Using Compute Session

In addition to see the list of compute sessions, Sessions tab lets you start new sessions or use and manage already running sessions.

Start a new session

Click START button to start a new compute session. The following setup dialog will appear. Specify the language environment (Environments, Version), the amount of resources (CPU, RAM, GPU, etc.) you want to use, and then press the LAUNCH button.


If the GPU resource is marked as FGPU, this means that the server is serving the GPU resources in a virtualized form. Backend.AI supports GPU virtualization technology that a single physical GPU can be divided and shared by multiple users for better utilization. Therefore, if you want to execute a task that does not require a large amount of GPU computation, you can create a compute session by allocating only a portion of a GPU. The amount of GPU resources that 1 FGPU actually allocates may vary from system to system depending on the administrator’s setting. For example, if administrator has set to split one physical GPU into five pieces, 5 FGPU means 1 physical GPU, or 1 FGPU means 0.2 physical GPU. At this configuration, if you create a compute session by allocating 1 FGPU, you can utilize SM (streaming multiprocessor) and GPU memory corresponding to 0.2 physical GPU for the session.

Wait for a while for the compute session to be started. If you have created a folder in the Storage menu, you can also choose them from the Folders to mount menu. Folders/Storages are discussed in a separate section.

Session launch dialog with various settings

Notice that a new compute session is created in the Running tab.

New session is created

Use and Manage Running Session

This time, let’s take a look at how to use and manage a running compute session. If you see the Control column in the session list, there are several icons. When you click the first icon, several app services supported by the session will appear as shown in the following figure.

App launch dialog

As a test, let’s click on Jupyter Notebook.

Jupyter app is launched

You will see a new window pop up and Jupyter Notebook is running. This Notebook was created inside the running compute session, and it’s easy to use with just a click of a button without any setup. In addition, you can just use the language environment and libraries provided by the compute session as is, so there is no need to install a separate packages. For more information on how to use Jupyter Notebook, please refer to the official documentation.

In the notebook’s file explorer, the id_container file contains a private SSH key. If necessary, you can download it and use it for SSH / SFTP access to the container.

Click the NEW button on the upper right corner and select Notebook for Backend.AI, and ipynb window will pop up where you can enter the new code.

Backend.AI notebook on Jupyter menu

In this window, you can enter and execute any code you want by using the environment that session provides. The code execution happens on one of the Backend.AI nodes where the compute session is actually created, and there is no need to configure a separate environment on the local machine.

Code execution on Jupyter Notebook

When you close the window, you can notice that the Untitled.ipynb file is created in the Notebook File Explorer. Note that the files created here are deleted when you destroy the session. The way to preserve those files even when the session is gone is described in the Storage/Folders section.

Untitled.ipynb file is created in the Jupyter

Return to the Session list page. This time, let’s launch the terminal. Click the terminal icon (the second button) to use the container’s ttyd daemon. The terminal will also appear in a new window, and you can type commands, just like any usual terminal, which will be delivered to the compute session as shown in the following figure. If you are familiar with using command-line interface (CLI), you can easily interact with Linux commands.

Backend.AI session terminal

If you create a file here, you can immediately see it in the Jupyter Notebook you opened earlier as well. Conversely, changes made to files in Jupyter Notebook can also be checked right from the terminal. This is because they are using the same files in the same compute session.

In addition, you can use web-based services such as TensorBoard, Jupyter Lab, etc., depending on the type of services provided by the compute session.

To delete a specific session, tap the red trash icon. Since the data in the folder inside the compute session is deleted as soon as the compute session ends, it is recommended that you move the data to the mounted folder or upload it to the folder from the beginning if you want to keep it.