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CONTRIBUTING.md
change. - You make the change and submit it for the review again. - This cycle repeats itself until the PR gets approved. - Note: As a friendly reminder, we may reach out to you if the PR is awaiting your response for more than 2 weeks. **4. Approved** - Once the PR is approved, it gets `kokoro:force-run` label applied and it initiates CI/CD tests.
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tensorflow/c/c_api_test.cc
EXPECT_EQ(X, dxy_dyValue); TF_DeleteTensor(fetchValues[0]); TF_DeleteTensor(fetchValues[1]); } // REGISTER_OP for CApiAttributesTest test cases. // Registers two ops, each with a single attribute called 'v'. // The attribute in one op will have a type 'type', the other // will have list(type). #define ATTR_TEST_REGISTER_OP(type) \
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ci/official/requirements_updater/updater.sh
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # script to run pip-compile for each requirement. # if there is a change in requirements.in then all lock files will be updated # accordingly # All commands run relative to this directory cd "$(dirname "${BASH_SOURCE[0]}")" mv BUILD.bazel BUILD
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ci/official/requirements_updater/README.md
updating requirements for multiple minor versions of Python. It takes in a file with a set of dependencies, and produces a more detailed requirements file for each version, with hashes specified for each dependency required, as well as their sub-dependencies. ### How to update/add requirements By default, the name of the base requirements file is `requirements.in`, but it
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ci/official/utilities/setup_macos.sh
echo 'sure they are present on your system and try again.' exit 1 fi # "TFCI_MACOS_BAZEL_TEST_DIR_PATH" specifies the directory that Bazel should use # when running tests. Each test will be executed in a separate subdirectory # inside this directory. TF Mac builds need ~150 GB of disk space to be able to # run all the tests. Since TFCI Mac VMs execute Bazel test commands in a
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RELEASE.md
* Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.random()` operation, which controls whether the sequence of generated random numbers should be re-randomized every epoch or not (the default behavior). If `seed` is set and `rerandomize_each_iteration=True`, the `random()` operation will produce a different (deterministic) sequence of numbers every epoch.
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tensorflow/c/experimental/gradients/math_grad.cc
// Get transpose attrs bool t_a; TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_a", &t_a)); bool t_b; TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_b", &t_b)); // Conj each input AbstractTensorHandle* conj_output; std::string name = "Conj_A_MatMul_Grad"; TF_RETURN_IF_ERROR( SafeConj(ctx, forward_inputs_[0], &conj_output, name.c_str()));
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tensorflow/c/c_api_experimental.cc
// (as in the case of ConstOp kernel creation on GPU, which involves copying a // CPU tensor to GPU). // Setting a larger thread pool does not help with the Swift caller, as we use // a different TFE context for each thread of execution (for running graph // functions, and their send/recvs corountines). config.set_inter_op_parallelism_threads(1); TF_Buffer* ret = TF_NewBuffer(); TF_CHECK_OK(MessageToBuffer(config, ret));
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tensorflow/c/eager/tape.h
// // First we filter the tape to just the subset of operations we want to // differentiate. In the process of doing so we count how many times each Tensor // is used as an input to an op (so we know when we're done computing gradients // for that Tensor). We also count, for each tape entry, how many of its output // Tensors need gradients to be computed (Tensors which are not used do not need // any gradients to be computed). //
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configure.py
'device at: https://developer.nvidia.com/cuda-gpus. Each capability ' 'can be specified as "x.y" or "compute_xy" to include both virtual and' ' binary GPU code, or as "sm_xy" to only include the binary ' 'code.\nPlease note that each additional compute capability ' 'significantly increases your build time and binary size, and that '
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