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ci/official/envs/disk_cache
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ci/official/envs/public_cache_push
# Sourcing this enables Bazel remote cache (read and write) # Note that "_push" cache configs write to GCS buckets and require # authentication. If you are not a Googler, source "public_cache" to enable the # public read-only cache. # The cache configs are different for MacOS and Linux if [[ $(uname -s) == "Darwin" ]]; then TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push" else
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# This bazelrc can build a CPU-supporting TF package. # Convenient cache configurations # Use a cache directory mounted to /tf/cache. Very useful! build:sigbuild_local_cache --disk_cache=/tf/cache # Use the public-access TF DevInfra cache (read only) build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false
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ci/official/README.md
# Ex. macos_arm64 -- arm64 MacOS platform # 3. Add modifiers. Some modifiers for local execution are: # Ex. disk_cache -- Use a local cache # Ex. public_cache -- Use TF's public cache (read-only) # Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only) # Ex. rbe -- Use RBE for faster builds (Googlers only; see below)
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tensorflow/c/eager/immediate_execution_tensor_handle.cc
} Status ImmediateExecutionTensorHandle::SummarizeValue( std::string& summary) const { Status status; AbstractTensorPtr resolved( // TODO(allenl): Resolve should be const, and the caches that get updated // marked mutable. const_cast<ImmediateExecutionTensorHandle*>(this)->Resolve(&status)); if (!status.ok()) { return status; } summary = resolved->SummarizeValue();
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tensorflow/c/eager/c_api_experimental.h
// is initialized (either locally or remotely). The context_id can change during // the process lifetime although this should cause the worker to be // reinitialized (e.g. cleared caches) as well. TF_CAPI_EXPORT extern uint64_t TFE_GetContextId(TFE_Context* ctx); // ----------------------------------------------------------------------------- // Cancellation APIs.
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ci/official/envs/public_cache
# Sourcing this enables Bazel remote cache (public, read-only) # The cache configs are different for MacOS and Linux if [[ $(uname -s) == "Darwin" ]]; then TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache" else TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_cache"
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ci/official/utilities/setup_docker.sh
docker pull "$TFCI_DOCKER_IMAGE" || sleep 15 docker pull "$TFCI_DOCKER_IMAGE" || sleep 15 docker pull "$TFCI_DOCKER_IMAGE" fi if [[ "$TFCI_DOCKER_REBUILD_ENABLE" == 1 ]]; then DOCKER_BUILDKIT=1 docker build --cache-from "$TFCI_DOCKER_IMAGE" -t "$TFCI_DOCKER_IMAGE" $TFCI_DOCKER_REBUILD_ARGS if [[ "$TFCI_DOCKER_REBUILD_UPLOAD_ENABLE" == 1 ]]; then docker push "$TFCI_DOCKER_IMAGE" fi fi
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tensorflow/c/eager/BUILD
"//tensorflow/core/distributed_runtime/rpc:grpc_channel", "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache", "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service", "//tensorflow/core/distributed_runtime/rpc:rpc_rendezvous_mgr", "//tensorflow/core/distributed_runtime/rpc/eager:grpc_eager_client",
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RELEASE.md
same implementation as their `tf.losses` equivalent. * For Keras model, the individual call of `Model.evaluate` uses no cached data for evaluation, while `Model.fit` uses cached data when `validation_data` arg is provided for better performance. * Adds a `save_traces` argument to `model.save`/ `tf.keras.models.save_model`
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