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requirements_lock_3_10.txt
absl-py==2.1.0 \ --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \ --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff # via # keras-nightly # tb-nightly astor==0.7.1 \ --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \ --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e # via -r requirements.in astunparse==1.6.3 \
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ci/official/utilities/extract_resultstore_links.py
f'Bazel invocations.\n' f'ResultStore contains individual representations of each target ' f'that were run/built during the invocation.\n' f'These results are generally easier to read than looking through ' f'the entire build log:\n') i = 1 for url, invocation_results in result_store_dict.items(): line_str = f'Invocation #{i} ({invocation_results["status"]}):\n'
Python - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Nov 08 17:50:27 GMT 2023 - 10.9K bytes - Viewed (0) -
tensorflow/c/c_api_function.cc
// does various checks while doing so. `input_nodes` will contain the same // information as input_tensors just in a different structure to make // following processing easier. TODO(iga): Simplify this nested structure. Status ProcessInputs( const TF_Graph* fn_body, const char* fn_name, int ninputs, const TF_Output* inputs, std::vector<OutputTensor>* input_tensors,
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 13.6K bytes - Viewed (2) -
tensorflow/c/eager/parallel_device/parallel_device.cc
if (TF_GetCode(status) != TF_OK) return nullptr; if (parallel_tensor->num_tensors() == 1) { // Copy-off for single-device tensors is allowed to make debugging dynamic // control flow easier. return TFE_TensorHandleCopySharingTensor(parallel_tensor->tensor(0), status); } else { TF_SetStatus( status, TF_UNIMPLEMENTED, absl::StrCat(
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Mar 29 22:05:31 GMT 2023 - 18.3K bytes - Viewed (0) -
requirements_lock_3_12.txt
absl-py==2.1.0 \ --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \ --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff # via # keras-nightly # tb-nightly astor==0.7.1 \ --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \ --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e # via -r requirements.in astunparse==1.6.3 \
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requirements_lock_3_9.txt
absl-py==2.1.0 \ --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \ --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff # via # keras-nightly # tb-nightly astor==0.7.1 \ --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \ --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e # via -r requirements.in astunparse==1.6.3 \
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ci/official/utilities/cleanup_summary.sh
function resultstore_extract_fallback { # In case the main script fails somehow. cat <<EOF IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you can view more detailed results that are probably easier to read than this log. Try the links below: EOF # Find any "Streaming build results to" line, then print the last word in it, # and don't print duplicates
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CONTRIBUTING.md
navigate to the [GitHub "issues" tab](https://github.com/tensorflow/tensorflow/issues) and start looking through interesting issues. If you are not sure of where to start, then start by trying one of the smaller/easier issues here i.e. [issues with the "good first issue" label](https://github.com/tensorflow/tensorflow/labels/good%20first%20issue) and then take a look at the
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ci/official/requirements_updater/requirements.in
numpy ~= 1.23.5 ; python_version <= "3.11" numpy ~= 1.26.0 ; python_version >= "3.12" wheel ~= 0.41.2 h5py >= 3.10.0 lit ~= 17.0.2 opt_einsum == 3.3.0 astunparse == 1.6.3 dill == 0.3.7 astor == 0.7.1 typing_extensions == 4.8.0 gast == 0.4.0 termcolor == 2.3.0 wrapt == 1.16.0 tblib == 2.0.0 # Install tensorboard, and keras # Note that here we want the latest version that matches TF major.minor version
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SECURITY.md
Eager mode lets users write imperative-style statements that can be easily inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface) are skipped, and any checks implemented inside the shape inference code are not executed.
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