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tensorflow/c/c_api_test.cc
EXPECT_TRUE(feed == feed2); ASSERT_TRUE(GetNodeDef(feed, &node_def)); ASSERT_TRUE(GetNodeDef(feed2, &node_def2)); EXPECT_EQ(node_def.DebugString(), node_def2.DebugString()); // Test iterating through the nodes of a graph. found_placeholder = false; found_scalar_const = false; found_add = false; bool found_neg = false; size_t pos = 0; TF_Operation* oper;
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CONTRIBUTING.md
the change has been submitted internally, your pull request will be merged automatically on GitHub. If you want to contribute, start working through the TensorFlow codebase, navigate to the [GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start looking through interesting issues. If you are not sure of where to start, then start by trying one of the smaller/easier issues here i.e.
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ci/official/utilities/rename_and_verify_wheels.sh
# ============================================================================== # # Usage: rename_and_verify_wheels.sh # This script is aware of TFCI_ variables, so it doesn't need any arguments. # Puts new wheel through auditwheel to rename and verify it, deletes the old # one, checks the filesize, and then ensures the new wheel is installable. set -euxo pipefail cd "$TFCI_OUTPUT_DIR"
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RELEASE.md
* Introducing `tf.types.experimental.AtomicFunction` as the fastest way to perform TF computations in Python. * Can be accessed through `inference_fn` property of `ConcreteFunction`s * Does not support gradients.
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tensorflow/BUILD
":macos_x86_64_with_framework_shared_object", ":macos_arm64_with_framework_shared_object", ], ) # Config setting that is satisfied when TensorFlow is being built with CUDA # support through e.g. `--config=cuda` (or `--config=cuda_clang` in OSS). alias( name = "is_cuda_enabled", actual = if_oss( "@local_config_cuda//:is_cuda_enabled", "@local_config_cuda//cuda:using_clang",
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.bazelrc
# release while SASS is only forward compatible inside the current # major release. Example: sm_80 kernels can run on sm_89 GPUs but # not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs. build:cuda_clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_60,sm_70,sm_80,sm_89,compute_90" # Set up compilation CUDA version and paths and use the CUDA Clang toolchain.
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