Using the Deep Learning AMI with Conda - AWS Deep Learning AMIs
Introduction to the Deep Learning AMI with CondaLog in to Your DLAMIStart the TensorFlow EnvironmentSwitch to the PyTorch Python 3 EnvironmentRemoving Environments

Using the Deep Learning AMI with Conda

Introduction to the Deep Learning AMI with Conda

Conda is an open source package management system and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer.

The Deep Learning AMI with Conda has been configured for you to easily switch between deep learning environments. The following instructions guide you on some basic commands with conda. They also help you verify that the basic import of the framework is functioning, and that you can run a couple simple operations with the framework. You can then move on to more thorough tutorials provided with the DLAMI or the frameworks' examples found on each frameworks' project site.

Log in to Your DLAMI

After you log in to your server, you will see a server "message of the day" (MOTD) describing various Conda commands that you can use to switch between the different deep learning frameworks. Below is an example MOTD. Your specific MOTD may vary as new versions of the DLAMI are released.

=============================================================================
        AMI Name: Deep Learning OSS Nvidia Driver AMI (Amazon Linux 2) Version 77
        Supported EC2 instances: G4dn, G5, G6, Gr6, P4d, P4de, P5
            * To activate pre-built tensorflow environment, run: 'source activate tensorflow2_p310'
            * To activate pre-built pytorch environment, run: 'source activate pytorch_p310'
            * To activate pre-built python3 environment, run: 'source activate python3'

        NVIDIA driver version: 535.161.08

    CUDA versions available: cuda-11.7 cuda-11.8 cuda-12.0 cuda-12.1 cuda-12.2

    Default CUDA version is 12.1

    Release notes: https://docs.aws.amazon.com/dlami/latest/devguide/appendix-ami-release-notes.html
    AWS Deep Learning AMI Homepage: https://aws.amazon.com/machine-learning/amis/
    Developer Guide and Release Notes: https://docs.aws.amazon.com/dlami/latest/devguide/what-is-dlami.html
    Support: https://forums.aws.amazon.com/forum.jspa?forumID=263
    For a fully managed experience, check out Amazon SageMaker at https://aws.amazon.com/sagemaker
    =============================================================================

Start the TensorFlow Environment

Note

When you launch your first Conda environment, please be patient while it loads. The Deep Learning AMI with Conda automatically installs the most optimized version of the framework for your EC2 instance upon the framework's first activation. You should not expect subsequent delays.

  1. Activate the TensorFlow virtual environment for Python 3.

    $ source activate tensorflow2_p310

  2. Start the iPython terminal.

    (tensorflow2_p310)$ ipython

  3. Run a quick TensorFlow program.

    
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))

You should see "Hello, Tensorflow!"

Next Up

Running Jupyter Notebook Tutorials

Switch to the PyTorch Python 3 Environment

If you're still in the iPython console, use quit(), then get ready to switch environments.

  • Activate the PyTorch virtual environment for Python 3.

    $ source activate pytorch_p310

Test Some PyTorch Code

To test your installation, use Python to write PyTorch code that creates and prints an array.

  1. Start the iPython terminal.

    (pytorch_p310)$ ipython

  2. Import PyTorch.

    import torch

    You might see a warning message about a third-party package. You can ignore it.

  3. Create a 5x3 matrix with the elements initialized randomly. Print the array.

    x = torch.rand(5, 3)
print(x)

    Verify the result.

    tensor([[0.3105, 0.5983, 0.5410],
        [0.0234, 0.0934, 0.0371],
        [0.9740, 0.1439, 0.3107],
        [0.6461, 0.9035, 0.5715],
        [0.4401, 0.7990, 0.8913]])

Removing Environments

If you run out of space on the DLAMI, you can choose to uninstall Conda packages that you are not using:

conda env list
conda env remove –-name <env_name>