TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=devel ci/official/containers/ml_build"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-x86_64"
TFCI_OUTPUT_DIR=build_output

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/ml_build_arm64"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-arm64"

        "https://github.com/bazelbuild/rules_shell/releases/download/v0.4.1/rules_shell-v0.4.1.tar.gz",
    ),
)

# Initialize toolchains for ML projects.
#
# A hermetic build system is designed to produce completely reproducible builds for C++.
# Details: https://github.com/google-ml-infra/rules_ml_toolchain
tf_http_archive(
    name = "rules_ml_toolchain",
    sha256 = "07802f21916a113be78ff2110891239bd5183ad09d8c42f6f9b04e4e0bfa5505",

    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):

    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }
    ],

					})
				}
			}
			ml = append(ml, MetricV2{
				Description: getClusterReplProxiedGetOperationsMD(clusterMetricNamespace),
				Value:       float64(m.Proxied.GetTotal),
			})
			ml = append(ml, MetricV2{
				Description: getClusterReplProxiedHeadOperationsMD(clusterMetricNamespace),
				Value:       float64(m.Proxied.HeadTotal),
			})
			ml = append(ml, MetricV2{

[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine

  stars: 3379
  owner_login: TracecatHQ
  owner_html_url: https://github.com/TracecatHQ
- name: opyrator
  html_url: https://github.com/ml-tooling/opyrator
  stars: 3135
  owner_login: ml-tooling
  owner_html_url: https://github.com/ml-tooling
- name: docarray
  html_url: https://github.com/docarray/docarray
  stars: 3114
  owner_login: docarray
  owner_html_url: https://github.com/docarray

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