Multi-version Machine Learning Container Support

Note

Objectives

  • Use of images such as TensorFlow and PyTorch without user’s installation
  • Support multiple versions of major ML libraries

Backend.AI provides variaous pre-built ML and HPC kernel images. Therefore, users can immediately utilize major libraries and packages without having to install packages themselves. Here, we’ll walk through an example that takes advantage of multiple versions of the multiple ML library immediately.

Go to the Sessions page and open the session launch dialog. There may be various kernel images depending on the installation settings.

../_images/various_kernel_images.png

Here, we selected the TensorFlow 2.2 environment and created a session.

../_images/session_launch_dialog_tf22.png

Open the web terminal of the created session and run the following Python command. You can see that TensorFlow 2.2 version is actually installed.

../_images/tf22_version_print.png

This time, we select the TensorFlow 1.13 environment to create a compute session. (If resources are insufficient, previous sessions are deleted)

../_images/session_launch_dialog_tf113.png

Open the web terminal of the created session and run the same Python command as before. You can see that TensorFlow 1.13(.1) version is actually installed.

../_images/tf113_version_print.png

Finally, create a compute session using PyTorch version 1.5.

../_images/session_launch_dialog_pytorch15.png

Open the web terminal of the created session and run the following Python command. You can see that PyTorch 1.5 version is actually installed.

../_images/pytorch15_version_print.png

Like this, you can utilize various versions of major libraries such as TensorFlow and PyTorch through Backend.AI without unnecessary installation effort.