├── .github └── FUNDING.yml ├── .gitignore ├── 1. From a notebook to serving millions of users ├── __init__.py ├── configs │ └── __init__.py ├── dataloader │ └── __init__.py ├── evaluation │ └── __init__.py ├── executor │ └── __init__.py ├── main.py ├── model │ └── __init__.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ └── __init__.py ├── 10. How to use Docker containers and Docker Compose for Deep Learning applications ├── __init__.py ├── app │ ├── __init__.py │ ├── app.ini │ ├── client.py │ ├── nginx │ │ └── nginx.conf │ ├── resources │ │ └── yorkshire_terrier.jpg │ └── service.py ├── app_docker │ ├── app │ │ ├── .dockerignore │ │ ├── Dockerfile │ │ ├── app.ini │ │ ├── requirements.txt │ │ ├── service.py │ │ └── unet_inferrer.py │ ├── docker-compose.yml │ └── nginx │ │ ├── Dockerfile │ │ └── nginx.conf ├── configs │ ├── __init__.py │ ├── config.py │ ├── data_schema.py │ └── logging_config.yaml ├── dataloader │ ├── __init__.py │ └── dataloader.py ├── evaluation │ └── __init__.py ├── executor │ ├── __init__.py │ ├── tests │ │ └── unet_inferrer_test.py │ ├── unet_inferrer.py │ └── unet_trainer.py ├── main.py ├── model │ ├── __init__.py │ ├── base_model.py │ ├── tests │ │ └── unet_test.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ ├── __init__.py │ ├── config.py │ ├── logger.py │ └── plot_image.py ├── 2. Writing Deep Learning code: Best Practises ├── __init__.py ├── configs │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── config.cpython-37.pyc │ └── config.py ├── dataloader │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── dataloader.cpython-37.pyc │ └── dataloader.py ├── evaluation │ └── __init__.py ├── executor │ └── __init__.py ├── main.py ├── model │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── base_model.cpython-37.pyc │ │ └── unet.cpython-37.pyc │ ├── base_model.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ └── config.cpython-37.pyc │ └── config.py ├── 3. How to unit test Deep Learning ├── __init__.py ├── configs │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── config.cpython-37.pyc │ └── config.py ├── dataloader │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── dataloader.cpython-37.pyc │ └── dataloader.py ├── evaluation │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-37.pyc ├── executor │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-37.pyc ├── main.py ├── model │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── base_model.cpython-37.pyc │ │ └── unet.cpython-37.pyc │ ├── base_model.py │ ├── tests │ │ ├── __pycache__ │ │ │ └── unet_test.cpython-37.pyc │ │ └── unet_test.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-37.pyc └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ └── config.cpython-37.pyc │ └── config.py ├── 4. Logging and Debugging in Machine Learning ├── __init__.py ├── configs │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── config.cpython-37.pyc │ │ └── data_schema.cpython-37.pyc │ ├── config.py │ ├── data_schema.py │ └── logging_config.yaml ├── dataloader │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── dataloader.cpython-37.pyc │ └── dataloader.py ├── evaluation │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-37.pyc ├── executor │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-37.pyc ├── main.py ├── model │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ ├── base_model.cpython-37.pyc │ │ └── unet.cpython-37.pyc │ ├── base_model.py │ ├── tests │ │ ├── __pycache__ │ │ │ └── unet_test.cpython-37.pyc │ │ └── unet_test.py │ ├── unet.py │ └── unet_pytorch.py ├── notebooks │ └── segmentation.ipynb ├── ops │ ├── __init__.py │ └── __pycache__ │ │ └── __init__.cpython-37.pyc └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── config.cpython-37.pyc │ └── logger.cpython-37.pyc │ ├── config.py │ └── logger.py ├── 5. Data preprocessing for deep learning ├── __init__.py ├── configs │ ├── __init__.py │ ├── config.py │ ├── data_schema.py │ └── logging_config.yaml ├── dataloader │ ├── __init__.py │ └── dataloader.py ├── evaluation │ └── __init__.py ├── executor │ └── __init__.py ├── main.py ├── model │ ├── __init__.py │ ├── base_model.py │ ├── tests │ │ └── unet_test.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ ├── __init__.py │ ├── config.py │ └── logger.py ├── 6. Data preprocessing for Deep Learning (part 2) ├── __init__.py ├── configs │ ├── __init__.py │ ├── config.py │ ├── data_schema.py │ └── logging_config.yaml ├── dataloader │ ├── __init__.py │ └── dataloader.py ├── evaluation │ └── __init__.py ├── executor │ └── __init__.py ├── main.py ├── model │ ├── __init__.py │ ├── base_model.py │ ├── tests │ │ └── unet_test.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ ├── __init__.py │ ├── config.py │ └── logger.py ├── 7.How to build a custom production-ready Deep Learning Training loop in Tensorflow from scratch ├── __init__.py ├── configs │ ├── __init__.py │ ├── config.py │ ├── data_schema.py │ └── logging_config.yaml ├── dataloader │ ├── __init__.py │ └── dataloader.py ├── evaluation │ └── __init__.py ├── executor │ ├── __init__.py │ └── unet_trainer.py ├── main.py ├── model │ ├── __init__.py │ ├── base_model.py │ ├── tests │ │ └── unet_test.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ ├── __init__.py │ ├── config.py │ └── logger.py ├── 8. Deploy a Deep Learning model as a web application using Flask and Tensorflow ├── __init__.py ├── app │ ├── __init__.py │ ├── client.py │ ├── resources │ │ └── yorkshire_terrier.jpg │ └── service.py ├── configs │ ├── __init__.py │ ├── config.py │ ├── data_schema.py │ └── logging_config.yaml ├── dataloader │ ├── __init__.py │ └── dataloader.py ├── evaluation │ └── __init__.py ├── executor │ ├── __init__.py │ ├── tests │ │ └── unet_inferrer_test.py │ ├── unet_inferrer.py │ └── unet_trainer.py ├── main.py ├── model │ ├── __init__.py │ ├── base_model.py │ ├── tests │ │ └── unet_test.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ ├── __init__.py │ ├── config.py │ ├── logger.py │ └── plot_image.py ├── 9. How to use uWSGI and Nginx to serve a Deep Learning model ├── __init__.py ├── app │ ├── __init__.py │ ├── app.ini │ ├── client.py │ ├── nginx │ │ └── nginx.conf │ ├── resources │ │ └── yorkshire_terrier.jpg │ └── service.py ├── configs │ ├── __init__.py │ ├── config.py │ ├── data_schema.py │ └── logging_config.yaml ├── dataloader │ ├── __init__.py │ └── dataloader.py ├── evaluation │ └── __init__.py ├── executor │ ├── __init__.py │ ├── tests │ │ └── unet_inferrer_test.py │ ├── unet_inferrer.py │ └── unet_trainer.py ├── main.py ├── model │ ├── __init__.py │ ├── base_model.py │ ├── tests │ │ └── unet_test.py │ └── unet.py ├── notebooks │ └── segmentation.ipynb ├── ops │ └── __init__.py └── utils │ ├── __init__.py │ ├── config.py │ ├── logger.py │ └── plot_image.py └── README.md /.github/FUNDING.yml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/The-AI-Summer/Deep-Learning-In-Production/HEAD/.github/FUNDING.yml -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .idea -------------------------------------------------------------------------------- /1. 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