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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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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/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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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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RELEASE.md
* Renamed `"binary"` output mode to `"multi_hot"` for `CategoryEncoding`, `StringLookup`, `IntegerLookup`, and `TextVectorization`. Multi-hot encoding will no longer automatically uprank rank 1 inputs, so these layers can now multi-hot encode unbatched multi-dimensional samples. * Added a new output mode `"one_hot"` for `CategoryEncoding`,
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tensorflow/c/eager/BUILD
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.bazelrc
build:ios_arm64e --cpu=ios_arm64e build:ios_sim_arm64 --config=ios build:ios_sim_arm64 --cpu=ios_sim_arm64 build:ios_x86_64 --config=ios build:ios_x86_64 --cpu=ios_x86_64 build:ios_fat --config=ios build:ios_fat --ios_multi_cpus=armv7,arm64,i386,x86_64 # Config to use a mostly-static build and disable modular op registration # support (this will revert to loading TensorFlow with RTLD_GLOBAL in Python).
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