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Tomasz Dłuski
2020-08-24 09:25:08 +02:00
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@@ -7,7 +7,22 @@ The only requirement is docker installed on your system and we are going to use
## Step by step guide
1. Configure `.env` file for your choice. You can put there anything you like, it will be used for our services configuration
2. Create mlflow bucket. You can do it **either using AWS CLI or Python Api**. **You dont need an AWS subscription**
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
```
Your mlflow_db is slowly getting ready - it migh take up to 1 minute. To be sure, that all of the services are running fine, just run docker-compose up -d until you see all services `up-to-date`
> $ docker-compose up -d
> mlflow_db is up-to-date
> aws-s3 is up-to-date
> tracker_mlflow is up-to-date
3. Create mlflow bucket. You can do it **either using AWS CLI or Python Api**. **You dont need an AWS subscription**
<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!**
@@ -53,9 +68,9 @@ s3Client.make_bucket('mlflow')
---
3. Open up http://localhost:5000/#/ for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file
4. Open up http://localhost:5000/#/ for MlFlow, and http://localhost:9000/minio/mlflow/ for S3 bucket (you artifacts) with credentials from `.env` file
4. Configure your client-side
5. Configure your client-side
For running mlflow files you AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables present on the client-side.
@@ -93,7 +108,7 @@ source ~/.bashrc
```
7. Test the pipeline with below command with conda. If you dont have conda installed run with `--no-conda`
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
@@ -104,7 +119,7 @@ Optionally you can run
python ./quickstart/mlflow_tracking.py
```
8. (Optional) If you are constantly switching your environment you can use this environment variable syntax
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