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* feat: Enhance annotation module with template management and validation - Added DatasetMappingCreateRequest and DatasetMappingUpdateRequest schemas to handle dataset mapping requests with camelCase and snake_case support. - Introduced Annotation Template schemas including CreateAnnotationTemplateRequest, UpdateAnnotationTemplateRequest, and AnnotationTemplateResponse for managing annotation templates. - Implemented AnnotationTemplateService for creating, updating, retrieving, and deleting annotation templates, including validation of configurations and XML generation. - Added utility class LabelStudioConfigValidator for validating Label Studio configurations and XML formats. - Updated database schema for annotation templates and labeling projects to include new fields and constraints. - Seeded initial annotation templates for various use cases including image classification, object detection, and text classification. * feat: Enhance TemplateForm with improved validation and dynamic field rendering; update LabelStudio config validation for camelCase support * feat: Update docker-compose.yml to mark datamate dataset volume and network as external * feat: Add tag configuration management and related components - Introduced new components for tag selection and browsing in the frontend. - Added API endpoint to fetch tag configuration from the backend. - Implemented tag configuration management in the backend, including loading from YAML. - Enhanced template service to support dynamic tag rendering based on configuration. - Updated validation utilities to incorporate tag configuration checks. - Refactored existing code to utilize the new tag configuration structure. * feat: Refactor LabelStudioTagConfig for improved configuration loading and validation * feat: Update Makefile to include backend-python-docker-build in the build process * feat: Migrate to poetry for better deps management * Add pyyaml dependency and update Dockerfile to use Poetry for dependency management - Added pyyaml (>=6.0.3,<7.0.0) to pyproject.toml dependencies. - Updated Dockerfile to install Poetry and manage dependencies using it. - Improved layer caching by copying only dependency files before the application code. - Removed unnecessary installation of build dependencies to keep the final image size small. * feat: Remove duplicated backend-python-docker-build target from Makefile * fix: airflow is not ready for adding yet * feat: update Python version to 3.12 and remove project installation step in Dockerfile
43 lines
1.5 KiB
Docker
43 lines
1.5 KiB
Docker
FROM python:3.12-slim
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# Single-stage image with build cache optimization using BuildKit cache mounts.
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# Note: to use the cache mount syntax you must build with BuildKit enabled:
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# DOCKER_BUILDKIT=1 docker build . -f scripts/images/datamate-python/Dockerfile -t datamate-backend-python
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ENV PYTHONDONTWRITEBYTECODE=1 \
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PYTHONUNBUFFERED=1 \
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# Poetry configuration
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POETRY_VERSION=2.2.1 \
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POETRY_NO_INTERACTION=1 \
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POETRY_VIRTUALENVS_CREATE=false \
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POETRY_CACHE_DIR=/tmp/poetry_cache
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ENV PATH="/root/.local/bin:$PATH"
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WORKDIR /app
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# Install Poetry via pipx as Poetry officially recommends
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RUN --mount=type=cache,target=/root/.cache/pip \
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pip install --upgrade --root-user-action=ignore pip \
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&& pip install --root-user-action=ignore pipx \
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&& pipx install "poetry==$POETRY_VERSION"
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# Copy only dependency files first (leverages layer caching when dependencies don't change)
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COPY runtime/datamate-python/pyproject.toml runtime/datamate-python/poetry.lock* /app/
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# Install dependencies using Poetry with cache mount
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# --no-root: don't install the project itself yet (only dependencies)
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# --only main: only install main dependencies, not dev dependencies
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RUN --mount=type=cache,target=$POETRY_CACHE_DIR \
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poetry install --no-root --only main
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# Copy the rest of the application
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COPY runtime/datamate-python /app
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COPY runtime/datamate-python/deploy/docker-entrypoint.sh /docker-entrypoint.sh
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RUN chmod +x /docker-entrypoint.sh || true
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# Expose the application port
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EXPOSE 18000
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ENTRYPOINT ["/docker-entrypoint.sh"] |