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ci/official/utilities/get_versions.sh
# nightly job. update_version.py affects TF_VER_SUFFIX, TF_VER_PYTHON, and # TF_VER_FULL. # Note: in awk, the command '/search/ {commands}' applies the commands to any line that # matches the /search/ regular expression. "print $N" prints the Nth "field", # where fields are strings separated by whitespace. export TF_VER_MAJOR=$(awk '/#define TF_MAJOR_VERSION/ {print $3}' tensorflow/core/public/version.h)
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tensorflow/c/eager/dlpack.cc
// TFE_NewTensorHandleFromDeviceMemory. void DeallocatorWrapperFunc(void* data, size_t len, void* dlmt_vptr) { TFE_CallDLManagedTensorDeleter(dlmt_vptr); } // Checks whether the stride array matches the layout of compact, row-majored // data. bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr, int ndim) { bool valid = true;
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CONTRIBUTING.md
![image](https://github.com/tensorflow/tensorflow/assets/52792999/3eea4ca5-daa0-4570-b0b5-2a2b03a724a3) ### Contributor License Agreements We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles. Please fill out either the individual or corporate Contributor License Agreement (CLA).
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ci/official/requirements_updater/requirements.in
dill == 0.3.7 astor == 0.7.1 typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version # Note that we must use nightly here as these are used in nightly jobs # For release jobs, we will pin these on the release branch keras-nightly ~= 3.0.0.dev tb-nightly ~= 2.17.0.a
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RELEASE.md
* `tf.distribute.experimental.MultiWorkerMirroredStrategy` adds support for partial batches. Workers running out of data now continue to participate in the training with empty inputs, instead of raising an error. Learn more about [partial batches here](https://www.tensorflow.org/tutorials/distribute/input#partial_batches). * Improve the performance of reading metrics eagerly under
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tensorflow/c/eager/tape.h
} // Returns whether any tensor in a list of tensors is being watched and has // a trainable dtype. bool ShouldRecord(absl::Span<const int64_t> tensor_ids, absl::Span<const tensorflow::DataType> dtypes) const; // Adds this tensor to the list of watched tensors. // // This is a no-op if the tensor is already being watched either from an
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
if (!status->status.ok()) { return nullptr; } TF_DataType dtype = TFE_TensorHandleDataType(components[0].get()); // Verify that the combined TensorHandle's dtype matches all of the component // dtypes. for (TensorHandlePtr& component : components) { TFE_TensorHandleGetStatus(component.get(), status); if (!status->status.ok()) { return nullptr; }
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