feat(ray): support containerized model serving #114
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Because - `model-backend` needs `Ray` CLI to deploy dockerized application This commit - return deployment handle for CLI to reference
Because - we need to provide easy-to-use script for user to build and push containerized model to desired registry This commit - add `docker` dependency - add `build` module script for easy image building and pushing
Because - we need to copy model weight files along with config and model.py This commit - update `dockerfile` to copy all files in the same directory
Because - It is not practical to determine vram usage solely from model file size This commit - expose cpu/gpu/ram resource allocation config to user
Because - It is hard to know what went wrong without build logs - pip install tends to timeout for large packages installation This commit - print build logs - add default timeout for pip package installation
Because - we remove the restriction of the model folder structure after deprecating triton model support This commit - update custom model guide
Because - user may want to push to multiple registries, it is undesirable to define in `instill.yaml` This commit - separate `build` and `push` script - remove `registry` from model config
heiruwu
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Mar 20, 2024
Because - We are going to support containerized model serving with `Instill Model` This commit - add deployment handle return - add modules for building and pushing model image - expose cpu/gpu/memory resource allocation configs - update `instill_deployable` decorator
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Instill ModelThis commit
instill_deployabledecorator