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ci/official/envs/rbe
EOF exit 1 fi TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" # These flags share the user's gcloud credentials with the container, so that bazel # inside the container can authenticate. Note: TF's CI does not have any credential # stored here.
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ci/official/requirements_updater/README.md
as a rough example of the steps below. All the files referenced below are located in the same directory as this README, unless indicated otherwise. 1) Add the new version to the `VERSIONS` variable inside `tensorflow/tools/toolchains/python/python_repo.bzl`. \ While this isn't necessary for running the updater, it is required for actually using the new version with Tensorflow.
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SECURITY.md
Since models are practically programs that TensorFlow executes, using untrusted models or graphs is equivalent to running untrusted code. If you need to run untrusted models, execute them inside a [**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions in TensorFlow ops can be recognized as security issues only if they are reachable and exploitable through production-grade, benign models.
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RELEASE.md
* `conversion_params` is now deprecated inside `TrtGraphConverterV2` in favor of direct arguments: `max_workspace_size_bytes`, `precision_mode`, `minimum_segment_size`, `maximum_cached_engines`, `use_calibration` and `allow_build_at_runtime`. * Added a new parameter called `save_gpu_specific_engines` to the `.save()` function inside `TrtGraphConverterV2`. When `False`, the
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ci/devinfra/docker_windows/Dockerfile
# Workaround for networking (b/112379377) was closed as won't fix for MTU setting. # Remaining lines handle making the metadata server on the VM accessible inside docker. RUN Get-NetAdapter | Where-Object Name -like "*Ethernet*" | ForEach-Object { \ & netsh interface ipv4 set subinterface $_.InterfaceIndex mtu=1460 store=persistent }; \
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.bazelrc
# See https://developer.nvidia.com/cuda-gpus#compute # `compute_XY` enables PTX embedding in addition to SASS. PTX # is forward compatible beyond the current compute capability major # release while SASS is only forward compatible inside the current # major release. Example: sm_80 kernels can run on sm_89 GPUs but # not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs.
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