cea5eda985
* Created two services: recommendation training and recommendation serving * Deleted Docker temporary * Added features based in signals information * Added method to get sessions features using PG * Added same utils and core elements into ml_trainer * Added checks before training models, added handler for model serving * Updated serving API and recommendation functions to use frontend signals features * reorganized modules to have base image and for both serving and training * Added Dockerfiles and base Dockerfile * Solved issue while ordering sessions by relevance * Added method to save user feedback of recommendations * Added security authorization * Updated Dockerfile * fixed issues with secret insertion to API * Updated feedback structure * Added git for dags * Solved issue of insertion on recommendation feedback * Changed update method from def to async def and it is called during startup * Solved issues of airflow running mlflow in dag * Changes sanity checks and added middleware params * base path renaming * Changed update method to a interval method which loads one model each 10s if there are models to download * Added sql files for recommendation service and trainer * Cleaned files and added documentation for methods and classes * Added README file * Renamed endpoints, changed None into empty array and updated readme * refactor(recommendation): optimized query * style(recommendation): changed import to top file, renamed endpoints parameters, function optimization * refactor(recommendation): .gitignore * refactor(recommendation): .gitignore * refactor(recommendation): Optimized Dockerfiles * refactor(recommendation): changed imports * refactor(recommendation): optimized requests * refactor(recommendation): optimized requests * Fixed boot for fastapi, updated some queries * Fixed issues while downloading models and while returning json response from API * limited number of recommendations and set a minimum score to present recommendations * fix(recommendation): fixed some queries and updated prediction method * Added env value to control number of predictions to make * docs(recommendation): Added third party libraries used in recommendation service * frozen requirements * Update base_crons.py added `misfire_grace_time` to recommendation crons --------- Co-authored-by: Taha Yassine Kraiem <tahayk2@gmail.com>
170 lines
3.1 KiB
Text
170 lines
3.1 KiB
Text
### Example user template template
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### Example user template
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# IntelliJ project files
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.idea
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*.iml
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out
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gen
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### Python template
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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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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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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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db.sqlite3-journal
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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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.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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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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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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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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