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ci/official/bisect.sh
# export TF_ANY_MODE=test set -euxo pipefail cd "$(dirname "$0")/../../" # tensorflow/ export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')" git bisect start "$TF_BISECT_BAD" "$TF_BISECT_GOOD"
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ci/official/utilities/code_check_changed_files.bats
# See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== setup_file() { bazel version # Start the bazel server # Fixes "fatal: detected dubious ownership in repository" for Docker. git config --system --add safe.directory '*' git config --system protocol.file.allow always
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CONTRIBUTING.md
automatically on GitHub. If you want to contribute, start working through the TensorFlow codebase, 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.
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ci/official/README.md
# which mounts your TensorFlow directory into the container. # # Important: because the container is persistent, you cannot change TFCI # variables in between script executions. To forcibly remove the # container and start fresh, run "docker rm -f tf". Removing the container # destroys some temporary bazel data and causes longer builds. # # You will need the NVIDIA Container Toolkit for GPU testing:
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tensorflow/c/c_api.cc
out_shape_and_type->set_dtype(p.dtype); *out_shape_and_type->mutable_type() = p.type; } } string str_data; handle_data.SerializeToString(&str_data); TF_Buffer* result = TF_NewBufferFromString(str_data.c_str(), str_data.size()); return result; } void TF_SetHandleShapeAndType(TF_Graph* graph, TF_Output output,
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tensorflow/c/experimental/gradients/math_grad.cc
std::string name = "Conj_Exp_Grad"; TF_RETURN_IF_ERROR(SafeConj(ctx, exp_.get(), &conj_output, name.c_str())); AbstractTensorHandlePtr conj_output_releaser(conj_output); name = "Mul_Exp_Grad"; TF_RETURN_IF_ERROR( Mul(ctx, conj_output, grad_outputs[0], &grad_inputs[0], name.c_str())); return absl::OkStatus(); } ~ExpGradientFunction() override {} private:
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tensorflow/c/eager/c_api_experimental.cc
for (const auto& op : op_names) { op_names_oss << op << ", "; } const std::string& op_names_str = op_names_oss.str(); void* data = tensorflow::port::Malloc(op_names_str.length()); op_names_str.copy(static_cast<char*>(data), op_names_str.length(), 0); buf->data = data; buf->length = op_names_str.length(); buf->data_deallocator = [](void* data, size_t length) { tensorflow::port::Free(data); };
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tensorflow/c/eager/c_api_test_util.cc
TFE_OpSetAttrString(op, "tensor_name", "dummy", 5); TFE_OpSetAttrString(op, "send_device", send_device.c_str(), send_device.size()); TFE_OpSetAttrString(op, "recv_device", recv_device.c_str(), recv_device.size()); TFE_OpSetAttrInt(op, "send_device_incarnation", send_device_incarnation); return op; }
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tensorflow/c/eager/c_api_unified_experimental_graph.cc
// this may not be consistent with python's naming policy. mutex_lock l(g_->mu); op_.reset(new TF_OperationDescription(g_, op_type_.c_str(), g_->graph.NewName(op_name).c_str())); return absl::OkStatus(); } const string& Name() const override { return op_type_; } const string& DeviceName() const override { return device_name_; }
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ci/official/libtensorflow.sh
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