Mismatch in python environment on AWS EC2 image

after following the instructions i’ve been able to update my cloudformation.yaml file and it looks like the tao cli is now running.

Description: 'MTData EC2 Training Instance CloudFormation Template'
Parameters:
  KeyName:
    Description: Name of an existing EC2 KeyPair to enable SSH access to the instance
    Type: AWS::EC2::KeyPair::KeyName
    ConstraintDescription: must be the name of an existing EC2 KeyPair.
  JupyterToken:
    Description: Token value used to access the JupyterLab server through the web browser.
    Type: String

Resources:
  EC2Instance:
    Type: AWS::EC2::Instance
    Properties: 
      ImageId: ami-06xxxxxxxxxxxxxx
      KeyName: !Ref 'KeyName'
      InstanceType: g4dn.xlarge 
      SubnetId: subnet-b8811abc
      SecurityGroupIds:
        - sg-009abcd12Afb441bc
      Tags:
        - Key: Name
          Value: a-training-vm
        - Key: Owner
          Value: Tom
        - Key: Environment
          Value: NONPROD
        - Key: Project
          Value: Retraining
        - Key: Customer
          Value: ACustomer
      UserData:
        Fn::Base64: 
          !Sub |
            #!/bin/bash -x 
            {
              echo "Following the Launcher CLI installation instructions at https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html#running-tao-toolkit" 
              echo '=== install nvidia docker ===' 
              export distribution=$(. /etc/os-release;echo $ID$VERSION_ID) 
              env > /var/log/my_env.log
              echo '=== checking for network ===' 
              retry=0
              max_retries=5
              while ! ping -c 1 -W 1 8.8.8.8; do
                  sleep 10
                  echo "waiting for network"
                  ((retry++))
                  if [ $retry -ge $max_retries ]; then
                      echo "Network not available, exiting."
                      exit 1
                  fi
              done
              echo '=== install nvidia-docker2 ===' 
              sudo mkdir -p /usr/share/keyrings
              curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | gpg --dearmor | sudo tee /usr/share/keyrings/nvidia-docker-archive-keyring.gpg > /var/log/my_curl.log 2>&1 || echo "Failed here" >> /var/log/my_debug.log
              echo "deb [signed-by=/usr/share/keyrings/nvidia-docker-archive-keyring.gpg] https://nvidia.github.io/nvidia-docker/$$distribution/ nvidia-docker main" | sudo tee /etc/apt/sources.list.d/nvidia-docker.list > /dev/null
              sudo apt-get update 
              sudo apt-get install -y nvidia-docker2  
              echo '=== setup docker password to login to nvcr.io ==='
              export DOCKER_PASSWORD=[this is the real docker password] 
              export DOCKER_USERNAME="\$oauthtoken" 
              export D_TOKEN=$(echo -n "$DOCKER_USERNAME:$DOCKER_PASSWORD" | base64 -w 0) 
              mkdir -p ~/.docker/ && echo "{ \"auths\": { \"nvcr.io\": { \"auth\":\"$D_TOKEN\" } } }" > ~/.docker/config.json 
              echo "{ \"default-runtime\": \"nvidia\", \"runtimes\": { \"nvidia\": { \"path\": \"nvidia-container-runtime\", \"args\": [] } } }" | sudo tee /etc/docker/daemon.json > /dev/null 
              sudo usermod -aG docker root
              sudo usermod -aG docker ubuntu
              sudo systemctl restart docker 
              docker login nvcr.io 
              echo "a quick docker test that fails...."
              sudo docker run --rm --gpus all nvidia/cuda1.0.3-base nvidia-smi 
              echo '=== install miniconda ==='
              export HOME=/root
              mkdir -p ~/miniconda3 
              wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh 
              bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 
              rm -rf ~/miniconda3/miniconda.sh 
              export PATH=/root/miniconda3/bin:$PATH
              echo '=== setup python 3.6 environement and activate'
              conda create -n launcher python=3.6 
              conda init bash
              source /root/miniconda3/etc/profile.d/conda.sh
              conda activate launcher 
              echo '=== install TAO toolkit ==='
              wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/tao/tao-getting-started/versions/5.0.0/zip -O ~/getting_started_v5.0.0.zip 
              unzip -u ~/getting_started_v5.0.0.zip  -d ~/getting_started_v5.0.0 && rm -rf ~/getting_started_v5.0.0.zip 
              echo '=== install TAO Launcher ===' 
              cd ~/getting_started_v5.0.0/ 
              chmod +x ~/getting_started_v5.0.0/setup/quickstart_launcher.sh 
              sudo usermod -aG docker root
              sudo usermod -aG docker ubuntu
              yes | ~/getting_started_v5.0.0/setup/quickstart_launcher.sh --install 
              sudo -H sh -c "yes | ~/getting_started_v5.0.0/setup/quickstart_launcher.sh --install"
              whoami
              sudo tao --help
              sudo which tao
              tao --help
              which tao
              echo "the end"
            } > /var/log/user-data-output.log 2>&1

  
Outputs:
  PublicDnsName:
    Value: !GetAtt [EC2Instance, PublicDnsName]
    Description: Public DNS name

the nvidia-driver version is already at version 528:

# nvidia-smi
Wed Sep 27 02:43:24 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+

so i think this is what i was after with this question.
I hope the cloudformation.yaml will help someone (just update the docker password and the correct AMI )

Thank you @Morganh !!