obsideo-cloud 0.1.0b1

CLI for capturing and restoring ML training artifacts with encrypted cloud storage

mlvault

Immutable, encrypted snapshots of ML training runs.

mlvault captures your model checkpoints, metrics, and logs after every training run — encrypted and stored in the cloud. Restore any run on any machine with a single command.

pip install mlvault mlvault-mlflow
mlvault init

Quick start

1. Install

pip install mlvault mlvault-mlflow

2. Configure your API key

Get a key at obsideo.io/mlvault-beta, then:

mlvault init
# Paste your mlvault API key: mlv1_...
# Validating key... ok
# You're all set.

3. Run and archive a training job

mlvault run --collect ./outputs python train.py

mlvault runs your script, then encrypts and uploads everything in ./outputs to cloud storage.

Run ID : myproject_20240307_143022_abc123
Collect: /home/user/project/outputs
Command: python train.py

-- Training started --
...
-- Collecting artifacts --
  3 artifact(s) -> myproject_20240307_143022_abc123.tar.gz
-- Encrypting --
  myproject_20240307_143022_abc123.tar.gz.enc (258.4 KB)
-- Uploading --

Done. Run archived: myproject_20240307_143022_abc123
Remote : myproject_20240307_143022_abc123_bundle.enc

4. Restore a run

mlvault restore myproject_20240307_143022_abc123

Works on any machine where mlvault init has been run with the same API key.

5. List runs

mlvault list

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MLflow integration

mlvault integrates with MLflow as a custom artifact store. Artifacts logged via mlf.log_artifact() are staged locally, then pushed to cloud storage with mlvault commit.

Setup

import mlflow

# Set the artifact store once when creating an experiment
mlflow.create_experiment("my-experiment", artifact_location="mlvault://my-project")
mlflow.set_experiment("my-experiment")

Or set it via environment variable before training:

export MLFLOW_ARTIFACT_URI=mlvault://my-project

Training loop

import mlflow

with mlflow.start_run() as run:
    mlflow.log_param("lr", 0.001)
    mlflow.log_metric("loss", 0.42)
    mlflow.log_artifact("checkpoint.pt")

    run_id = run.info.run_id

# After training, push artifacts to cloud storage:
# mlvault commit <run_id>
print(f"Run ID: {run_id}")

Commit artifacts

mlvault commit <mlflow_run_id>

Restore MLflow artifacts

mlvault restore <run_id>

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Commands

Command	Description
mlvault init	Configure API key
mlvault run [cmd]	Run training command and archive artifacts
mlvault commit <mlflow_run_id>	Push staged MLflow artifacts to storage
mlvault restore <run_id>	Download and decrypt a stored run
mlvault list	List locally tracked runs
mlvault log <run_id>	Show run details

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Security

Artifacts are encrypted with AES-256-GCM before leaving your machine. Encryption keys are derived from your API key — only you can decrypt your artifacts.

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License

MIT