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tensorflow/c/eager/c_api_unified_experimental_test.cc
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
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ci/official/README.md
# Ex. macos_arm64 -- arm64 MacOS platform # 3. Add modifiers. Some modifiers for local execution are: # Ex. disk_cache -- Use a local cache # Ex. public_cache -- Use TF's public cache (read-only) # Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only) # Ex. rbe -- Use RBE for faster builds (Googlers only; see below) # Ex. no_docker -- Disable docker on enabled platforms
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tensorflow/c/eager/gradient_checker.h
* parameter specified by `input_index`. * * I.e. if y = <output of the forward model> and w = inputs[input_index], * this will calculate dy/dw numerically. * * `use_function` indicates whether to use graph mode(true) or eager(false). * * `numerical_grad` is the pointer to the AbstractTensorHandle* which will * hold the numerical gradient data at the end of the function. */
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.github/bot_config.yml
Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below: * Try Google Colab to use TensorFlow. * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install``` to install any other preferred TF version.
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tensorflow/c/experimental/gradients/array_grad_test.cc
/*tfrt*/ ::testing::Values(false), /*use_function*/ ::testing::Values(true, false))); #else INSTANTIATE_TEST_SUITE_P( UnifiedCAPI, CppGradients, ::testing::Combine(::testing::Values("graphdef", "mlir"), /*tfrt*/ ::testing::Values(false), /*use_function*/ ::testing::Values(true, false))); #endif } // namespace
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.github/ISSUE_TEMPLATE/tflite-in-play-services.md
--- name: TensorFlow Lite in Play Services issue about: Use this template for issues with TensorFlow Lite in Google Play Services labels: 'comp:lite-in-play-services' --- **System information** - Android Device information (use `adb shell getprop ro.build.fingerprint` if possible): - TensorFlow Lite in Play Services SDK version (found in `build.gradle`): - Google Play Services version
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.github/workflows/release-branch-cherrypick.yml
# and click "Run Workflow." Leave "Use Workflow From" set to "master", then # input the branch name and paste the cherry-pick commit and click Run. A PR # will be created. name: Release Branch Cherrypick on: workflow_dispatch: inputs: # We use this instead of the "run on branch" argument because GitHub looks
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tensorflow/c/experimental/grappler/grappler.h
/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
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ci/official/requirements_updater/README.md
By default, the lowest supported version is used. To set a different version, use the `TF_PYTHON_VERSION` environment variable, e.g. ``` export TF_PYTHON_VERSION=3.11 ``` To specify the version via a Bazel command argument, use the following: ``` --repo_env=TF_PYTHON_VERSION=3.11 ``` ## Requirements updater
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CONTRIBUTING.md
for the required packages. Alternatively, use the said [tensorflow/build Docker images](https://hub.docker.com/r/tensorflow/build) (`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` are no longer supported for) development. Use TF SIG Build Dockerfiles in development to avoid installing the packages directly on your system (in
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