ProjectDependencyGraphStub

Since they define the primary structure of the project/build.

The stubs define three top-level targets that are defined in  LifecycleTaskSegmentCalculatorStub;
clean, aggr and install. "aggr" is an aggregating task while clean and install are lifecyclephases.
There will be three items in the task list for this dataset.

The stubs also exist at different "levels", where one test might wire stubs into a specific live implementation.

# The RBE machine itself has older kernel mode driver, and it requires
# nvidia driver to be installed.
nvidia-driver-580-open
# TODO(b/445248346): The Docker image shouldn't have cuda-compat installed.
# However, hermetic CUDA forward-compatibility mode is still missing some
# libraries.

}

/**
 * To other developers, if you are using this method above, please use the following syntax.
 * By default this method does NOT execute ITs anymore, just "install".
 *
 * mavenBuild("<jdk>", "<profiles> <goals> <plugins> <properties>"
 *
 * @param jdk the jdk tool name (in jenkins) to use for this build
 * @param extraArgs extra command line args
 */

            <id>test</id>

            <!--  The key to this test... -->
            <inherited>false</inherited>

            <goals>
              <goal>compile</goal>
            </goals>
            <phase>install</phase>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>

        projects.put(p2, Collections.singletonList(p1));

        BuildPlan plan = calculateLifecycleMappings(projects, "verify");
        plan.then(calculateLifecycleMappings(projects, "install"));

        Stream.of(p1, p2).forEach(project -> {
            plan.requiredStep(project, "after:resources").addMojo(new MojoExecution(null), 0);

libcufft-12-8
libcurand-12-8
libcusolver-dev-12-8
libcusparse-dev-12-8
libcublas-12-8
libcublas-dev-12-8
libnccl-dev=2.25.1-1+cuda12.8
libnccl2=2.25.1-1+cuda12.8
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-dev-cuda-12=9.8.0.87-1

libcufft-13-0
libcurand-13-0
libcusolver-dev-13-0
libcusparse-dev-13-0
libcublas-13-0
libcublas-dev-13-0
libnccl-dev=2.27.7-1+cuda13.0
libnccl2=2.27.7-1+cuda13.0
# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn9-headers-cuda-13=9.12.0.46-1
libcudnn9-static-cuda-13=9.12.0.46-1
libcudnn9-dev-cuda-13=9.12.0.46-1

## Get Started

### 1. Prerequisites

- Install MinIO - [MinIO Quickstart Guide](https://docs.min.io/community/minio-object-store/operations/deployments/baremetal-deploy-minio-on-redhat-linux.html)
- Install `awscli` - [Installing AWS Command Line Interface](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html)

### 2. Set bucket WORM configuration

                    .hasArg()
                    .get());
            options.addOption(Option.builder(ALTERNATE_INSTALLATION_SETTINGS)
                    .longOpt("install-settings")
                    .desc("Alternate path for the installation settings file")
                    .hasArg()
                    .get());
            options.addOption(Option.builder(ALTERNATE_USER_TOOLCHAINS)

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt

# (9)
RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt