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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/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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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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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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tensorflow/BUILD
# Description: # TensorFlow is a computational framework, primarily for use in machine # learning applications. load("@bazel_skylib//lib:selects.bzl", "selects") load("@bazel_skylib//rules:common_settings.bzl", "bool_flag", "bool_setting") load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda") load( "//tensorflow:tensorflow.bzl", "VERSION", "VERSION_MAJOR", "check_deps", "if_google", "if_oss",
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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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RELEASE.md
* `tf.keras.optimizers.Optimizer` now points to the new Keras optimizer, and old optimizers have moved to the `tf.keras.optimizers.legacy` namespace. If you find your workflow failing due to this change, you may be facing one of the following issues: * **Checkpoint loading failure.** The new optimizer handles optimizer state differently from the old optimizer, which simplies the logic of
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ci/devinfra/docker_windows/Dockerfile
[Environment]::SetEnvironmentVariable('HOME', 'C:\Users\ContainerAdministrator\', 'Machine'); \ [Environment]::SetEnvironmentVariable('HOMEDRIVE', 'C:', 'Machine'); \ [Environment]::SetEnvironmentVariable('HOMEPATH', '\Users\ContainerAdministrator\', 'Machine'); \ [Environment]::SetEnvironmentVariable('GOROOT', 'C:\Program Files\Go\', 'Machine'); \
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CITATION.cff
title: TensorFlow, Large-scale machine learning on heterogeneous systems
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.bazelrc
build:rbe_cross_compile_base --remote_instance_name=projects/tensorflow-testing/instances/default_instance # XLA depends on some local Python headers that are configured as Genrule. They # are present on the local host machine but not on the remote execution machine, # leading to build failures. To resolve the issue, the following line is added # to make sure all Genrule targets are excuted locally. build:rbe_cross_compile_base_xla --config=rbe_cross_compile_base
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