build: parallelize python pip installation and remove redudant python-pip install #149
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WalkthroughThe pull request modifies the development Dockerfile to enhance the installation process for Python and its dependencies. The command to add the Changes
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Actionable comments posted: 0
🧹 Nitpick comments (3)
docker/development/Dockerfile (3)
95-97: LGTM: Efficient parallelization of pip installation.Good use of parallel with
-j5for concurrent pip installations. The temporary file cleanup is properly handled.Consider adding
--no-cache-dirto pip installations to reduce disk space usage during the build:- parallel -j5 "python{} /tmp/get-pip.py" ::: 3.9 3.10 3.11 3.12 3.13 && \ + parallel -j5 "python{} /tmp/get-pip.py --no-cache-dir" ::: 3.9 3.10 3.11 3.12 3.13 && \
101-102: LGTM: Good parallelization of Python tools installation.The parallel installation of Python tools is well organized and includes all necessary development packages.
Consider adding error checking to ensure all installations succeed:
-RUN parallel -j5 "python{} -m pip install --upgrade pip ipython" ::: 3.9 3.10 3.11 3.12 3.13 && \ - parallel -j5 "python{} -m pip install --upgrade setuptools build wheel twine pytest pybind11-stubgen" ::: 3.9 3.10 3.11 3.12 3.13 +RUN set -e && \ + parallel --halt now,fail=1 -j5 "python{} -m pip install --upgrade pip ipython" ::: 3.9 3.10 3.11 3.12 3.13 && \ + parallel --halt now,fail=1 -j5 "python{} -m pip install --upgrade setuptools build wheel twine pytest pybind11-stubgen" ::: 3.9 3.10 3.11 3.12 3.13The
--halt now,fail=1flag will stop all parallel jobs immediately if any job fails, preventing partial installations.
83-102: Great optimization of Python installation process!The changes effectively address the PR objectives by:
- Parallelizing pip and package installations
- Consolidating Python installations
- Adding necessary dev packages
These changes should significantly reduce the build time while maintaining reliability.
To further optimize the build time, consider:
- Using multi-stage builds to reduce the final image size
- Implementing layer caching strategies
- Moving less frequently changed installations earlier in the Dockerfile
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🔇 Additional comments (2)
docker/development/Dockerfile (2)
24-24: LGTM: Good addition of the parallel package.The
parallelpackage is correctly placed in the common tools section and will enable concurrent execution of pip installations.
83-91: Verify Python 3.13 compatibility.The consolidation of Python installations looks good and the addition of dev packages is beneficial. However, Python 3.13 is still in development and might not be stable enough for production use.
Run this script to check Python 3.13's current status:
✅ Verification successful
Python 3.13 installation is acceptable for development environment
Including Python 3.13 in a development Dockerfile is appropriate for early testing and compatibility checks, even though it's in alpha stage. This allows developers to test against upcoming Python versions while maintaining stable versions (3.9-3.12) for production use.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check Python 3.13's current release status curl -s https://www.python.org/downloads/ | grep -A 2 "Python 3.13"Length of output: 69
Script:
#!/bin/bash # Check deadsnakes PPA package information for Python 3.13 apt-cache policy python3.13 2>/dev/null || echo "Package information not available" # Search for any Python 3.13 related configurations or requirements rg -l "python3\.13|Python 3\.13" --type yaml --type conf --type txt --type md # Check if there are any specific Python 3.13 dependencies or requirements fd requirements.txt -x cat {} \; | grep -i "python.*3\.13"Length of output: 279
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This PR hopes to speed up the main dockerfile build a little more to get more margin under the 6h github runner limit.
It does this by removing unneeded python installations and parallelizing the pip installing
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