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.github/workflows/trusted-partners.yml
permissions: # Needed to attach tags into the PR issues: write contents: write pull-requests: write if: | github.event.pull_request.draft == false && github.event.sender.type == 'User' steps: - name: Checkout repo uses: actions/checkout@755da8c3cf115ac066823e79a1e1788f8940201b # v3.2.0 - name: Trusted-Partners-PR
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Tue Sep 12 14:49:29 GMT 2023 - 2.4K bytes - Viewed (0) -
SECURITY.md
`SavedModel`) and executes them in parallel on available executors. Running TensorFlow in a multitenant design mixes the risks described above with the inherent ones from multitenant configurations. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks. ### Tenant isolation Since any tenants or users providing models, graphs or checkpoints can execute
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RELEASE.md
replaced with an alternative API in `tf.contrib.seq2seq`. * Intel MKL Integration (https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture). Intel developed a number of optimized deep learning primitives: In addition to matrix multiplication and convolution, these building blocks include:
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tensorflow/c/c_api_experimental.cc
status->status = tensorflow::errors::NotFound( op_name, " does not have number_attr() defined."); return nullptr; } // The returned string is owned by OpRegistry, so liveness is not a concern. return input_arg.number_attr().c_str(); } int TF_OpIsStateful(const char* op_type, TF_Status* status) { const tensorflow::OpRegistrationData* op_reg_data; status->status =
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configure.py
elif is_windows(): default_cc_opt_flags = '/arch:AVX' else: # On all other platforms, no longer use `-march=native` as this can result # in instructions that are too modern being generated. Users that want # maximum performance should compile TF in their environment and can pass # `-march=native` there. # See https://github.com/tensorflow/tensorflow/issues/45744 and duplicates
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