{"id":18664654,"url":"https://github.com/scionoftech/mlflow-docker-s3","last_synced_at":"2025-11-06T10:30:25.838Z","repository":{"id":101410381,"uuid":"413333851","full_name":"scionoftech/mlflow-docker-s3","owner":"scionoftech","description":"MLflow setup using Docker and AWS S3","archived":false,"fork":false,"pushed_at":"2021-10-04T09:06:05.000Z","size":6,"stargazers_count":2,"open_issues_count":1,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-12-27T17:23:38.639Z","etag":null,"topics":["mlflow","mlflow-docker","mlflow-server","mlflow-tracking-server"],"latest_commit_sha":null,"homepage":"","language":"Shell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/scionoftech.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-10-04T08:19:45.000Z","updated_at":"2024-04-12T09:34:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"53aec16e-8dbe-42e1-9cd1-cd7e1e8f4dfb","html_url":"https://github.com/scionoftech/mlflow-docker-s3","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scionoftech%2Fmlflow-docker-s3","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scionoftech%2Fmlflow-docker-s3/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scionoftech%2Fmlflow-docker-s3/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/scionoftech%2Fmlflow-docker-s3/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/scionoftech","download_url":"https://codeload.github.com/scionoftech/mlflow-docker-s3/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239491352,"owners_count":19647811,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["mlflow","mlflow-docker","mlflow-server","mlflow-tracking-server"],"created_at":"2024-11-07T08:24:24.154Z","updated_at":"2025-11-06T10:30:25.725Z","avatar_url":"https://github.com/scionoftech.png","language":"Shell","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MLflow-Docker-S3\n\n\n# Features\n - MLflow in Docker container\n - Mysql Docker container for MLflow tracking data\n - Minio browser(https://min.io/) Docker container for artifacts.\n - Nginx proxy Docker container for MLflow UI\n\n## How to setup\n\n1. Clone the Repo \n\n2. Update `.env` file with required details\n\n3. Start the Setup by this one line:\n   \n   ```shell\n   $ docker-compose up -d\n   ```\n\n4. Open up http://localhost:5000 for MlFlow, and http://localhost:9000 for S3 bucket (MLflow artifacts) with credentials from `.env` file\n\n5. Configure MLflow client-side\n\nFor running mlflow files we need various environment variables set on the client side. To generate them use the script `./bashrc_install.sh`, which installs it on your system.\n\n\u003e $ ./bashrc_install.sh   \n\u003e [ OK ] Successfully installed environment variables into your .bashrc!\n\nThe 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.\n\n6. Test the MLflow setup for tracking and Artifacts in S3\n\n```shell\npython mlflow_tracking.py\n```\n\n### References\n\n- mlflow-docker - [Production ready docker-compose configuration for ML Flow with Mysql and Minio S3 Topics](https://github.com/Toumash/mlflow-docker)\n- deploy-mlflow-with-docker-compose - [Track your machine learning experiences with MLflow easily deployed thanks to docker-compose](https://towardsdatascience.com/deploy-mlflow-with-docker-compose-8059f16b6039)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscionoftech%2Fmlflow-docker-s3","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fscionoftech%2Fmlflow-docker-s3","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fscionoftech%2Fmlflow-docker-s3/lists"}