{"id":18656003,"url":"https://github.com/prgrmcode/transferlearningwithdocker","last_synced_at":"2026-04-21T16:36:00.545Z","repository":{"id":211127698,"uuid":"728181371","full_name":"prgrmcode/TransferLearningwithDocker","owner":"prgrmcode","description":"This is a AI classification project using PyTorch and 2 classes. We use trained model in a Flask application and show the AI classification prediction results on web page. 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Desktop](https://hub.docker.com/)_ on Windows before executing 'docker' commands\n\n# Docker Containers:\n\n## Training Container:\n\n### Build the training container\n\n```\ndocker build -t transfer_train-container -f Dockerfile_train .\n```\n\n### check gpus on training container\n\ndocker run --gpus 1 -ti transfer_train-container nvidia-smi\n\n### Run the training container\n\n- in unix terminal:\n\ndocker run --gpus 1 -v $(pwd)/data:/home/prgrmcode/app/data -ti --name train-container transfer_train-container\n\n- in windows command prompt:\n\n```\ndocker run --gpus 1 -v \"%cd%/data:/home/prgrmcode/app/data\" -ti --name train-container transfer_train-container\n```\n\n-- number of gpus, -v --volume mounts first folder from local machine to the folder in docker container, -ti target image, command(python3 .py)\n\n## Application Container:\n\n### Build the application container\n\n```\ndocker build -t transfer_app-container -f Dockerfile_app .\n```\n\n### Run the application container\n\n```\ndocker run -it --gpus 1 -p 5000:5000 --name app-container transfer_app-container bash\n```\n\n---\n\n---\n\n# With Docker compose:\n\nRun everything easily from docker-compose.yml file with one command\n\n## To create and run new training and app container together:\n\n```\ndocker-compose up\n```\n\n### When train and app container are up and running, you can navigate to:\n\n- **[localhost:5000](http://localhost:5000/)**\n\n\n- [app predictions](application/localhost.png)\n\n## Create and run a new training container:\n\n```\ndocker-compose up --no-deps --build transfer_train-container\n```\n\n## Create and run a new app container for hosting the application:\n\n```\ndocker-compose up --build transfer_app-container\n```\n\n## To start / reuse the existing training container:\n\n```\ndocker-compose start transfer_train-container\n```\n\n## To start / reuse the existing app container:\n\n```\ndocker-compose start transfer_app-container\n```\n\n## If docker uses too much disk space, run:\n\n```\ndocker system prune\n```\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprgrmcode%2Ftransferlearningwithdocker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprgrmcode%2Ftransferlearningwithdocker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprgrmcode%2Ftransferlearningwithdocker/lists"}