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RELEASE.md
* Add `RFFT2D` as builtin op. (`RFFT2D` also supports `RFFTD`.) Currently only supports float32 input. * Add 5D support to `SLICE` op. * TFLite Supports SingatureDef: * TFLiteConverter exports models with SignatureDef * Interpreter supports getting a list of signatures and getting callable function for a given signaturedef.
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SECURITY.md
to the network can execute arbitrary code with the privileges of the user running the `tf.train.Server`. ## Untrusted inputs during training and prediction TensorFlow supports a wide range of input data formats. For example it can process images, audio, videos, and text. There are several modules specialized in taking those formats, modifying them, and/or converting them to intermediate
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CITATION.cff
flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research....
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ISSUES.md
1. The issue should be related to the repo it is created in. **Here's why we have this policy:** We want to focus on the work that benefits the whole community, e.g., fixing bugs and adding features. Individual support should be sought on Stack Overflow or other non-GitHub channels. It helps us to
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.github/ISSUE_TEMPLATE/tflite-other.md
body: - type: dropdown id: issue-type attributes: label: Issue Type description: What type of issue would you like to report? multiple: false options: - Bug - Build/Install - Performance - Support - Feature Request - Documentation Feature Request - Documentation Bug - Others validations: required: true - type: markdown attributes: value: | Please make sure that this is a bug. As per our
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ci/official/requirements_updater/README.md
dependencies are always used. \ See https://github.com/bazelbuild/rules_python/ for more details. ### Specifying the Python version Note: Only a number of minor Python versions are supported at any given time. 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 ```
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
- Model produces wrong results and/or has lesser accuracy. - Model produces correct results, but it is slower than expected. ### 4. (optional) RNN conversion support If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title. ### 5. (optional) Any other info / logs
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tensorflow/c/experimental/filesystem/BUILD
srcs = ["modular_filesystem_test.cc"], linkopts = ["-ldl"], tags = [ "manual", # Requires DSOs as arguments, eventual setup "notap", # b/139060984, requires implementing modular support for Google filesystem ], deps = [ ":modular_filesystem", "//tensorflow/core:framework_internal", "//tensorflow/core/lib/io:path", "//tensorflow/core/platform:env",
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ci/official/README.md
- Different Python versions - Linux, MacOS, and Windows machines (these pool definitions are internal) - x86 and arm64 - CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs ## How to Test Your Changes to TensorFlow You may check how your changes will affect TensorFlow by: 1. Creating a PR and observing the presubmit test results
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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 which case remember to change the directory from `/root` to `/tensorflow`
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