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.github/workflows/stale-issues.yml
close-issue-reason: completed # List of labels to remove when issues/PRs unstale. labels-to-remove-when-unstale: "stat:contribution welcome,stat:good first issue" any-of-labels: "stat:contribution welcome,stat:good first issue" stale-issue-message: > This issue is stale because it has been open for 180 days with no activity.
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.github/bot_config.yml
* 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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CONTRIBUTING.md
[issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue) and then take a look at the [issues with the "contributions welcome" label](https://github.com/tensorflow/tensorflow/labels/stat%3Acontributions%20welcome). These are issues that we believe are particularly well suited for outside contributions, often because we probably won't get to them right now. If you
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.bazelrc
# Don't trigger --config=<host platform> when cross-compiling. build:android --noenable_platform_specific_config build:ios --noenable_platform_specific_config # Suppress all C++ compiler warnings, otherwise build logs become 10s of MBs. build:android --copt=-w build:ios --copt=-w build:linux --host_copt=-w build:macos --copt=-w build:windows --copt=/W0 build:windows --host_copt=/W0
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tensorflow/c/eager/c_api_experimental.h
TF_Status* status); // Set configuration key and value using coordination service. // If coordination service is enabled, the key-value will be stored on the // leader and become accessible to all workers in the cluster. // Currently, a config key can only be set with one value, and subsequently // setting the same key will lead to errors. //
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RELEASE.md
`jit_compile=True` may not necessarily work for all models. * Added DTensor-related Keras APIs under `tf.keras.dtensor` namespace. The APIs are still classified as experimental. You are welcome to try it out. Please check the tutorial and guide on https://www.tensorflow.org/ for more details about DTensor. * `tf.lite`:
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tensorflow/c/experimental/filesystem/modular_filesystem.h
/// For documentation on these methods, consult `core/platform/filesystem.h`. namespace tensorflow { // TODO(b/143949615): After all filesystems are converted, this file will be // moved to core/platform, and this class can become a singleton and replace the // need for `Env::Default()`. At that time, we might decide to remove the need // for `Env::Default()` altogether, but that's a different project, not in
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tensorflow/c/c_api_function.cc
fn_name, "'"); output_tensors->emplace_back(node, idx); } return absl::OkStatus(); } // Populates `body_nodes` with the nodes that will become function's body. // Performs various checks. Status ComputeBodyNodes( const TF_Graph* fn_body, const char* fn_name, int num_opers, const TF_Operation* const* opers,
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tensorflow/c/c_api.h
// the nodes in the graph (except for the automatic skipping, see // below). // opers - Array of operations to become the body of the function or null. // - If no array is given (`num_opers` = -1), all the // operations in `fn_body` will become part of the function // except operations referenced in `inputs`. These operations
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