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ci/official/pycpp.sh
fi # Note: the profile can be viewed by visiting chrome://tracing in a Chrome browser. # See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling
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.github/ISSUE_TEMPLATE/tflite-other.md
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 [GitHub Policy](https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md),we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub.
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build --distinct_host_configuration=false # Store performance profiling log in the mounted artifact directory. # The profile can be viewed by visiting chrome://tracing in a Chrome browser. # See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling build --profile=/tf/pkg/profile.json.gz # Use the rebuilt gcc toolchain to compile for manylinux2014
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ISSUES.md
If you open a GitHub Issue, here is our policy: 1. It must be a bug/performance issue or a feature request or a build issue or a documentation issue (for small doc fixes please send a PR instead). 1. Make sure the Issue Template is filled out. 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
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build --copt=-Wno-gnu-offsetof-extensions # Store performance profiling log in the mounted artifact directory. # The profile can be viewed by visiting chrome://tracing in a Chrome browser. # See https://docs.bazel.build/versions/main/skylark/performance.html#performance-profiling build --profile=/tf/pkg/profile.json.gz # Use the rebuilt gcc toolchain to compile for manylinux2014
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SECURITY.md
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for internal communication only. It is not built for use in untrusted environments or networks. For performance reasons, the default TensorFlow server does not include any authorization protocol and sends messages unencrypted. It accepts connections from anywhere, and executes the graphs it is sent without performing any checks.
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.github/ISSUE_TEMPLATE/tensorflow_issue_template.yaml
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:
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RELEASE.md
replicas taking part in sync training. * Performance improvements for GPU multi-worker distributed training using `tf.distribute.experimental.MultiWorkerMirroredStrategy` * Update NVIDIA `NCCL` to `2.5.7-1` for better performance and performance tuning. Please see
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tensorflow/c/experimental/filesystem/plugins/gcs/expiring_lru_cache.h
// is okay, as stat requests are typically fast, and concurrent requests are // often for the same file. Future work can split this up into one lock per // key if this proves to be a significant performance bottleneck. absl::MutexLock lock(&mu_); if (LookupLocked(key, value)) { return TF_SetStatus(status, TF_OK, ""); } compute_func(key, value, status); if (TF_GetCode(status) == TF_OK) {
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tensorflow/c/eager/immediate_execution_context.h
DEVICE_PLACEMENT_EXPLICIT = 0, // Copy the tensor to the right device but log a warning. DEVICE_PLACEMENT_WARN = 1, // Silently copy the tensor, which has a performance cost since the operation // will be blocked till the copy completes. This is the default policy. DEVICE_PLACEMENT_SILENT = 2, // Placement policy which silently copies int32 tensors but not other dtypes.
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