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",

					})
				}
			}
			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{

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

[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

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.5.1 \
    --hash=sha256:023ce2f502efd4d6c1e0472cc58ce3640d051d40e71e27386bed33901e201327 \
    --hash=sha256:05f23447a1c20ddf4dc7c2c661aa9ed93fcb2658f1017c204d1e758714dc28a8 \

MinIO is a high-performance, S3-compatible object storage solution released under the GNU AGPL v3.0 license.
Designed for speed and scalability, it powers AI/ML, analytics, and data-intensive workloads with industry-leading performance.

- S3 API Compatible – Seamless integration with existing S3 tools
- Built for AI & Analytics – Optimized for large-scale data pipelines

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.5.1 \
    --hash=sha256:023ce2f502efd4d6c1e0472cc58ce3640d051d40e71e27386bed33901e201327 \
    --hash=sha256:05f23447a1c20ddf4dc7c2c661aa9ed93fcb2658f1017c204d1e758714dc28a8 \

mdurl==0.1.2 \
    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
    # via markdown-it-py
ml-dtypes==0.5.1 \
    --hash=sha256:023ce2f502efd4d6c1e0472cc58ce3640d051d40e71e27386bed33901e201327 \
    --hash=sha256:05f23447a1c20ddf4dc7c2c661aa9ed93fcb2658f1017c204d1e758714dc28a8 \