0ce879aa44
- Implemented HTML pages for datasets, models, training, testing, and results. - Created API endpoints for managing repositories, results, tests, and training sessions. - Added functionality for streaming training progress via Server-Sent Events (SSE). - Introduced a Dockerfile for the ML runner with necessary dependencies. - Developed an SDK for user code execution within the runner container. - Enhanced CSS styles for improved UI layout and navigation. - Established a layout template for consistent HTML structure across pages. - Added JavaScript for dynamic interactions on the models page. - Implemented WebSocket handling for real-time communication with kiosk devices and controllers. - Implemented model registration and management API at /api/models - Added Gitea proxy API for repository interactions at /api/repos - Created results API for listing and comparing training results at /api/results - Developed training management API for enqueueing and retrieving training jobs at /api/trainings - Introduced SSE endpoint for live training progress updates - Added HTML pages for models, datasets, and training management - Created a Dockerfile for the ML runner with necessary dependencies - Developed SDK for user code execution within the runner container - Enhanced CSS styles for improved UI/UX - Implemented WebSocket communication for real-time device and controller interactions in the kiosk system
17 lines
338 B
Docker
17 lines
338 B
Docker
FROM python:3.11-slim
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WORKDIR /app
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ENV PYTHONUNBUFFERED=1
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RUN apt-get update && apt-get install -y --no-install-recommends git \
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&& rm -rf /var/lib/apt/lists/*
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COPY ./requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . /app
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EXPOSE 3007
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "3007"]
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