This repo provides a docker image of MLFLow Tracking Server which is based on sqlite, an internal file system for metadata (e.g. parameters, metrics) and an AWS S3 Bucket for files and artifacts.

Before you start the MLFlow Tracking Server, you must create an AWS S3 Bucket and the corresponding credentials.

1. AWS Account
2. AWS CLI
3. Create a new AWS S3 Bucket
4. Add a new AWS User to to the system
5. Assign the following policy to the user:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "VisualEditor0",
            "Effect": "Allow",
            "Action": [
                "s3:ListAllMyBuckets",
                "s3:HeadBucket"
            ],
            "Resource": "*"
        },
        {
            "Sid": "VisualEditor1",
            "Effect": "Allow",
            "Action": "s3:*",
            "Resource": [
                "arn:aws:s3:::<_AWS_BUCKET_NAME_>",
                "arn:aws:s3:::<_AWS_BUCKET_NAME_>/*"
            ]
        }
    ]
}

$ docker run \
    --rm \
    --name mlflow-tracking-server \
    -p 5000:5000 \
    -e PORT=5000 \
    -e FILE_DIR=/mlflow \
    -e AWS_BUCKET=<YOUR_AWS_BUCKET> \
    -e AWS_ACCESS_KEY_ID=<YOUR_AWS_ACCESS_KEY_ID> \
    -e AWS_SECRET_ACCESS_KEY=<YOUR_AWS_SECRET_ACCESS_KEY> \
    foxrider/mlflow-tracking-server:0.2.0

Access to http://127.0.0.1:5000

1. Install the AWS CLI
2. Add the AWS credentials to the client. You can either set the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY on your client or use the AWS CLI command aws configure.

See this link

Pull requests are welcome :)

Florian Muchow

Thanks to all the contributors