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tensorflow/c/experimental/filesystem/modular_filesystem.cc
// Note that `matches` is allocated by the plugin and freed by core // TensorFlow, so we need to use `plugin_memory_free_` here. char** matches = nullptr; const int num_matches = ops_->get_matching_paths( filesystem_.get(), pattern.c_str(), &matches, plugin_status.get()); if (num_matches >= 0) { for (int i = 0; i < num_matches; i++) { result->push_back(std::string(matches[i]));
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Sep 06 19:12:29 GMT 2023 - 23.1K bytes - Viewed (0) -
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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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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tensorflow/c/eager/dlpack.cc
} } // Wraps the deleter function of DLManagedTensor to match the function signature // 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.
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 12.8K bytes - Viewed (0) -
tensorflow/c/c_api_test.cc
TF_DeleteSessionOptions(opts); TF_Output feeds[] = {TF_Output{a, 0}, TF_Output{b, 0}}; TF_Output fetches[] = {TF_Output{plus2, 0}, TF_Output{plusB, 0}}; const char* handle = nullptr; TF_SessionPRunSetup(sess, feeds, TF_ARRAYSIZE(feeds), fetches, TF_ARRAYSIZE(fetches), nullptr, 0, &handle, s); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); // Feed A and fetch A + 2.
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 96.9K bytes - Viewed (3) -
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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ci/official/containers/linux_arm64/builder.devtoolset/build_devtoolset.sh
rpm2cpio "devtoolset-10-gcc-10.2.1-11.2.el7.src.rpm" |cpio -idmv tar -xvf "gcc-10.2.1-20210130.tar.xz" --strip 1 ;; esac # Apply the devtoolset patches to gcc. /rpm-patch.sh "gcc.spec" ./contrib/download_prerequisites mkdir -p "${TARGET}-build" cd "${TARGET}-build" "${TARGET}-src/configure" \ --prefix="${TARGET}/usr" \
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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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SECURITY.md
potentially exploit a vulnerability in the TensorFlow code that handles videos, which could allow them to execute arbitrary code on the system running TensorFlow. It is important to keep TensorFlow up to date with the latest security patches and follow the sandboxing guideline above to protect against these types of vulnerabilities. ## Security properties of execution modes TensorFlow has several execution modes, with Eager-mode being the default in v2.
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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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