Optimize Docker build to avoid timeout#5
Open
railway-app[bot] wants to merge 1 commit into
Open
Conversation
|
The latest updates on your projects. Learn more about Vercel for GitHub.
|
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Problem
The Docker build was timing out during image import because
sentence-transformerspulls in the full CUDA-enabled PyTorch distribution as a transitive dependency — roughly 2 GB of CUDA libraries (nvidia-cublas,nvidia-cuda-runtime, etc.) that are completely unused since the app runs inference on CPU via Groq's cloud API. This bloated the final image well past Railway's import timeout.Solution
Three targeted changes bring the image back under the timeout. First,
requirements.txtnow pinstorch==2.2.2+cpufrom PyTorch's CPU-only wheel index, which strips all CUDA libraries from the install. Second,--no-cache-diris added to thepip installstep in the Dockerfile so pip's download cache doesn't linger in the image layer. Third,HF_HOMEandTRANSFORMERS_CACHEare set to/app/data/hf_cache(both in the Dockerfile andstart.sh) so theall-MiniLM-L6-v2model — which was already downloaded at container startup, not build time — is cached inside the existing data volume and reused across restarts rather than re-downloaded on every deploy.Changes
server/python_bot/requirements.txtDockerfilestart.shGenerated by Railway