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WORKSPACE
) load("@python//:defs.bzl", "interpreter") load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse") NUMPY_ANNOTATIONS = { "numpy": package_annotation( additive_build_content = """\ filegroup( name = "includes", srcs = glob(["site-packages/numpy/core/include/**/*.h"]), ) cc_library( name = "numpy_headers", hdrs = [":includes"],
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tensorflow/c/experimental/ops/BUILD
srcs = [ "array_ops.cc", ], hdrs = [ "array_ops.h", ], visibility = [ "//tensorflow:internal", ], deps = [ "//tensorflow/c/eager:abstract_context", "//tensorflow/c/eager:abstract_operation", "//tensorflow/c/eager:abstract_tensor_handle", "//tensorflow/c/eager:c_api_unified_internal", "//tensorflow/c/eager:tracing_utils",
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tensorflow/c/eager/parallel_device/BUILD
"//tensorflow/c/eager:c_api_experimental", "//tensorflow/c/eager:tfe_context_internal", "//tensorflow/core:framework", "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core/common_runtime/eager:context", ], ) cc_library( name = "parallel_device_testlib", testonly = 1,
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ci/official/envs/macos_x86_cross_compile
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tensorflow/c/BUILD
":checkpoint_reader", ":tf_buffer", ":tf_buffer_internal", "//tensorflow/c/eager:c_api", "//tensorflow/c/eager:c_api_internal", "//tensorflow/c/eager:tfe_context_internal", "//tensorflow/c/eager:tfe_op_internal", "//tensorflow/c/eager:tfe_tensorhandle_internal", "//tensorflow/compiler/jit:flags", "//tensorflow/compiler/jit:get_compiler_ir",
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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
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LICENSE
names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS,
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tensorflow/c/eager/BUILD
"//tensorflow/core:protos_all_cc", ], ) cc_library( name = "immediate_execution_context", hdrs = ["immediate_execution_context.h"], visibility = [ "//tensorflow:internal", ], deps = [ ":abstract_context", ":immediate_execution_distributed_manager", ":immediate_execution_operation", ":immediate_execution_tensor_handle",
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tensorflow/c/experimental/gradients/tape/BUILD
licenses = ["notice"], ) cc_library( name = "tape_context", srcs = ["tape_context.cc"], hdrs = [ "tape_context.h", ], visibility = [ "//tensorflow:internal", ], deps = [ ":tape_operation", "//tensorflow/c/eager:abstract_context", ], ) cc_library( name = "tape_operation", srcs = ["tape_operation.cc"],
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SECURITY.md
tenant isolation, resource allocation, model sharing and hardware attacks. ### Tenant isolation Since any tenants or users providing models, graphs or checkpoints can execute code in context of the TensorFlow service, it is important to design isolation mechanisms that prevent unwanted access to the data from other tenants. Network isolation between different models is also important not only to prevent
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