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tensorflow/c/experimental/ops/BUILD
load("//tensorflow:tensorflow.default.bzl", "filegroup") load("//tensorflow/core/platform:rules_cc.bzl", "cc_library") # Experimental ops. These will eventually be replaced by machine-generated versions. package( # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"], licenses = ["notice"], ) cc_library( name = "array_ops", srcs = [ "array_ops.cc", ], hdrs = [ "array_ops.h",
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ci/official/containers/linux_arm64/builder.devtoolset/gcc9-fixups.patch
+ __attribute__ ((__alloc_size__ params)) + #include <iostream> using namespace std; diff --git a/sysdeps/aarch64/dl-machine.h b/sysdeps/aarch64/dl-machine.h index 185402f..bbdeae0 100644 --- a/sysdeps/aarch64/dl-machine.h +++ b/sysdeps/aarch64/dl-machine.h @@ -49,23 +49,11 @@ elf_machine_load_address (void) /* To figure out the load address we use the definition that for any symbol:
Others - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Mon Sep 18 14:52:45 GMT 2023 - 8.9K bytes - Viewed (0) -
CITATION.cff
title: TensorFlow, Large-scale machine learning on heterogeneous systems
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform.
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SECURITY.md
## TensorFlow models are programs TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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.zenodo.json
{ "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.", "license": "Apache-2.0", "title": "TensorFlow", "upload_type": "software", "creators": [ { "name": "TensorFlow Developers" }
Json - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Tue May 18 19:19:25 GMT 2021 - 741 bytes - Viewed (0) -
tensorflow/c/experimental/grappler/BUILD
"//tensorflow/core:framework", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler/clusters:single_machine", "//tensorflow/core/grappler/costs:graph_properties", "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder", "//tensorflow/core/protobuf:error_codes_proto_impl_cc", ],
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build --linkopt="-lm" build --linkopt="-Wl,--undefined-version" # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code # > on the host machine. If we don't have --distinct_host_configuration=false, # > the core TensorFlow code will be built once for the host and once for the # > target platform.
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tensorflow/c/env.h
// Flush local buffers to the filesystem. If the process terminates after a // successful flush, the contents may still be persisted, since the underlying // filesystem may eventually flush the contents. If the OS or machine crashes // after a successful flush, the contents may or may not be persisted, depending // on the implementation. TF_CAPI_EXPORT extern void TF_FlushWritableFile(TF_WritableFileHandle* handle,
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Sat Jan 09 02:53:27 GMT 2021 - 9.6K bytes - Viewed (0) -
ci/official/README.md
- Uses `pycpp.sh`, `code_check_changed_files.sh` These "env" files match up with an environment matrix that roughly covers: - Different Python versions - Linux, MacOS, and Windows machines (these pool definitions are internal) - x86 and arm64 - CPU-only, or with NVIDIA CUDA support (Linux only), or with TPUs ## How to Test Your Changes to TensorFlow
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