__init__() {
	if which curl &>/dev/null; then
		echo "curl is already installed"
	else
		echo "Installing curl:"
		sudo apt install curl -y
	fi

	export GOPATH=/tmp/gopath
	export PATH="${PATH}":"${GOPATH}"/bin

	if which mc &>/dev/null; then
		echo "mc is already installed"
	else
		echo "Installing mc:"
		go install github.com/minio/mc@latest
	fi

	if [ ! -x ./minio.${OLD_VERSION} ]; then

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

## Prerequisites

Please make sure the following packages are already installed via `dnf` or `apt`: 

- `tuned`
- `curl`

### Install `tuned.conf` performance profile

#### Step 1 - download `tuned.conf` from the referenced link
```
wget https://raw.githubusercontent.com/minio/minio/master/docs/tuning/tuned.conf
```

#### Step 2 - install tuned.conf as supported performance profile on all nodes
```

    ```bash
    python tf_doctest.py --file=<file_path>
    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from

                }
                getLogger().debug(message);
            } else {
                // Locally installed file is newer, don't use the resolved version
                getLogger().debug(artifact.getArtifactId() + ": using locally installed snapshot");
            }
        }
        return version;
    }

     * correctly installed
     *
     * <p>{@link #transitioned}: is set by {@link #transitionService} to indicate that some
     * transition has been performed.
     *
     * <p>Together, they allow us to enforce that all services have their listeners installed prior
     * to any service performing a transition, then we can fail in the ServiceManager constructor

downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/

      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.
         * It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task.

# Packages to be installed for the new Docker image.

# Packages needed to build devtoolset
file
flex
g++
make
patch
rpm2cpio
unar
wget
xz-utils
cpio

# Other build-related tools
apt-transport-https
autoconf
automake
build-essential
ca-certificates
llvm-18
clang-18
clang-tidy-18
lld-18
clang-format-12
curl
git
parallel
sudo
swig
unzip
zip
openjdk-21-jdk
vim

-   type: dropdown id: source attributes: label: Source description: Tensorflow
    installed from options: - source - binary validations: required: true

-   type: input id: tfversion attributes: label: Tensorflow Version description:
    placeholder: ex,. tf 2.8 validations: required: true