevent corruption during upload. If set to “immutable”, disable protection and rely on the user not to delete or change the file.
overwrite: If True, overwrite the file if it already exists.

Returns: The added manifest entry.

Raises:

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

`method` `Artifact.add_reference`

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

Add a reference denoted by a URI to the artifact.

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

By default, the following schemes are supported:

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

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

Args:

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

Returns: The added manifest entries.

Raises:

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

`method` `Artifact.checkout`

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

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

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

Args:

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

Returns: The path of the checked out contents.

Raises:

ArtifactNotLoggedError: If the artifact is not logged.

`method` `Artifact.delete`

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

Delete an artifact and its files.

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

Args:

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

Raises:

ArtifactNotLoggedError: If the artifact is not logged.

`method` `Artifact.download`

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

`method` `Artifact.link`

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.

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

Returns: An Artifact object.

Raises:

ArtifactNotLoggedError: If the artifact is not logged.

`method` `Artifact.new_file`

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

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

Args:

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

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

Raises:

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

`method` `Artifact.remove`

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

Remove an item from the artifact.

Args:

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

Raises:

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

`method` `Artifact.save`

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

Persist any changes made to the artifact.

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

Args:

project: A project to use for the artifact in the case that a run is not already in context.
settings: A settings object to use when initializing an automatic run. Most commonly used in testing harness.

`method` `Artifact.unlink`

unlink() → None

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

Raises:

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

`method` `Artifact.used_by`

used_by() → list[Run]

Get a list of the runs that have used this artifact.

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.

4.3 - controller

View the source code

`function` `controller`

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

Public sweep controller constructor.

Examples:

import wandb

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

4.4 - 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.

4.5 - Error

View the source code

`class` `Error`

Base W&B Error.

`method` `Error.init`

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

4.6 - 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=...).

4.7 - 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 | Literal[None, 'default', 'return_previous', 'finish_previous']" = None,
    resume: "bool | Literal['allow', 'never', 'must', 'auto'] | None" = None,
    resume_from: 'str | None' = None,
    fork_from: 'str | None' = None,
    save_code: 'bool | None' = None,
    tensorboard: 'bool | None' = None,
    sync_tensorboard: 'bool | None' = None,
    monitor_gym: 'bool | None' = None,
    settings: 'Settings | dict[str, Any] | None' = None
) → Run

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

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

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

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

Args:

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

Raises:

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

Returns: A Run object.

Examples: wandb.init() returns a run object, and you can also access the run object with wandb.run:

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 ...

4.8 - link_model

View the source code

`function` `wandb.link_model`

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.

First, 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”.

Next, 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: A path to the contents of this model, can be in the following forms:
- /local/directory
- /local/directory/file.txt
- s3://bucket/path
registered_model_name: the name of the registered model that the model is to be linked to. A registered model is a collection of model versions linked to the model registry, typically representing a team’s specific ML Task. The entity that this registered model belongs to will be derived from the run
name: the name of the model artifact that files in path will be logged to. This will default to the basename of the path prepended with the current run id if not specified.
aliases: 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.

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

Examples:

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

Invalid usage

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"],
)

4.9 - 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.

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})

4.10 - 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: A path to the contents of this artifact, can be in the following forms
- /local/directory
- /local/directory/file.txt
- s3://bucket/path
name (Optional[str]): An artifact name. Defaults to the basename of the path prepended with the current run id if not specified. Valid names can be in the following forms:
- name:version
- name:alias
- digest
type: The type of artifact to log, examples include dataset, model
aliases: Aliases to apply to this artifact, defaults to ["latest"]
tags: Tags to apply to this artifact, if any.

Returns: An Artifact object.

4.11 - 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: A path to the contents of this model, can be in the following forms
- /local/directory
- /local/directory/file.txt
- s3://bucket/path
name: A name to assign to the model artifact that the file contents will be added to. The string must contain only alphanumeric characters such as dashes, underscores, and dots. This will default to the basename of the path prepended with the current run id if not specified.
aliases: Aliases to apply to the created model artifact, defaults to ["latest"]

Returns: None

Raises:

ValueError: if name has invalid special characters

Examples:

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

Invalid usage

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

4.12 - login

View the source code

`function` `login`

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

Set up W&B login credentials.

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

Args:

anonymous: Set to “must”, “allow”, or “never”. If set to “must”, always log a user in anonymously. If set to “allow”, only create an anonymous user if the user isn’t already logged in. If set to “never”, never log a user anonymously. Default set to “never”.
key: The API key to use.
relogin: If true, will re-prompt for API key.
host: The host to connect to.
force: If true, will force a relogin.
timeout: Number of seconds to wait for user input.
verify: Verify the credentials with the W&B server.
referrer: The referrer to use in the URL login request.

Returns:

bool: if key is configured

Raises: AuthenticationError if api_key fails verification with the server UsageError if api_key cannot be configured and no tty

4.13 - plot

View the source code

`module` `wandb`

Chart Visualization Utilities

This module offers a collection of predefined chart types, along with functionality for creating custom charts, enabling flexible visualization of your data beyond the built-in options.

Global Variables

custom_chart
utils
viz

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

Returns:

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

Raises:

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

4.15 - restore

View the source code

`function` `restore`

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

Download the specified file from cloud storage.

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

Args:

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

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

Raises:

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

4.16 - 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

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.

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/".

4.17 - 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.

4.18 - sweep

View the source code

`function` `sweep`

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

Initialize a hyperparameter sweep.

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

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

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

Args:

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

Returns:

sweep_id: str. A unique identifier for the sweep.

4.19 - 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().

4.20 - 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: Optional list of pytorch models that have had watch called on them

4.21 - 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: An artifact name. May be prefixed with project/ or entity/project/. You can also pass an Artifact object created by calling wandb.Artifact. If no entity is specified in the name, the Run or API setting’s entity is used. Valid names can be in the following forms:
- name:version
- name:alias
type: The type of artifact to use.
aliases: Aliases to apply to this artifact
use_as: Optional string indicating what purpose the artifact was used with. Will be shown in UI.

Returns: An Artifact object.

4.22 - 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: 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

Raises:

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

Returns:

path: path to downloaded model artifact file(s).

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

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

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

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

Python

SDK

Data Types

Analytics and Query API

Launch Library

1 - Analytics and Query API

1.1 - api

module wandb.apis.public

class RetryingClient

method RetryingClient.__init__

property RetryingClient.app_url

property RetryingClient.server_info

method RetryingClient.execute

method RetryingClient.version_supported

class Api

method Api.__init__

property Api.api_key

property Api.client

property Api.default_entity

property Api.user_agent

property Api.viewer

method Api.artifact

method Api.artifact_collection

method Api.artifact_collection_exists

method Api.artifact_collections

method Api.artifact_exists

method Api.artifact_type

method Api.artifact_types

method Api.artifact_versions

method Api.artifacts

method Api.create_project

method Api.create_run

method Api.create_run_queue

method Api.create_team

method Api.create_user

method Api.flush

method Api.from_path

method Api.list_jobs

method Api.project

method Api.projects

method Api.queued_run

method Api.registries

method Api.reports

method Api.run

method Api.run_queue

method Api.runs

method Api.sweep

method Api.sync_tensorboard

method Api.team

method Api.upsert_run_queue

method Api.user

method Api.users

1.2 - artifacts

module wandb.apis.public

function server_supports_artifact_collections_gql_edges

function artifact_collection_edge_name

function artifact_collection_plural_edge_name

class ArtifactTypes

method ArtifactTypes.__init__

property ArtifactTypes.cursor

property ArtifactTypes.length

property ArtifactTypes.more

method ArtifactTypes.convert_objects

method ArtifactTypes.update_variables

class ArtifactType

method ArtifactType.__init__

property ArtifactType.id

property ArtifactType.name

method ArtifactType.collection

method ArtifactType.collections

method ArtifactType.load

class ArtifactCollections

method ArtifactCollections.__init__

property ArtifactCollections.cursor

property ArtifactCollections.length

property ArtifactCollections.more

method ArtifactCollections.convert_objects

method ArtifactCollections.update_variables

class ArtifactCollection

method ArtifactCollection.__init__

`module` `wandb.apis.public`

`class` `RetryingClient`

`method` `RetryingClient.init`

`property` RetryingClient.app_url

`property` RetryingClient.server_info

`method` `RetryingClient.execute`

`method` `RetryingClient.version_supported`

`class` `Api`

`method` `Api.init`

`property` Api.api_key

`property` Api.client

`property` Api.default_entity

`property` Api.user_agent

`property` Api.viewer

`method` `Api.artifact`

`method` `Api.artifact_collection`

`method` `Api.artifact_collection_exists`

`method` `Api.artifact_collections`

`method` `Api.artifact_exists`

`method` `Api.artifact_type`

`method` `Api.artifact_types`

`method` `Api.artifact_versions`

`method` `Api.artifacts`

`method` `Api.create_project`

`method` `Api.create_run`

`method` `Api.create_run_queue`

`method` `Api.create_team`

`method` `Api.create_user`

`method` `Api.flush`

`method` `Api.from_path`

`method` `Api.list_jobs`

`method` `Api.project`

`method` `Api.projects`

`method` `Api.queued_run`

`method` `Api.registries`

`method` `Api.reports`

`method` `Api.run`

`method` `Api.run_queue`

`method` `Api.runs`

`method` `Api.sweep`

`method` `Api.sync_tensorboard`

`method` `Api.team`

`method` `Api.upsert_run_queue`

`method` `Api.user`

`method` `Api.users`

`module` `wandb.apis.public`

`function` `server_supports_artifact_collections_gql_edges`

`function` `artifact_collection_edge_name`

`function` `artifact_collection_plural_edge_name`

`class` `ArtifactTypes`

`method` `ArtifactTypes.init`

`property` ArtifactTypes.cursor

`property` ArtifactTypes.length

`property` ArtifactTypes.more

`method` `ArtifactTypes.convert_objects`

`method` `ArtifactTypes.update_variables`

`class` `ArtifactType`

`method` `ArtifactType.init`

`property` ArtifactType.id

`property` ArtifactType.name

`method` `ArtifactType.collection`

`method` `ArtifactType.collections`

`method` `ArtifactType.load`

`class` `ArtifactCollections`

`method` `ArtifactCollections.init`

`property` ArtifactCollections.cursor

`property` ArtifactCollections.length

`property` ArtifactCollections.more

`method` `ArtifactCollections.convert_objects`

`method` `ArtifactCollections.update_variables`

`class` `ArtifactCollection`

`method` `ArtifactCollection.init`

`property` ArtifactCollection.aliases

`property` ArtifactCollection.created_at

`property` ArtifactCollection.description

`property` ArtifactCollection.id

`property` ArtifactCollection.name

`property` ArtifactCollection.tags

`property` ArtifactCollection.type

`method` `ArtifactCollection.artifacts`