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Logging artifacts with Aim

One of the key aspects of experiment tracking is the ability to link certain files/artifacts to the training run metadata. Examples of such artifacts are the model checkpoints, training run configuration files, etc.

Starting from the version 3.19, Aim provides the logging API for artifacts, and the UI for showing artifact's metadata.

Artifacts logging SDK

There are only two steps for logging artifacts with Aim:

  1. Set the artifacts storage URI for the Aim Run:
import aim

run = aim.Run()
# Use S3 as artifacts storage
run.set_artifacts_uri('s3://aim/artifacts/')

# Use GCS as artifacts storage
run.set_artifacts_uri('gs://aim/artifacts/')

# Use file-system as artifacts storage
run.set_artifacts_uri('file:///home/user/aim/artifacts/')

Aim will create directory with the name of run.hash and store all artifacts there. Note that setting artifacts storage URI is required only once per run. 2. Log artifact object:

# Log run configuration files as artifacts
run.log_artifact('config.yaml', name='run-config')

Once logged, artifact metadata will appear in the Aim UI Run details page:

Storage backends

Training artifacts may represent large files, and, depending on the use case, might require different storage backends. For example, in case of small runs using the local file system, or network shared FS might be a good option. In case of the large models, cloud-based object stores, such as AWS S3, may be a better choice.

When the artifacts URI is set, Aim will detect storage backend based on the URI scheme. Currently supported backends for artifacts storage are.

  • S3
  • File System
  • GCS

S3 Artifacts Storage Backend

Aim uses boto3 Python package for accessing S3 resources. By default boto3 targets AWS S3 resources. Connection and credential validation is handled by boto3. A typical way of supplying credentials for instance is by setting AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables. More details on how configuration is done for boto3 is available here.

If you require direct control of how the boto3 client handles your s3 connection, you may use aim.storage.artifacts.s3_storage.S3ArtifactStorage_clientconfig(...). S3ArtifactStorage_clientconfig accepts any keyword-arguments that the boto3.client accepts, overwriting other means of boto3 configuration. This allows for the setting of credentials and connection details such as endpoint_url and botocore Config parameters.

import aim
from aim.storage.artifacts.s3_storage import S3ArtifactStorage_clientconfig

S3ArtifactStorage_clientconfig(aws_access_key_id=..., aws_secret_access_key=...,
                               endpoint_url=..., config={'retries': {...}, },)
run = aim.Run(...)
run.set_artifacts_uri('s3://...')
run.log_artifact(..., name=...)

GCS Artifacts Storage Backend

In order to use Google Cloud Store with Aim, install aim[gcs].

Aim uses google-cloud-storage Python package for accessing GCS resources. Connection and credential validation is handled by google-cloud-storage. More information is available here.

If you require direct control of how the google-cloud-storage Client is created, you may use aim.storage.artifacts.gc_storage.GCArtifactStorage_clientconfig(...). This method accepts any keyword-arguments that the google.cloud.storage.Client constructor accepts.

import aim
from aim.storage.artifacts.gc_storage import GCArtifactStorage_clientconfig

GCArtifactStorage_clientconfig(project=..., credentials=...)
run = aim.Run(...)
run.set_artifacts_uri('gs://...')
run.log_artifact(..., name=...)

File-system Artifacts Storage Backend

Aim provides ability to use mounted FS as an artifact storage. Any kind of storage that provides a mounted FS interface can thus be used as Aim artifact storage. However, performance of the solution should be considered when choosing this approach.