Track external files

Use reference artifacts to track and use files saved outside of W&B servers, for example in CoreWeave AI Object Storage, an Amazon Simple Storage Service (Amazon S3) bucket, GCS bucket, Azure blob, HTTP file server, or NFS share.

W&B logs metadata about the the object, such as the object’s ETag and size. If object versioning is enabled on the bucket, the version ID is also logged.

The following describes how to construct reference artifacts.

Track an artifact in an external bucket

Use the W&B Python SDK to track references to files stored outside of W&B.

1. Initialize a run with wandb.init().
2. Create an artifact object with wandb.Artifact().
3. Specify the reference to the bucket path with the artifact object’s add_reference() method.
4. Log the artifact’s metadata with run.log_artifact().

import wandb

# Initialize a W&B run
run = wandb.init()

# Create an artifact object
artifact = wandb.Artifact(name="name", type="type")

# Add a reference to the bucket path
artifact.add_reference(uri = "uri/to/your/bucket/path")

# Log the artifact's metadata
run.log_artifact(artifact)
run.finish()

Suppose your bucket has the following directory structure:

s3://my-bucket

|datasets/
  |---- mnist/
|models/
  |---- cnn/

The datasets/mnist/ directory contains a collection of images. Track the directory as a dataset with wandb.Artifact.add_reference(). The following code sample creates a reference artifact mnist:latest using the artifact object’s add_reference() method.

import wandb

run = wandb.init()
artifact = wandb.Artifact(name="mnist", type="dataset")
artifact.add_reference(uri="s3://my-bucket/datasets/mnist")
run.log_artifact(artifact)
run.finish()

Within the W&B App, you can look through the contents of the reference artifact using the file browser, explore the full dependency graph, and scan through the versioned history of your artifact. The W&B App does not render rich media such as images, audio, and so forth because the data itself is not contained within the artifact.

Download an artifact from an external bucket

W&B retrieves the files from the underlying bucket when it downloads a reference artifact using the metadata recorded when the artifact is logged. If your bucket has object versioning enabled, W&B retrieves the object version that corresponds to the state of the file at the time an artifact was logged. As you evolve the contents of your bucket, you can always point to the exact version of your data a given model was trained on, because the artifact serves as a snapshot of your bucket during the training run.

The following code sample shows how to download a reference artifact. The the APIs for downloading artifacts are the same for both reference and non-reference artifacts:

import wandb

run = wandb.init()
artifact = run.use_artifact("mnist:latest", type="dataset")
artifact_dir = artifact.download()

Add and download an external reference example

The following code sample uploads a dataset to an Amazon S3 bucket, tracks it with a reference artifact, then downloads it:

import boto3
import wandb

run = wandb.init()

# Training here...

s3_client = boto3.client("s3")
s3_client.upload_file(file_name="my_model.h5", bucket="my-bucket", object_name="models/cnn/my_model.h5")

# Log the model artifact
model_artifact = wandb.Artifact("cnn", type="model")
model_artifact.add_reference("s3://my-bucket/models/cnn/")
run.log_artifact(model_artifact)

At a later point, you can download the model artifact. Specify the name of the artifact and its type:

import wandb

run = wandb.init()
artifact = run.use_artifact(artifact_or_name = "cnn", type="model")
datadir = artifact.download()

Cloud storage credentials

W&B uses the default mechanism to look for credentials based on the cloud provider you use. Read the documentation from your cloud provider to learn more about the credentials used:

For AWS, if the bucket is not located in the configured user’s default region, you must set the AWS_REGION environment variable to match the bucket region.

Track an artifact in a filesystem

Another common pattern for fast access to datasets is to expose an NFS mount point to a remote filesystem on all machines running training jobs. This can be an even simpler solution than a cloud storage bucket because from the perspective of the training script, the files look just like they are sitting on your local filesystem. Luckily, that ease of use extends into using Artifacts to track references to file systems, whether they are mounted or not.

Suppose you have a filesystem mounted at /mount with the following structure:

mount
|datasets/
		|-- mnist/
|models/
		|-- cnn/

Within mnist/ is a dataset, a collection of images. You can track it with an artifact:

import wandb

run = wandb.init()
artifact = wandb.Artifact("mnist", type="dataset")
artifact.add_reference("file:///mount/datasets/mnist/")
run.log_artifact(artifact)

Note the triple slash in the URL. The first component is the file:// prefix that denotes the use of filesystem references. The second component begins the path to the dataset, /mount/datasets/mnist/.

The resulting artifact mnist:latest looks and acts like a regular artifact. The only difference is that the artifact only consists of metadata about the files, such as their sizes and MD5 checksums. The files themselves never leave your system.

You can interact with this artifact just as you would a normal artifact. In the UI, you can browse the contents of the reference artifact using the file browser, explore the full dependency graph, and scan through the versioned history of your artifact. However, the UI cannot render rich media such as images, audio, because the data itself is not contained within the artifact.

Downloading a reference artifact:

import wandb

run = wandb.init()
artifact = run.use_artifact("entity/project/mnist:latest", type="dataset")
artifact_dir = artifact.download()

For a filesystem reference, a download() operation copies the files from the referenced paths to construct the artifact directory. In the above example, the contents of /mount/datasets/mnist are copied into the directory artifacts/mnist:v0/. If an artifact contains a reference to a file that was overwritten, then download() will throw an error because the artifact can no longer be reconstructed.

Putting it all together, you can use the following code to track a dataset under a mounted filesystem that feeds into a training job:

import wandb

run = wandb.init()

artifact = wandb.Artifact("mnist", type="dataset")
artifact.add_reference("file:///mount/datasets/mnist/")

# Track the artifact and mark it as an input to
# this run in one swoop. A new artifact version
# is only logged if the files under the directory
# changed.
run.use_artifact(artifact)

artifact_dir = artifact.download()

# Perform training here...

To track a model, log the model artifact after the training script writes the model files to the mount point:

import wandb

run = wandb.init()

# Training here...

# Write model to disk

model_artifact = wandb.Artifact("cnn", type="model")
model_artifact.add_reference("file:///mount/cnn/my_model.h5")
run.log_artifact(model_artifact)