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CONTRIBUTING.md
debugging information with `--config=dbg`, as issues occur on Linux if there is too much debug info (see [this GitHub issue](https://github.com/tensorflow/tensorflow/issues/48919) for context). If you want to debug a kernel, you can compile specific files with `-g` using the `--per_file_copt` bazel option. For example, if you want to debug the Identity op, which are in files starting with `identity_op`, you can run ```bash
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tensorflow/c/c_api.cc
*node_def = node->def(); } } *graph_def.mutable_library() = graph.flib_def().ToProto(); if (flags::Global().more_stack_traces.value()) { *graph_def.mutable_debug_info() = graph.BuildDebugInfo(); } session->graph->mu.unlock(); status->status = session->session->Extend(std::move(graph_def)); if (!status->status.ok()) {
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tensorflow/c/eager/c_api_experimental.h
TF_Status* status); // Get a comma-separated list of op names executed in graph functions dispatched // to `ctx`. This feature is currently only enabled for TFRT debug builds, for // performance and simplicity reasons. TF_CAPI_EXPORT extern void TFE_GetExecutedOpNames(TFE_Context* ctx, TF_Buffer* buf,
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tensorflow/c/eager/BUILD
], deps = [ ":immediate_execution_operation", "//tensorflow/c:conversion_macros", ], ) cc_library( name = "tfe_tensor_debug_info_internal", hdrs = ["tfe_tensor_debug_info_internal.h"], visibility = ["//visibility:private"], deps = [ "//tensorflow/core:lib", ], ) cc_library( name = "tfe_tensorhandle_internal",
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tensorflow/c/eager/c_api_experimental.cc
"Unable to find function with name: ", function_name); return; } tensorflow::GraphDebugInfo debug_info = tensorflow::StackTracesMapToGraphDebugInfo( function_record->stack_traces()); string str = debug_info.SerializeAsString(); void* data = tensorflow::port::Malloc(str.length()); str.copy(static_cast<char*>(data), str.length(), 0); buf->data = data;
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tensorflow/BUILD
visibility = ["//visibility:public"], ) config_setting( name = "linux_riscv64", values = {"cpu": "riscv64"}, visibility = ["//visibility:public"], ) config_setting( name = "debug", values = { "compilation_mode": "dbg", }, visibility = ["//visibility:public"], ) config_setting( name = "optimized", values = { "compilation_mode": "opt",
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RELEASE.md
* tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as `map`. The debug mode can be enabled through `tf.data.experimental.enable_debug_mode()`. * `tf.lite`
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tensorflow/c/c_api_experimental.cc
tensorflow::mutex_lock c(graph->mu); const auto& debug_str = graph->graph.ToGraphDefDebug().DebugString(); *len = debug_str.size(); char* ret = static_cast<char*>(malloc(*len + 1)); memcpy(ret, debug_str.c_str(), *len + 1); return ret; } char* TF_FunctionDebugString(TF_Function* func, size_t* len) { const auto& debug_str = DebugString(func->record->fdef()); *len = debug_str.size();
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.bazelrc
build:nvcc_clang --@local_config_cuda//:cuda_compiler=nvcc # Debug config build:dbg -c dbg # Only include debug info for files under tensorflow/, excluding kernels, to # reduce the size of the debug info in the binary. This is because if the debug # sections in the ELF binary are too large, errors can occur. See # https://github.com/tensorflow/tensorflow/issues/48919. # Users can still include debug info for a specific kernel, e.g. with:
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ci/official/utilities/code_check_full.bats
//tensorflow/python:extra_py_tests_deps //tensorflow/python:mixed_precision //tensorflow/python:tf_optimizer //tensorflow/python:compare_test_proto_py //tensorflow/python/framework:test_file_system.so //tensorflow/python/debug:grpc_tensorflow_server.par //tensorflow/python/feature_column:vocabulary_testdata //tensorflow/python/util:nest_test_main_lib //tensorflow/lite/experimental/examples/lstm:rnn_cell
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