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.github/ISSUE_TEMPLATE/tflite-other.md
required: true - type: textarea id: code-to-reproduce attributes: label: Standalone code to reproduce the issue description: Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/Jupyter/any notebook. placeholder: Tell us what you see! value: render: shell validations: required: true
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.github/ISSUE_TEMPLATE/tflite-in-play-services.md
- Google Play Services version (`Settings` > `Apps` > `Google Play Services` > `App details`): **Standalone code to reproduce the issue** Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to or attach code demonstrating the problem. **Any other info / logs** Include any logs or source code that would be helpful to diagnose the problem.
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.github/ISSUE_TEMPLATE/tflite-op-request.md
**Provide the text output from tflite_convert** ``` # Copy and paste here ``` **Standalone code to reproduce the issue** Provide a reproducible test case that is the bare minimum necessary to generate the problem. If possible, please share a link to Colab/Jupyter/any notebook. Also, please include a link to a GraphDef or the model if possible. **Any other info / logs**
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.github/ISSUE_TEMPLATE/tensorflow_issue_template.yaml
required: true - type: textarea id: code-to-reproduce attributes: label: Standalone code to reproduce the issue description: Provide a reproducible test case that is the bare minimum necessary to generate the problem. Please share a link to Colab, Jupyter, or any notebook. placeholder: Tell us what you see! value: render: shell validations: required: true
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.bazelrc
# Build with the AVX instruction set when on macOS x86 build:release_macos_x86 --config=avx_linux build:release_macos_x86 --cpu=darwin # Target Catalina as the minimum compatible OS version build:release_macos_x86 --macos_minimum_os=10.15 build:release_macos_x86 --action_env MACOSX_DEPLOYMENT_TARGET=10.15 # Build configs for macOS Arm64 build:release_macos_arm64 --config=release_macos_base
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tensorflow/c/eager/c_api_test_util.h
tensorflow::uint64 send_device_incarnation); // Return a 1-D INT32 tensor containing a single value 1. TFE_TensorHandle* TestAxisTensorHandle(TFE_Context* ctx); // Return an op taking minimum of `input` long `axis` dimension. TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input, TFE_TensorHandle* axis); // If there is a device of type `device_type`, returns true
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tensorflow/c/c_api_experimental.h
// Returns whether the single GPU or general XLA auto jit optimizations are // enabled through MarkForCompilationPassFlags. TF_CAPI_EXPORT unsigned char TF_GetXlaAutoJitEnabled(); // Sets XLA's minimum cluster size. This has global effect. TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size); // Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
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RELEASE.md
* TensorFlow 1.7 may be the last time we support Cuda versions below 8.0. Starting with TensorFlow 1.8 release, 8.0 will be the minimum supported version. * TensorFlow 1.7 may be the last time we support cuDNN versions below 6.0. Starting with TensorFlow 1.8 release, 6.0 will be the minimum supported version. ## Thanks to our Contributors
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tensorflow/c/eager/c_api_experimental.h
// executed in sequence. Assigning nodes to different executors allows executing // nodes in parallel. // in_flight_nodes_limit: when is_async is true, this value controls the // maximum number of in flight async nodes. Enqueuing of additional async ops // after the limit is reached blocks until some inflight nodes finishes. // The effect is bounding the memory held by inflight TensorHandles that are
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SECURITY.md
your report: - A descriptive title - Your name and affiliation (if any). - A description of the technical details of the vulnerabilities. - A minimal example of the vulnerability. It is very important to let us know how we can reproduce your findings. For memory corruption triggerable in TensorFlow models, please demonstrate an exploit against one of Alphabet's
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