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tensorflow/c/experimental/filesystem/modular_filesystem.cc
std::unique_ptr<RandomAccessFile>* result) { if (ops_->new_random_access_file == nullptr) return errors::Unimplemented(tensorflow::strings::StrCat( "Filesystem for ", fname, " does not support NewRandomAccessFile()")); UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus); auto file = std::make_unique<TF_RandomAccessFile>(); std::string translated_name = TranslateName(fname);
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Sep 06 19:12:29 GMT 2023 - 23.1K bytes - Viewed (0) -
.github/ISSUE_TEMPLATE/tflite-other.md
body: - type: dropdown 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: value: | Please make sure that this is a bug. As per our
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ci/official/containers/linux_arm64/builder.devtoolset/gcc9-fixups.patch
{ @@ -332,6 +333,7 @@ unsetenv (const char *name) } else ++ep; + } UNLOCK; diff --git a/support/Makefile b/support/Makefile index a253698..2f4e2a9 100644 --- a/support/Makefile +++ b/support/Makefile @@ -167,13 +167,6 @@ CFLAGS-support_paths.c = \ -DINSTDIR_PATH=\"$(prefix)\" \ -DLIBDIR_PATH=\"$(libdir)\" -ifeq (,$(CXX)) -LINKS_DSO_PROGRAM = links-dso-program-c
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RELEASE.md
* <NOTES SHOULD BE GROUPED PER AREA> * `tf.lite` * Added support for `stablehlo.gather`. * Added support for `stablehlo.add`. * Added support for `stablehlo.multiply`. * Added support for `stablehlo.maximum`. * Added support for `stablehlo.minimum`. * Added boolean parameter support for `tfl.gather_nd`. * C API: * New API functions:
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.github/bot_config.yml
* Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup). * If error still persists then, apparently your CPU model does not support AVX instruction sets. * Refer [hardware requirements](https://www.tensorflow.org/install/pip#hardware-requirements). -----------------------------------------------------------------------------------------------
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ci/official/envs/linux_arm64
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python TFCI_DOCKER_PULL_ENABLE=1
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tensorflow/BUILD
config_setting( name = "stackdriver_support", define_values = {"stackdriver_support": "true"}, visibility = ["//visibility:public"], ) config_setting( name = "with_xla_support", define_values = {"with_xla_support": "true"}, visibility = ["//visibility:public"], ) # By default, XLA GPU is compiled into tensorflow when building with # --config=cuda even when `with_xla_support` is false. The config setting
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.bazelrc
# v2: Build TF v2 # # Feature and Third party library support options: # xla: Build TF with XLA # tpu: Build TF with TPU support # cuda: Build with CUDA support. # cuda_clang Build with CUDA Clang support. # rocm: Build with AMD GPU support (rocm) # mkl: Enable full mkl support. # tensorrt: Enable Tensorrt support. # noaws: Disable AWS S3 storage support
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.github/ISSUE_TEMPLATE/tflite-converter-issue.md
- Model produces wrong results and/or has lesser accuracy. - Model produces correct results, but it is slower than expected. ### 4. (optional) RNN conversion support If converting TF RNN to TFLite fused RNN ops, please prefix [RNN] in the title. ### 5. (optional) Any other info / logs
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tensorflow/c/experimental/filesystem/BUILD
srcs = ["modular_filesystem_test.cc"], linkopts = ["-ldl"], tags = [ "manual", # Requires DSOs as arguments, eventual setup "notap", # b/139060984, requires implementing modular support for Google filesystem ], deps = [ ":modular_filesystem", "//tensorflow/core:framework_internal", "//tensorflow/core/lib/io:path", "//tensorflow/core/platform:env",
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