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tests/test_tutorial/test_events/test_tutorial003.py
Python - Registered: Sun Apr 21 07:19:11 GMT 2024 - Last Modified: Fri Jun 30 18:25:16 GMT 2023 - 3.6K bytes - Viewed (0) -
docs_src/events/tutorial003.py
yield # Clean up the ML models and release the resources ml_models.clear() app = FastAPI(lifespan=lifespan) @app.get("/predict") async def predict(x: float): result = ml_models["answer_to_everything"](x)
Python - Registered: Sun Apr 21 07:19:11 GMT 2024 - Last Modified: Tue Mar 07 15:46:00 GMT 2023 - 569 bytes - Viewed (0) -
tensorflow/c/c_op_requires.h
#define C_OPKERNELCONTEXT_REQUIRES_OK(CTX, C_STATUS, __VA_ARGS__) \ do { \ ::tensorflow::Status _s(__VA_ARGS__); \ if (!TF_PREDICT_TRUE(_s.ok())) { \ ::tensorflow::Set_TF_Status_from_Status(C_STATUS, _s); \ TF_OpKernelContext_Failure(CTX, C_STATUS); \
C - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Tue Aug 02 21:35:06 GMT 2022 - 2.3K bytes - Viewed (0) -
docs/bucket/lifecycle/DESIGN.md
### Transition Status
Plain Text - Registered: Sun Apr 21 19:28:08 GMT 2024 - Last Modified: Thu Sep 29 04:28:45 GMT 2022 - 4.3K bytes - Viewed (0) -
RELEASE.md
* `Model.fit_generator`, `Model.evaluate_generator`, `Model.predict_generator`, `Model.train_on_batch`, `Model.test_on_batch`, and `Model.predict_on_batch` methods now respect the `run_eagerly` property, and will correctly run using `tf.function` by default. Note that `Model.fit_generator`, `Model.evaluate_generator`, and `Model.predict_generator` are deprecated endpoints. They are
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tensorflow/c/eager/tape.h
// it (e.g. conds instead of ifs). The value shouldn't really matter. TF_RETURN_IF_ERROR(vspace_.BuildOnesLike(output_tensor, &aid)); } if (TF_PREDICT_FALSE(aid == nullptr)) { return tensorflow::errors::Internal( "Failed to create ones tensor for tensor ", output_tensor.GetID(), " with dtype ", output_tensor.GetDType()); }
C - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Tue Apr 02 12:40:29 GMT 2024 - 47.2K bytes - Viewed (1) -
tensorflow/BUILD
"//learning/lib/ami/simple_ml/...", "//learning/pathways/...", "//learning/serving/contrib/tfrt/mlir/canonical_ops/...", "//learning/serving/experimental/remote_predict/...", "//perftools/accelerators/xprof/convert/...", "//perftools/accelerators/xprof/integration_tests/...", "//smartass/brain/configure/...", "//tensorflow/...",
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docs/en/docs/release-notes.md
yield # Clean up the ML models and release the resources ml_models.clear() app = FastAPI(lifespan=lifespan) @app.get("/predict") async def predict(x: float): result = ml_models["answer_to_everything"](x) return {"result": result} ``` **Note**: This is the recommended way going forward, instead of using `startup` and `shutdown` events.
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common-protos/k8s.io/api/core/v1/generated.proto
// Those cannot be well described when embedded. // 3. Inconsistent validation. Because the usages are different, the validation rules are different by usage, which makes it hard for users to predict what will happen. // 4. The fields are both imprecise and overly precise. Kind is not a precise mapping to a URL. This can produce ambiguity
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