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https://github.com/Toumash/mlflow-docker
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144
.gitignore
vendored
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144
.gitignore
vendored
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# mac
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# General
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.DS_Store
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.AppleDouble
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.LSOverride
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# Icon must end with two \r
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Icon
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# Thumbnails
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._*
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# Files that might appear in the root of a volume
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.DocumentRevisions-V100
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.fseventsd
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.Spotlight-V100
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.TemporaryItems
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.Trashes
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.VolumeIcon.icns
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.com.apple.timemachine.donotpresent
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# Directories potentially created on remote AFP share
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.AppleDB
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.AppleDesktop
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Network Trash Folder
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Temporary Items
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.apdisk
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95
README.md
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95
README.md
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@@ -0,0 +1,95 @@
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# mlflow
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* Reference:
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* official website: https://mlflow.org/
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* github: https://github.com/mlflow/mlflow
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## Usage
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### Build a Docker image
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```sh
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git clone https://github.com/jiankaiwang/mlflow-basis.git
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cd ./mlflow-basis
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sudo docker build -t mlflow-basis:latest .
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```
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### Run a Container
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```sh
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# list available docker images
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sudo docker images
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# list running containers
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sudo docker ps -a
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# run the container
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# container port 5000: mlflow server
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# --rm: remove the container while exiting
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# -i: interactive
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# -t: terminal mode
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# -v: path for host:container
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#
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# example: docker run -it --rm --name mlflow -p 5000:5000 mlflow:latest
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#
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sudo docker run -it --rm --name mlflow -p 5000:5000 -v <local>:<container> mlflow-basis:latest
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# stop the container
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sudo docker stop mlflow
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# restart the container
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sudo docker restart mlflow
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# remove the container
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sudo docker rm mlflow
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```
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### Interact with Container
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```sh
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sudo docker exec -it mlflow /bin/bash
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```
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### mlflow Quickstart
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* start the training in mlflow example
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```sh
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# by default
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# working dir: /app/mlflow/examples
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python ./quickstart/mlflow_tracking.py
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```
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* start the mlflow server to monitor the result
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```sh
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# host 0.0.0.0: allow all remote access
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mlflow server --file-store ./mlruns --host 0.0.0.0
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```
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### Push to Dockerhub
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```sh
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sudo docker login
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# set another tag
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sudo docker tag mlflow-basis:latest <username_in_dockerhub>/mlflow-basis:<version>
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# push to the dockerhub
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sudo docker push <username_in_dockerhub>/mlflow-basis:<version>
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```
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