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CONTRIBUTING.md
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 ``` Once you have the packages installed, you can run a specific unit test in bazel by doing as follows: ```bash
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tensorflow/c/eager/c_api_experimental.h
// EagerService to maintain consistency between client and worker. The // context_id is initialized with a dummy value and is later set when the worker // is initialized (either locally or remotely). The context_id can change during // the process lifetime although this should cause the worker to be // reinitialized (e.g. cleared caches) as well. TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx);
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tensorflow/c/BUILD
"//conditions:default": [], }), tags = [ "no_cuda_asan", # TODO(b/181771536) "no_windows", # TODO(b/155444728) ], # We must ensure that the dependencies can be dynamically linked since # the shared library must be able to use core:framework. deps = [ ":c_api", ":c_api_internal", ":c_test_util", ":test_op_kernel",
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tensorflow/c/eager/dlpack.cc
int64_t expected_stride = 1; for (int i = ndim - 1; i >= 0; --i) { // Empty tensors are always compact regardless of strides. if (shape_arr[i] == 0) return true; // Note that dimensions with size=1 can have any stride. if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) { valid = false; } expected_stride *= shape_arr[i]; } return valid; } } // namespace
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tensorflow/c/eager/tape.h
// gradients available initially). // // Then we repeatedly pop an entry from the stack, run its backprop, and update // the gradients of its inputs. Once we have computed all gradients for a single // input we can mark this input as done, and this can trigger adding an entry to // the stack if all outputs of that entry are now done. // // When the stack is empty we have gradients for all tensors we're interested // in. namespace {
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ci/official/utilities/code_check_full.bats
# checks for cross-file issues like bad includes or missing BUILD definitions. # # We can't test on the windows toolchains because they're using a legacy # toolchain format (or something) that specifies the toolchain directly instead # of as a "repository". They can't be valid on Linux because Linux can't do # anything with a Windows-only toolchain, and bazel errors if trying to build # that directory.
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tensorflow/c/c_api_experimental.cc
// TODO(b/113217601): This is needed for EagerContext::runner_ to use a // threadpool, so that we avoid the possibility of running the runner_ in the // threadpool of GPU event mgr, as that can trigger more callbacks to be // scheduled on that same threadpool, causing a deadlock in cases where the // caller of event_mgr->ThenExecute() blocks on the completion of the callback
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
CancellationManager* cancellation_manager_ TF_GUARDED_BY(execution_mutex_); // Outputs: std::vector<TensorHandlePtr> op_outputs_ TF_GUARDED_BY(execution_mutex_); // TF_Status is an incomplete type and so can't be stack allocated. To avoid // unnecessary allocations each Execute call, we keep one heap-allocated // version for the thread. StatusPtr status_ TF_GUARDED_BY(execution_mutex_); const std::string device_;
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tensorflow/c/eager/c_api_distributed_test.cc
} return tensorflow::errors::Internal("Graph pass runs for more than once!"); } private: bool first_call_ = true; }; // After the graph pass is registered, it takes effect globally and can affect // other test cases. Define a static variable to switch it on and off. bool GraphErrorInjectionPass::enabled_ = false; // Test to ensure that a registered graph optimization pass is only executed
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