That setting must exist for this script to function correctly.
EOF
  exit 1
fi
TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX"

# These flags share the user's gcloud credentials with the container, so that bazel
# inside the container can authenticate. Note: TF's CI does not have any credential
# stored here.

    bazel by doing as follows:

    ```bash
    export flags="--config=opt -k"
    ```

    If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and
    add the `cuda` option flag

    ```bash
    export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
    export flags="--config=opt --config=cuda -k"
    ```

# END TF TEST SUITE OPTIONS

# START CROSS-COMPILE CONFIGS
# Set execution platform to Linux x86
# Note: Lot of the "host_" flags such as "host_cpu" and "host_crosstool_top"
# flags seem to be actually used to specify the execution platform details. It
# seems it is this way because these flags are old and predate the distinction
# between host and execution platform.
build:cross_compile_base --host_cpu=k8

)

# This flag enables experimental TPU support
bool_flag(
    name = "enable_tpu_support",
    build_setting_default = False,
)

config_setting(
    name = "with_tpu_support_define",
    define_values = {"with_tpu_support": "true"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "with_tpu_support_flag",
    flag_values = {":enable_tpu_support": "True"},

            "//tensorflow/core:lib_internal",
            "//tensorflow/core:op_gen_lib",
            "//tensorflow/core:protos_all_cc",
            "//tensorflow/core/config:flag_defs",
            "//tensorflow/core/config:flags",
            "//tensorflow/core/distributed_runtime:server_lib",
            "//tensorflow/core/kernels:logging_ops",
            "@com_google_absl//absl/strings",
        ],
    }),

# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which
# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available

    hdrs = ["tape.h"],
    visibility = ["//tensorflow:internal"],
    deps = [
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "//tensorflow/core/config:flag_defs",
        "//tensorflow/core/config:flags",
    ],
)

filegroup(
    name = "headers",
    srcs = [
        "c_api.h",
        "c_api_experimental.h",
        "dlpack.h",
    ],

*   CPU
    *   Fix an issue w/ dynamic weights and `Conv2D` on x86.
    *   Add a runtime Android flag for enabling `XNNPACK` for optimized CPU
        performance.
    *   Add a runtime iOS flag for enabling `XNNPACK` for optimized CPU
        performance.
    *   Add a compiler flag to enable building a TFLite library that applies
        `XNNPACK` delegate automatically when the model has a `fp32` operation.

A smaller CPU-only package is also available:

```
$ pip install tensorflow-cpu
```

To update TensorFlow to the latest version, add `--upgrade` flag to the above
commands.

*Nightly binaries are available for testing using the
[tf-nightly](https://pypi.python.org/pypi/tf-nightly) and
[tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPi.*