Programmatically manage binary, file-based assets

Clarifai artifacts are binary, file-based assets associated with your apps. An artifact acts as a logical container for any binary or structured output produced by a user’s workload.

At a high level, artifacts are:

• User-defined — Created explicitly by users or automatically by Pipelines. Pipelines support long-running workloads such as model training, fine-tuning, and data preprocessing, which produce artifact files used in downstream tasks.
• Versioned — Each artifact contains one or more immutable ArtifactVersions, representing distinct snapshots or checkpoints over time.
• Taggable and searchable — Artifacts support metadata and tagging, making them easy to organize, filter, and discover.
• Linked — Artifacts can be associated with Clarifai resources such as PipelineVersionRuns, Models, or Datasets, establishing clear data lineage and traceability.
• Timestamped — Artifacts track lifecycle timestamps including created_at, modified_at, and deleted_at.
• Resumable — Runs can resume from stored artifact checkpoints (exact URI and checksum) instead of recomputing from scratch, significantly reducing training time and cost.
• Reproducible and auditable — Pipeline runs can persist the exact files used to produce results (weights, checkpoints, configs, logs), ensuring experiments are reproducible and verifiable.
• Access-controlled and workspace-scoped — Artifacts inherit app and project permissions, enabling secure sharing and collaboration. Shared artifacts can be reused across multiple pipeline version runs without duplication.
• Modality-agnostic — Unlike entities such as inputs, models, or datasets — which are semantically tied to specific modalities or roles — an artifact provides a generic abstraction for storing arbitrary data, regardless of format or purpose.

Common Artifact Examples

Artifacts can store a wide range of assets, including:

• Model checkpoints and weights (.ckpt, .pt, .bin)
• Training logs or metrics (.json, .csv)
• Preprocessed embeddings (.npy, .pkl)
• Packaged datasets (.tar.gz, .parquet)
• Serialized configuration or tokenizer files (.yaml, .json)

Artifact Resource Paths

Artifacts are addressed using fully qualified Clarifai resource paths:

users/{user-id}/apps/{app-id}/artifacts/{artifact-id}

Artifact versions are addressed as:

users/{user-id}/apps/{app-id}/artifacts/{artifact-id}/versions/{version-id}

These paths are used consistently across the CLI and SDK when managing artifacts and their versions.

Build your next AI app, test and tune popular LLMs models, and much more.