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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 2. Running the CI scripts locally, as explained below
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SECURITY.md
ways to detect malicious models/graphs/checkpoints, so the recommended way to mitigate the risk in this scenario is to sandbox the model execution. ### Hardware attacks Physical GPUs or TPUs can also be the target of attacks. [Published research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it might be possible to use side channel attacks on the GPU to leak data from other
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CITATION.cff
state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives 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...
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