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ci/official/README.md
# # Here is how to choose your TFCI value: # 1. A Python version must come first, because other scripts reference it. # Ex. py39 -- Python 3.9 # Ex. py310 -- Python 3.10 # Ex. py311 -- Python 3.11 # Ex. py312 -- Python 3.12 # 2. Choose the platform, which corresponds to the version of TensorFlow to # build. This should also match the system you're using--you cannot build
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tensorflow/c/eager/dlpack.cc
// There are two ways to represent compact row-major data // 1) nullptr indicates tensor is compact and row-majored. // 2) fill in the strides array as the real case for compact row-major data. // Here we choose option 2, since some frameworks didn't handle the strides // argument properly. dlm_tensor->dl_tensor.strides = stride_arr->data(); dlm_tensor->dl_tensor.byte_offset =
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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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tensorflow/c/eager/c_api_test.cc
CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); EXPECT_EQ(-1, TFE_OpGetInputLength(identityOp, "cheese", status)); CHECK_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status)) << TF_Message(status); EXPECT_EQ(-1, TFE_OpGetOutputLength(identityOp, "cheese", status)); CHECK_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); TFE_DeleteOp(identityOp);
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configure.py
environ_cp['TF_NEED_CLANG'] = str(choose_compiler(environ_cp)) if environ_cp.get('TF_NEED_CLANG') == '1': clang_compiler_path = set_clang_compiler_path(environ_cp) clang_version = retrieve_clang_version(clang_compiler_path) disable_clang_offsetof_extension(clang_version) if is_windows(): environ_cp['TF_NEED_CLANG'] = str(choose_compiler_Win(environ_cp))
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tensorflow/c/eager/c_api.cc
return status.status; } absl::StatusOr<bool> ShallPinToThisDevice( const ImmediateExecutionOperation* op) override { TF_Status status; // Let this custom device choose the device to pin this op on if it // implements the pinning function. if (device_.shall_pin_to_this_device != nullptr) { return device_.shall_pin_to_this_device(tensorflow::wrap(op), &status); }
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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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ci/official/containers/linux_arm64/builder.devtoolset/gcc9-fixups.patch
@@ -49,23 +49,11 @@ elf_machine_load_address (void) /* To figure out the load address we use the definition that for any symbol: dynamic_addr(symbol) = static_addr(symbol) + load_addr - The choice of symbol is arbitrary. The static address we obtain - by constructing a non GOT reference to the symbol, the dynamic - address of the symbol we compute using adrp/add to compute the
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RELEASE.md
as arg with default value `None` to choose the I/O device for loading models and weights. * Update `tf.saved_model.SaveOptions` with [`experimental_io_device`](https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/saved_model/SaveOptions?hl=en) as arg with default value `None` to choose the I/O device for saving models and weights.
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ci/official/pycpp.sh
else tfrun bazel test $TFCI_BAZEL_COMMON_ARGS --profile "$TFCI_OUTPUT_DIR/profile.json.gz" --config="${TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX}_pycpp_test" 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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