Add Python SDK & model runner test framework (186 tests)#1
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Jovonni
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Mar 1, 2026
Jovonni
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- 29 test files across 4 categories: SDK unit (119), framework integration (22), model runner (37), E2E integration (8)
- Shared conftest.py with HTTP mocking, env fixtures, sample models, singleton reset
- CI workflow with 4 parallel jobs: SDK unit (Python 3.9/3.11/3.12 matrix), framework, model runner, integration
- Makefile targets: test-sdk, test-sdk-unit, test-sdk-frameworks, test-sdk-cov
- pyproject.toml test extras
- 29 test files across 4 categories: SDK unit (119), framework integration (22), model runner (37), E2E integration (8) - Shared conftest.py with HTTP mocking, env fixtures, sample models, singleton reset - CI workflow with 4 parallel jobs: SDK unit (Python 3.9/3.11/3.12 matrix), framework, model runner, integration - Makefile targets: test-sdk, test-sdk-unit, test-sdk-frameworks, test-sdk-cov - pyproject.toml test extras
- Fix: guard pandas/numpy imports with pytest.importorskip in test_datasets.py and test_features.py (caused CI collection errors) - Fix: add numpy/pandas to SDK unit test CI install step - Fix: add fix/** branch trigger to test-python.yml - Add TF/Keras detection tests to test_detect_framework.py - Add TF serialization roundtrip test to test_serialize_model.py - Add TF/Keras model registration test to test_models.py - Add register_model roundtrip integration tests: sklearn, pytorch, tensorflow — verifies full pipeline (detect → serialize → codegen → exec train/infer) through the SDK abstraction layer - Remove unnecessary _serialize_model mock from pytorch registration test (SimpleNet is now at module level, picklable by torch.save) 190 tests passing, 8 skipped.
test_create_dataset_from_dataframe_parquet requires pyarrow/fastparquet for pd.to_parquet() and pd.read_parquet(). Skip gracefully in CI environments that don't have it.
Tests the full roundtrip: serialize model → mock API artifact download → load_model() deserializes → verify usable model object. - sklearn: pickle.dumps → load_model → LogisticRegression with correct params - PyTorch: torch.save → load_model → nn.Module with working forward pass - TensorFlow: model.save(.keras) → load_model → working predict() - Unknown framework: raises ValueError This closes the last framework-aware SDK function that wasn't tested per model type. All framework-specific code paths now have coverage.
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