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ci/official/requirements_updater/WORKSPACE
) # buildifier: disable=same-origin-load load("@rules_python//python:repositories.bzl", "py_repositories") py_repositories() load("@rules_python//python:repositories.bzl", "python_register_multi_toolchains") # buildifier: disable=same-origin-load load("@rules_python//python/pip_install:repositories.bzl", "pip_install_dependencies") default_python_version = "3.10" load( "//:updater_config_repository.bzl",
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tensorflow/c/eager/immediate_execution_distributed_manager.h
int keep_alive_secs) = 0; // Set up a multi-client distributed execution environment. Must be called // on all tasks in the cluster. This call internally coordinates with other // tasks to initialize the eager context and TF server for multi-client // execution. virtual Status EnableCollectiveOps(const ServerDef& server_def) = 0;
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ci/official/envs/linux_arm64
# despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python TFCI_DOCKER_PULL_ENABLE=1 TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/linux_arm64" TFCI_INDEX_HTML_ENABLE=1 TFCI_LIB_SUFFIX="-cpu-linux-arm64" TFCI_OUTPUT_DIR=build_output TFCI_WHL_AUDIT_ENABLE=1
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ci/official/containers/linux_arm64/build.sh
# Builds the following Docker images for Linux ARM64. See the accompanying # Dockerfile for more details: # - gcr.io/tensorflow-sigs/build-arm64:jax-latest-multi-python # - gcr.io/tensorflow-sigs/build-arm64:tf-latest-multi-python set -exo pipefail function is_continuous_or_release() { [[ "$KOKORO_JOB_TYPE" == "CONTINUOUS_INTEGRATION" ]] || [[ "$KOKORO_JOB_TYPE" == "RELEASE" ]] }
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ci/official/requirements_updater/README.md
actually using the new version with Tensorflow. 2) In the `WORKSPACE` file, add the new version to the `python_versions` parameter of the `python_register_multi_toolchains` function. 3) In the `BUILD.bazel` file, add a load statement for the new version, e.g. ``` load("@python//3.11:defs.bzl", compile_pip_requirements_3_11 = "compile_pip_requirements")
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tensorflow/c/eager/immediate_execution_context.h
#if !defined(IS_MOBILE_PLATFORM) // Set up a multi-client distributed execution environment. Must be called on // all tasks in the cluster. // This call internally coordinates with other tasks to initialize the eager // context and TF server for multi-client execution. virtual Status EnableCollectiveOps(const ServerDef& server_def) = 0;
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ci/official/wheel_test/WORKSPACE
"python_repository", ) python_repository(name = "python_version_repo") load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION") # Register multi toolchains load("@rules_python//python:repositories.bzl", "python_register_toolchains") # buildifier: disable=same-origin-load python_register_toolchains( name = "python", ignore_root_user_error = True,
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SECURITY.md
stated above is already equivalent to code execution. In any case, the recommendation is not to serve models using Eager mode since it also has performance limitations. ## Multi-Tenant environments It is possible to run multiple TensorFlow models in parallel. For example, `ModelServer` collates all computation graphs exposed to it (from multiple
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ci/official/any.sh
# export TF_ANY_EXTRA_ENV=ci/official/envs/local_rbe # ./any.sh # ... set -euxo pipefail cd "$(dirname "$0")/../../" # tensorflow/ # Any request that includes "nightly_upload" should just use the # local multi-cache (public read-only cache + disk cache) instead. export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')" if [[ -n "${TF_ANY_EXTRA_ENV:-}" ]]; then export TFCI="$TFCI,$TF_ANY_EXTRA_ENV" fi
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tensorflow/c/c_api_experimental.h
TF_CAPI_EXPORT extern void TFE_AbortCollectiveOps(TFE_Context* ctx, TF_Status* status); // Checks the health of collective ops peers. Explicit health check is needed in // multi worker collective ops to detect failures in the cluster. If a peer is // down, collective ops may hang. TF_CAPI_EXPORT extern void TFE_CollectiveOpsCheckPeerHealth( TFE_Context* ctx, const char* task, int64_t timeout_in_ms,
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