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ci/official/requirements_updater/requirements.in
# TODO(b/304751256): Adjust the numpy pin to a single version, when ready numpy ~= 1.23.5 ; python_version <= "3.11" numpy ~= 1.26.0 ; python_version >= "3.12" wheel ~= 0.41.2 h5py >= 3.10.0 lit ~= 17.0.2 opt_einsum == 3.3.0 astunparse == 1.6.3 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
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tensorflow/c/eager/c_api_experimental.h
// Pins the op to `device` based on inputs to `op`. Returns true // signifying to pin to the current custom device. Returns false // to pin to the physical device. // // This function is guaranteed to be called only when all of the custom-device // inputs are on this device. bool (*shall_pin_to_this_device)(const TFE_Op* op, TF_Status* s) = nullptr; } TFE_CustomDevice;
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tensorflow/c/eager/c_api.cc
const ImmediateExecutionOperation* op) override { TF_Status status; // Let this custom device choose the device to pin this op on if it // implements the pinning function. if (device_.shall_pin_to_this_device != nullptr) { return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status); } return errors::Unimplemented("No custom device pinning implementation."); }
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RELEASE.md
already installed on the system. * Using .post1 instead of a full minor release allowed us to push this release out quickly. However, please note the following caveat: * For users wishing to pin their Python dependency in a requirements file or other situation, under Python's version specification rules, `tensorflow[and-cuda]==2.15.0` will not install this fixed version.
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