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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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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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env/
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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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*.egg-info/
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.installed.cfg
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*.egg
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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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.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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# 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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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# DotEnv configuration
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.env
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# Database
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*.db
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*.rdb
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# Pycharm
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.idea
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# VS Code
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.vscode/
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# Spyder
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.spyproject/
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# Jupyter NB Checkpoints
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.ipynb_checkpoints/
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# exclude data from source control by default
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/data/
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# Mac OS-specific storage files
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.DS_Store
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# vim
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*.swp
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*.swo
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# Mypy cache
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.mypy_cache/

‎.s2i/environment

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APP_CONFIG=gunicorn_config.py

‎0_start_here.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# My Project\n",
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"\n",
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"In addition to being a place to experiment, this project has been structured to build and serve your model in a Flask application. The purpose is to allow data science exploration to easily transition into deployed services and applications on the OpenShift platform. After saving this project to git, it can be built on the OpenShift platform to serve models.\n",
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"\n",
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"Your dependencies will live in `requirements.txt` and your prediction function will live in `prediction.py`. As a Python based s2i application, this project can be configured and built upon to fit your needs.\n",
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"\n",
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"### Project Organization\n",
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"```\n",
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".\n",
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"├── README.md\n",
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"├── LICENSE\n",
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"├── requirements.txt <- Used to install packages for s2i application\n",
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"├── 0_start_here.ipynb <- Instructional notebook\n",
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"├── 1_run_flask.ipynb <- Notebook for running flask locally to test\n",
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"├── 2_test_flask.ipynb <- Notebook for testing flask requests\n",
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"├── .gitignore <- standard python gitignore\n",
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"├── .s2i <- hidden folder for advanced s2i configuration\n",
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"│   └── environment <- s2i environment settings\n",
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"├── gunicorn_config.py <- configuration for gunicorn when run in OpenShift\n",
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"├── prediction.py <- the predict function called from Flask\n",
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"└── wsgi.py <- basic Flask application\n",
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"```\n",
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"\n",
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"### Basic Flow\n",
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"1. Install and manage dependencies in `requirements.txt`.\n",
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"1. Experiment as usual.\n",
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"1. Extract your prediction into the `prediction.py` file.\n",
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"1. Update any dependencies.\n",
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"1. Run and test your application locally.\n",
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"1. Save to git.\n",
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"\n",
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"For a complete overview, please read the [README.md](./README.md)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Install Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "both",
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"id": "KUu4vOt5zI9d"
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},
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"outputs": [],
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"source": [
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"import sys\n",
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"!{sys.executable} -m pip install -r requirements.txt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Experiment\n",
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"\n",
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"Experiment with data and create your prediction function. Create any serialized models needed."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict(args_dict):\n",
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" return {'prediction': 'not implemented'}\n",
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"\n",
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"predict({'keys': 'values'})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create a Predict Function\n",
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"\n",
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"Extract the prediction logic into a standalone python file, `prediction.py` in a `predict` function. Also, make sure `requirements.txt` is updated with any additional packages you've used and need for prediction."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def predict(args_dict):\n",
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" return {'prediction': 'not implemented'}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Test Predict Function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from prediction import predict\n",
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"\n",
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"predict({'keys': 'values'})\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Run Flask\n",
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"\n",
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"Run flask in a separate notebook ([1_run_flask.ipynb](./1_run_flask.ipynb)) to create a local service to try it out. You must run the application in a separate notebook since it will use the kernel until stopped.\n",
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"\n",
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"```\n",
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"!FLASK_ENV=development FLASK_APP=wsgi.py flask run\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Test the Flask Endpoint\n",
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"\n",
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"Test your new service endpoint in this notebook or from a separate notebook ([2_test_flask.ipynb](./2_test_flask.ipynb)) to try it out. You can \n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!curl -X POST -H \"Content-Type: application/json\" --data '{\"data\": \"hello world\"}' http://localhost:5000/predictions\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"import json\n",
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"response = requests.post('http://127.0.0.1:5000/predictions', '{\"hello\":\"world\"}')\n",
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"response.json()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Save Your Project to Git (and Build)\n",
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"\n",
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"Now that you've created and tested your prediction and service endpoint, push the code up to git. This can be built as an s2i application on OpenShift.\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"name": "Object detection",
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"private_outputs": true,
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"provenance": [],
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"toc_visible": true
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎1_run_flask.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Run Flask\n",
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"Run your flask app (`wsgi.py`) from here and test it from a separate notebook."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"!{sys.executable} -m pip install -r requirements.txt\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!FLASK_ENV=development FLASK_APP=wsgi.py flask run"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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