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ci/official/utilities/code_check_changed_files.bats
# 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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tensorflow/c/eager/parallel_device/BUILD
"//tensorflow/c/eager:c_api_experimental", "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", ],
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tensorflow/c/BUILD
"python_api.cc", "python_api.h", "*test*", ], ) + [ "//tensorflow/cc:srcs_no_runtime", "//tensorflow/core/distributed_runtime:server_lib.h", "@local_tsl//tsl/platform:ctstring", "@local_xla//xla/tsl/c:srcs", ], visibility = ["//visibility:public"], ) cc_library( name = "pywrap_required_hdrs",
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CONTRIBUTING.md
[how-to](https://help.github.com/articles/using-pull-requests/). TensorFlow team members will be assigned to review your pull requests. Once the pull requests are approved and pass continuous integration checks, a TensorFlow team member will apply `ready to pull` label to your change. This means we are working on getting your pull request submitted to our internal repository. After the change has been submitted internally, your pull request will be merged
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SECURITY.md
read/write/executions. ### Running a TensorFlow server TensorFlow is a platform for distributed computing, and as such there is a TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for internal communication only. It is not built for use in untrusted environments or networks. For performance reasons, the default TensorFlow server does not include any
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tensorflow/c/eager/BUILD
"//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "@com_google_absl//absl/strings", ], ) tf_cuda_cc_test( name = "c_api_remote_test", size = "medium", srcs = [ "c_api_remote_test.cc", ], # TODO(b/136478427): Figure out how to correctly shut the server down args = ["--heap_check="], tags = [ "no_windows",
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tensorflow/BUILD
"//tensorflow/core/data/service:grpc_util", "//tensorflow/core/data/service:py_utils", "//tensorflow/core/data/service:server_lib", "//tensorflow/core/debug", "//tensorflow/core/distributed_runtime:server_lib", "//tensorflow/core/framework:full_type_util", "//tensorflow/core/function/runtime_client:runtime_client_cc",
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requirements_lock_3_11.txt
tblib==2.0.0 \ --hash=sha256:9100bfa016b047d5b980d66e7efed952fbd20bd85b56110aaf473cb97d18709a \ --hash=sha256:a6df30f272c08bf8be66e0775fad862005d950a6b8449b94f7c788731d70ecd7 # via -r requirements.in tensorboard-data-server==0.7.2 \ --hash=sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb \ --hash=sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60 \
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RELEASE.md
based server to register tf.function methods and a GRPC client to invoke remote registered methods. RPC APIs are intended for multi-client setups i.e. server and clients are started in separate binaries independently. * Example usage to create server: ```python server = tf.distribute.experimental.rpc.Server.create("grpc", "127.0.0.1:1234")
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ci/official/containers/linux_arm64/devel.usertools/code_check_full.bats
# limitations under the License. # ============================================================================== setup_file() { cd /tf/tensorflow bazel version # Start the bazel server } # Do a bazel query specifically for the licenses checker. It searches for # targets matching the provided query, which start with // or @ but not # //tensorflow (so it looks for //third_party, //external, etc.), and then
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