Sherif akoush/quickfix/create artifacts on fly #374
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adriangonz
approved these changes
Nov 9, 2021
| @@ -120,8 +124,16 @@ def rest_client(rest_app: FastAPI) -> TestClient: | |||
| return TestClient(rest_app) | |||
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| @pytest.fixture | |||
| def anchor_image_directory() -> Path: | |||
| if not _ANCHOR_IMAGE_DIR.exists(): | |||
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Should we always write the model to a temporary dir (we could leverage pytest's tmp_path fixture for this)? Or do you think it's worth it to keep the "cached" artifact between tests?
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yeah I felt that locally we might want to look at the models that we created and perhaps not incur the overhead of creating them every time (locally)
| @@ -174,11 +186,19 @@ async def integrated_gradients_runtime() -> AlibiExplainRuntime: | |||
| extra=AlibiExplainSettings( | |||
| init_parameters={"n_steps": 50, "method": "gausslegendre"}, | |||
| explainer_type="integrated_gradients", | |||
| infer_uri=str(TESTS_PATH / "data" / "tf_mnist" / "model.h5"), | |||
| infer_uri=str(get_tf_mnist_model_uri()), | |||
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Similarly to the comment above, should we always train the model and save it to a temporary path?
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if it starts causing us issues lets do tmpdir but for now it seems that it is more useful to have the models cached as you mentioned.
sakoush
added a commit
to sakoush/MLServer
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Nov 24, 2021
* train models on the fly * delete artifacts as we create them on the fly * lint, format
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Create alibi test artifacts on-the-fly