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- Add plain KNN implementation with JSONL data processing - Create Docker deployment setup with python:3.13-slim base - Add comprehensive OJ-style testing system with accuracy validation - Update README with detailed scoring mechanism explanation - Add run.sh script following competition manual requirements 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
23 lines
637 B
Docker
23 lines
637 B
Docker
FROM python:3.13-slim
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# Create necessary directories
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RUN mkdir -p /home/admin/predict \
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/home/admin/data \
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/home/admin/workspace/job/logs \
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/home/admin/workspace/job/output/predictions \
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/home/admin/workspace/job/input
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# Copy files to required locations according to manual
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COPY run.sh /home/admin/predict/run.sh
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RUN chmod 777 /home/admin/predict/run.sh
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# Copy the compiled binary as 'test' executable
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COPY target/release/hfe_knn /home/admin/predict/test
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RUN chmod +x /home/admin/predict/test
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# Copy training data
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COPY dataset/train.jsonl /home/admin/data/data.jsonl
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# Set default command
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CMD ["/home/admin/predict/run.sh"]
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