mlflow-docker-minio/README.md

# MLFlow Docker Setup [![Actions Status](https://github.com/Toumash/mlflow-docker/workflows/VerifyDockerCompose/badge.svg)](https://github.com/Toumash/mlflow-docker/actions)

If you want to boot up mlflow project with one-liner - this repo is for you. 

The only requirement is docker installed on your system and we are going to use Bash on linux/windows.

[![Youtube tutorial](https://img.youtube.com/vi/ma5lA19IJRA/0.jpg)](https://www.youtube.com/watch?v=ma5lA19IJRA)

# Features
 - Setup by one file (.env)
 - Production-ready docker volumes
 - Separate artifacts and data containers
 - [Artifacts GUI](https://min.io/)
 - Ready bash scripts to copy and paste for colleagues to use your server!


## Simple setup guide
1. Configure `.env` file for your choice. You can put there anything you like, it will be used to configure you services

2. Run the Infrastructure by this one line:
```shell
$ docker-compose up -d
Creating network "mlflow-basis_A" with driver "bridge"
Creating mlflow_db      ... done
Creating tracker_mlflow ... done
Creating aws-s3         ... done
```

3. Create mlflow bucket. You can use my bundled script.

Just run 
```shell 
bash ./run_create_bucket.sh
```

You can also do it **either using AWS CLI or Python Api**.
<details><summary>AWS CLI</summary>

1. [Install AWS cli](https://aws.amazon.com/cli/) **Yes, i know that you dont have an Amazon Web Services Subscription - dont worry! It wont be needed!**
2. Configure AWS CLI - enter the same credentials from the `.env` file

```shell
aws configure
```
> AWS Access Key ID [****************123]: AKIAIOSFODNN7EXAMPLE  
> AWS Secret Access Key [****************123]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY  
> Default region name [us-west-2]: us-east-1  
> Default output format [json]: <ENTER>  

3. Run
```shell
aws --endpoint-url=http://localhost:9000 s3 mb s3://mlflow
```

</details>

<details><summary>Python API</summary>

1. Install Minio
```shell
pip install Minio
```
2. Run this to create a bucket
```python
from minio import Minio
from minio.error import ResponseError

s3Client = Minio(
    'localhost:9000',
    access_key='<YOUR_AWS_ACCESSS_ID>', # copy from .env file
    secret_key='<YOUR_AWS_SECRET_ACCESS_KEY>', # copy from .env file
    secure=False
)
s3Client.make_bucket('mlflow')
```

</details>

---

4. Open up http://localhost:5000 for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file

5. Configure your client-side

For running mlflow files you need various environment variables set on the client side. To generate them user the convienience script `./bashrc_install.sh`, which installs it on your system or `./bashrc_generate.sh`, which just displays the config to copy & paste.

> $ ./bashrc_install.sh   
> [ OK ] Successfully installed environment variables into your .bashrc!

The script installs this variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, MLFLOW_S3_ENDPOINT_URL, MLFLOW_TRACKING_URI. All of them are needed to use mlflow from the client-side.

6. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`

```shell
mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
# or
python ./quickstart/mlflow_tracking.py
```

7. *(Optional)* If you are constantly switching your environment you can use this environment variable syntax

```shell
MLFLOW_S3_ENDPOINT_URL=http://localhost:9000 MLFLOW_TRACKING_URI=http://localhost:5000 mlflow run git@github.com:databricks/mlflow-example.git -P alpha=0.5
```