Delete an artifact

Delete artifacts interactively with the App UI or programmatically with the W&B Python SDK.

This page shows you how to delete W&B artifacts so you can remove unneeded data, free up storage, and manage retention. Delete artifacts interactively with the W&B App or programmatically with the W&B Python SDK. When you delete an artifact, W&B marks that artifact as a soft-delete. In other words, W&B marks the artifact for deletion but doesn’t immediately delete files from storage.

The contents of the artifact remain as a soft-delete, or pending deletion state, until a regularly run garbage collection process reviews all artifacts marked for deletion. The garbage collection process deletes associated files from storage if a previous or subsequent artifact version doesn’t use the artifact and its associated files.

Garbage collection is best-effort. W&B does not guarantee how quickly freed space appears in your object storage after you delete an artifact. Large deployments or backlogs can take longer than expected. For how this fits with run data, retention settings, and optional operator actions, see Manage bucket storage and costs.

Artifact garbage collection workflow

This section provides a visual overview of how artifact deletion flows through soft-delete and garbage collection before files are permanently removed. The following diagram illustrates the complete artifact garbage collection process:

You can schedule when artifacts are deleted from W&B with TTL policies. For more information, see Manage data retention with Artifact TTL policy.

Artifacts deleted by a TTL policy, the W&B Python SDK, or the W&B App are first soft-deleted. Soft-deleted artifacts are then garbage-collected before they’re permanently deleted.

Deleting an entity, project, or artifact collection triggers the artifact deletion process described on this page. When you delete a run and choose to delete its associated artifacts, those artifacts follow the same soft-delete and garbage collection workflow.

Delete an artifact version

Delete an artifact version interactively with the W&B App or programmatically with the W&B Python SDK.

W&B App

To delete an artifact version:

1. Navigate to the project that contains the artifact version you want to delete.
2. Select the Artifacts tab.
3. From the list of artifact types, select the type of artifact that contains the version you want to delete.
4. Click the action () menu next to the artifact version you want to delete.
5. From the dropdown, choose Delete Version.

W&B Python SDK

Delete an artifact version programmatically with the wandb.Artifact.delete() method. Provide the full name of the artifact. The full name consists of <entity>/<project>/<artifact_name>:<version>. Set the delete_aliases parameter to True to delete the artifact even if it has one or more aliases associated with it.

import wandb

api = wandb.Api()

# Get the artifact by its path
artifact = api.artifact("<entity>/<project>/<artifact_name>:<version>")

# Delete the artifact version along with any aliases
artifact.delete(delete_aliases=True)

Delete multiple artifact versions

Use this approach when you want to clean up several artifact versions logged by a single run in one operation, rather than deleting each version individually. The following code example shows how to delete multiple artifact versions. Provide the entity, project name, and run ID that created the artifact as arguments to wandb.Api.run(). This returns a run object that you can use to access all artifact versions created by that run. Next, iterate through the artifact versions and delete the versions that match your criteria.

Set the delete_aliases parameter to True (wandb.Artifact.delete(delete_aliases=True)) to delete an artifact version and any aliases associated with it.

Replace the <entity>, <project>, <run_id>, and <artifact_name> placeholders with your own values:

import wandb

# Initialize W&B API
api = wandb.Api()

# Get the run by its path. Consists of <entity>/<project>/<run_id>
run = api.run("<entity>/<project>/<run_id>")

# Specify the artifact name to delete versions for
artifact_name = "<artifact_name>"

# Search and delete artifact versions with the specified name
for artifact in run.logged_artifacts():
    print(f"Found artifact: {artifact.name}") # Example name run_4dfbufgq_model:v0
    # Grab only the artifact name without the version with split()
    if artifact.name.split(":")[0] == artifact_name:
        print(f"Deleting artifact version: {artifact.name}")
        artifact.delete(delete_aliases=True)

Delete multiple artifact versions with a specific alias

To remove only versions tagged with a particular alias (for example, an obsolete release tag), filter on the alias before deleting. The following code demonstrates how to delete multiple artifact versions that have a specific alias.

Replace the <entity>, <project>, <run_id>, <artifact_name>, and <alias> placeholders with your own values:

import wandb

# Initialize W&B API
api = wandb.Api()

# Get the run by its path. Consists of <entity>/<project>/<run_id>
run = api.run("<entity>/<project>/<run_id>")

# Specify the artifact name to delete versions for
artifact_name = "<artifact_name>"

# Specify the alias to filter artifact versions for deletion
desired_alias = "<alias>"

# Delete artifacts logged to run with alias 'v3' and 'v4'
for artifact in run.logged_artifacts():
    print(f"Found artifact: {artifact.name}")
    if (artifact.name.split(":")[0] == artifact_name) and (desired_alias in artifact.aliases):
            artifact.delete(delete_aliases=True)

Delete an artifact collection

Deleting a collection removes the collection and all artifact versions it contains. Deleted versions follow the same soft-delete and garbage collection workflow described in the preceding sections.

W&B App

To delete an artifact collection:

1. Navigate to the artifact collection you want to delete.
2. Click the action () menu next to the artifact collection name.
3. From the dropdown menu, select Delete.

W&B Python SDK

Delete artifact collection programmatically with the wandb.Artifact.delete() method.

Provide the full path of the artifact collection to wandb.Api.artifact_collection(name=""). The full path consists of <entity>/<project>/<artifact_collection_name>.

import wandb

# Initialize W&B API
api = wandb.Api()

# Get the artifact collection by its path. Consists of
# <entity>/<project>/<artifact_collection_name>
collection = api.artifact_collection(
    type_name = "<artifact_type>",
    name = "<entity>/<project>/<artifact_collection_name>"
)
collection.delete()

Protected aliases and deletion permissions

Artifacts with protected aliases have special deletion restrictions. Protected aliases are aliases in the W&B Registry that registry admins can set to prevent unauthorized deletion.

Important considerations for protected aliases:

• Non-registry admins can’t delete artifacts with protected aliases.
• Within a registry, registry admins can unlink protected artifact versions and delete collections and registries that contain protected aliases.
• For source artifacts: if a source artifact is linked to a registry with a protected alias, no user can delete it.
• Registry admins can remove the protected aliases from source artifacts and then delete them.

Enable garbage collection based on how W&B is hosted

Garbage collection timing is not guaranteed. See Manage bucket storage and costs for details.

Whether garbage collection is active by default depends on your W&B deployment type, so the steps to enable it vary. Garbage collection is active by default if you use W&B Multi-tenant Cloud. In W&B Dedicated and Self-Managed, you might need to take the following steps to activate garbage collection.

1. W&B Self-Managed: Set GORILLA_ARTIFACT_GC_ENABLED=true.
2. Dedicated Cloud: Contact support to verify that garbage collection is active.
3. Enable bucket versioning if you use AWS, Google Cloud, or any other storage provider such as Minio. If you use Azure, enable soft deletion, which is equivalent to bucket versioning.

Use the following table to confirm which requirements apply to your deployment. An X indicates you must satisfy the requirement:

	Environment variable	Enable versioning
Multi-tenant Cloud
Multi-tenant Cloud with BYOB storage		X
Dedicated Cloud
Dedicated Cloud with BYOB storage		X
Self-Managed	X	X

Secure storage connector is available only for Google Cloud Platform and Amazon Web Services.