> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tape.network/llms.txt
> Use this file to discover all available pages before exploring further.

# MLflow

[MLflow](https://mlflow.org) tracks machine learning experiments and stores each run's artifacts in an artifact store. Pointing its S3 artifact store at the gateway takes one environment variable.

## Before you start

You need a bucket and a credential. A bucket is a tape that has delegated writes to the gateway, and a credential is an access key pair the gateway operator issues you. [Set up a bucket](/tools/s3-gateway#set-up-a-bucket) walks through both.

A training run logs many files. Run it against your own gateway ([gateway setup](/protocol/node-setup/gateway)) rather than a public one.

## Configure

```bash theme={null}
export MLFLOW_S3_ENDPOINT_URL=https://s3.example.com
export AWS_ACCESS_KEY_ID=TAPE4Q7ZK2M9XH3PD8AW
export AWS_SECRET_ACCESS_KEY=mZ9vQ3nL8kR2tW6yB4cX7dF1hJ5pS0aG3uE8iO2q
```

Keep the tracking store local and point the experiment's artifact location at the bucket:

```python theme={null}
import mlflow

mlflow.set_tracking_uri("sqlite:///mlflow.db")
experiment_id = mlflow.create_experiment("demo", artifact_location="s3://my-bucket/mlflow")
```

## Everyday calls

```python theme={null}
with mlflow.start_run(experiment_id=experiment_id) as run:
    # one file, a whole directory, a dict, a string
    mlflow.log_artifact("model.pkl")
    mlflow.log_artifacts("./plots", artifact_path="plots")
    mlflow.log_dict({"lr": 0.001}, "params.json")
    mlflow.log_text("notes", "notes.txt")

# list and fetch
mlflow.artifacts.list_artifacts(run_id=run.info.run_id)
path = mlflow.artifacts.download_artifacts(run_id=run.info.run_id, artifact_path="model.pkl")
```

A 24 MiB artifact uploads in about 2 s and downloads in well under a second. Downloaded files match the originals byte for byte.

## Good to know

* The bucket must exist before MLflow starts. MLflow cannot create one. Buckets are created with [`tape create`](/tools/cli/commands/tape-management#create).
* Every artifact is a transaction on the chain, so the cost of a run depends on how many files it logs, not how large they are.

## Next

* [AWS CLI and boto3](/tools/quickstarts/aws-cli) for the client MLflow uses underneath.
* [Uploads](/tools/s3-gateway/uploads) for multipart uploads and overwrites.