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ci/official/envs/disk_cache
Plain Text - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 01 03:21:19 GMT 2024 - 1023 bytes - Viewed (0) -
tensorflow/c/experimental/filesystem/modular_filesystem.cc
// Since `p` is allocated by plugin, free it using plugin's method. plugin_memory_free_(p); return ret; } void ModularFileSystem::FlushCaches(TransactionToken* token) { if (ops_->flush_caches != nullptr) ops_->flush_caches(filesystem_.get()); } Status ModularFileSystem::SetOption(const std::string& name, const std::vector<string>& values) {
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Sep 06 19:12:29 GMT 2023 - 23.1K bytes - Viewed (0) -
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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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();
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 2.1K bytes - Viewed (0) -
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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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.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 39.5K bytes - Viewed (0) -
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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tensorflow/c/experimental/filesystem/plugins/gcs/gcs_filesystem.cc
}); uint64_t stat_cache_max_age = kStatCacheDefaultMaxAge; size_t stat_cache_max_entries = kStatCacheDefaultMaxEntries; const char* stat_cache_max_age_env = std::getenv(kStatCacheMaxAge); if (stat_cache_max_age_env && absl::SimpleAtoi(stat_cache_max_age_env, &value)) { stat_cache_max_age = value; } const char* stat_cache_max_entries_env = std::getenv(kStatCacheMaxEntries);
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tensorflow/c/eager/immediate_execution_context.h
virtual std::vector<string> ListFunctionNames() = 0; struct CacheStats { int64_t kernel_cache_size; int64_t device_cache_size; std::map<std::string, int64_t> func_kernel_cache_entries; int64_t local_rendezvous_cache_active_size; }; virtual CacheStats GetCacheStats() = 0; //===--------------------------------------------------------------------===//
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Jul 06 08:34:00 GMT 2023 - 12.3K bytes - Viewed (0) -
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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