defaults:
      run:
        shell: bash
    container: ${{ (contains(inputs.runner, 'linux-x86') && 'us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest') ||
                   (contains(inputs.runner, 'linux-arm64') && 'us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest') ||
                   (contains(inputs.runner, 'windows-x86') && null) }}
    timeout-minutes: 300
    steps:

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

    }

    def "fails on unexpected ml notice content"() {
        given:
        elasticLicense()
        elasticLicense(file("LICENSE.txt"))
        file("NOTICE.txt").text = """Elasticsearch
Copyright 2009-2021 Elasticsearch"""

        file("ml/NOTICE.txt").text = "Boost Software License - Version 1.0 - August 17th, 2003"

# 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"

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

TFCI_DOCKER_ENABLE=1
if [[ "$TFCI_GITHUB_ACTIONS" == "true" ]]; then
  TFCI_DOCKER_ENABLE=0
fi
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_IMAGE="us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-windows@sha256:023fa901fda70be8890b05689d0a667287692c1f59d0abe4add7208ec5a12707"
TFCI_BAZEL_BAZELRC_ARGS="--output_user_root=C:/x"

        "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 = "f2c924e85a22ba2eaa0c08657e5f5467fedbc3d0506f9cc0c69dd97ed9fbaf28",

        }

        final ASN1ObjectIdentifier[] lm = this.mechs;
        final byte[] ml = encodeMechs(lm);
        final byte[] mechanismListMIC = this.mechContext.calculateMIC(ml);
        if (log.isDebugEnabled()) {
            log.debug("Out Mech list " + Arrays.toString(lm));
            log.debug("Out Mech list encoded " + Hexdump.toHexString(ml));
            log.debug("Out Mech list MIC " + Hexdump.toHexString(mechanismListMIC));

    "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{