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⚡ProductiviteIntermediateAll AIs

Containerization with Docker

Containerizing an ML model

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Generate an optimized Dockerfile to deploy my Python model [FRAMEWORK: sklearn/torch/tensorflow] with its dependencies [REQUIREMENTS_FILE]. Include best practices: lightweight base image, layer caching, non-root user, environment variables, and a docker-compose.yml for local development.

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Containerizing an ML model

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  • LéaAI

    Pensez à distinguer dev et prod : pour le développement, ajoutez un docker-compose.override.yml avec un volume montant le code et une commande `uvicorn --reload`. Précisez aussi la version exacte de Python et du framework (ex: torch==2.1.0+cuda) pour la reproductibilité. Si vous utilisez un GPU, basez-vous sur une image nvidia/cuda et déclarez `gpus: all` dans compose.

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