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tensorflow/c/eager/c_api.h
// Returns the number of elements in dimension `dim_index`. // Tensor representation on device can be transposed from its representation // on host. The data contained in dimension `dim_index` on device // can correspond to the data contained in another dimension in on-host // representation. The dimensions are indexed using the standard TensorFlow // major-to-minor order (slowest varying dimension first),
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
# targets matching the provided query, which start with // or @ but not # //tensorflow (so it looks for //third_party, //external, etc.), and then # gathers the list of all packages (i.e. directories) which contain those # targets. license_query() { bazel cquery --experimental_cc_shared_library "$1" --keep_going \ | grep -e "^//" -e "^@" \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort -u }
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tensorflow/c/eager/c_api_experimental_test.cc
/*isolate_session_state=*/false, init_timeout_in_ms); // Remote device on `worker2`. const char remote_device[] = "/job:localhost/replica:0/task:2/device:CPU:0"; // `ctx_0`, `ctx_1` contains `remote_device`. { const std::vector<std::string>& device_names = ListDeviceNames(ctx_0); ASSERT_TRUE(std::find(device_names.begin(), device_names.end(),
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ci/official/utilities/code_check_full.bats
# targets matching the provided query, which start with // or @ but not # //tensorflow (so it looks for //third_party, //external, etc.), and then # gathers the list of all packages (i.e. directories) which contain those # targets. license_query() { bazel cquery --experimental_cc_shared_library "$1" --keep_going \ | grep -e "^//" -e "^@" \ | grep -E -v "^//tensorflow" \ | sed -e 's|:.*||' \ | sort -u }
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tensorflow/c/eager/tape.h
// a tensor with the result. virtual Gradient* AggregateGradients( gtl::ArraySlice<Gradient*> gradient_tensors) const = 0; // Calls the passed-in backward function. // // `unneeded_gradients` contains sorted list of input indices for which a // gradient is not required. virtual Status CallBackwardFunction( const string& op_type, BackwardFunction* backward_function,
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tensorflow/c/eager/custom_device_test.cc
ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get()); TFE_OpSetAttrType(op.get(), "dtype", TF_FLOAT); TFE_OpSetAttrShape(op.get(), "shape", {}, 0, status.get()); TFE_OpSetAttrString(op.get(), "container", "", 0); TFE_OpSetAttrString(op.get(), "shared_name", "", 0); TFE_OpSetDevice(op.get(), name, status.get()); ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
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CONTRIBUTING.md
development to avoid installing the packages directly on your system (in which case remember to change the directory from `/root` to `/tensorflow` once you get into the running container so `bazel` can find the `tensorflow` workspace). you can do this by using the following command. As an example- ```bash docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
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tensorflow/c/eager/c_api_experimental.h
// which is passed to the functions referenced in the TFE_CustomDevice struct // `device` (execute, delete_device, etc.). It can for example contain the // names of wrapped devices. // // There are currently no graph semantics implemented for registered custom // devices, so executing tf.functions which contain operations placed on the // custom devices will fail. // // `device_name` must not name an existing physical or custom device. It must
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tensorflow/c/BUILD
"//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core/kernels:ops_testutil", "@com_google_absl//absl/container:inlined_vector", "@com_google_absl//absl/strings:str_format", "@eigen_archive//:eigen3", ], ) tf_cc_test( name = "ops_test", size = "medium",
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tensorflow/c/eager/immediate_execution_context.h
virtual void ClearCachesAndThreadExecutors() = 0; // Initialize the step resource container for a training step. This is used // in current TF runtime. For tfrt, it is used by fallback op handler. virtual void StartStep() = 0; // Destroy the step resource container for a training step. virtual void EndStep() = 0; // Return the Eager Executor for current thread. Please note that Eager
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