13 docs tagged with "artifacts"

Use wandb.init(mode="disabled") or set WANDB_MODE=disabled to configure W&B as a no-operation (NOOP) for testing purposes.

If an artifact reference is logged with W&B and versioning is enabled on the buckets, the version IDs appear in the Amazon S3 UI. To retrieve these version IDs and ETags in W&B, fetch the artifact and access the corresponding manifest entries. For example:

W&B tracks artifacts logged by each run and those used by each run to construct an artifact graph. This graph is a bipartite, directed, acyclic graph with nodes representing runs and artifacts. An example can be viewed here (click "Explode" to expand the graph).

One effective approach for logging models in a sweep involves creating a model artifact for the sweep. Each version represents a different run from the sweep. Implement it as follows:

To retrieve artifacts from the best performing run in a sweep, use the following code:

Occasionally, it is necessary to mark an artifact as the output of a previously logged run. In this case, reinitialize the old run and log new artifacts as follows:

Use save_code=True in wandb.init to save the main script or notebook that launches the run. To save all code for a run, version the code with Artifacts. The following example demonstrates this process:

To manage artifacts that contain sensitive data or to schedule the deletion of artifact versions, set a TTL (time-to-live) policy. For detailed instructions, refer to the TTL guide.

Only files that change between two artifact versions incur storage costs.

When deleting a run, a prompt asks whether to delete the associated artifacts. Choosing this option permanently removes the artifacts, making recovery impossible, even if the run itself is restored later.

There are various methods to version a model. Artifacts provide a tool for model versioning tailored to specific needs. A common approach for projects that explore multiple model architectures involves separating artifacts by architecture. Consider the following steps:

No caching exists; each launch job operates independently. Configure the queue or agent to mount a shared cache using Docker arguments in the queue configuration.

Artifacts inherit access permissions from their parent project: