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Tomasz Dłuski
2021-12-03 22:22:39 +01:00
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# MLFlow Docker Setup [![Actions Status](https://github.com/Toumash/mlflow-docker/workflows/VerifyDockerCompose/badge.svg)](https://github.com/Toumash/mlflow-docker/actions) # 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. > 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.
# 🚀 1-2-3! 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 `docker compose up`
3. Open up http://localhost:5000 for MlFlow, and http://localhost:9001/ to browse your files in S3 artifact store
The only requirement is docker installed on your system and we are going to use Bash on linux/windows.
**👇Video tutorial how to set it up on Microsoft Azure 👇** **👇Video tutorial how to set it up on Microsoft Azure 👇**
@@ -15,10 +20,6 @@ The only requirement is docker installed on your system and we are going to use
- Ready to use bash scripts for python development! - Ready to use bash scripts for python development!
- Automatically-created s3 buckets - Automatically-created s3 buckets
# 🚀 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 `docker compose up`
3. Open up http://localhost:5000 for MlFlow, and http://localhost:9001/ to browse your files in S3 artifact store
## How to use in ML development in python ## How to use in ML development in python