SSH/SFTP Connection to a Compute Session¶

Backend.AI supports SSH/SFTP connection to the created compute sessions (containers). In this section, we will learn how to do it.

Note

SSH/SFTP connection feature is supported through a WebUI Desktop application. Currently web-based WebUI does not support this feature due to limitations of file system access on browser-level. Desktop app can be downloaded a panel from the Summary page. Using this panel, the compatible version will be downloaded automatically.

You can also download the app from https://github.com/lablup/backend.ai-webui/releases. Make sure to download the compatible version of the Web-UI in this case. You can check the Web-UI version by clicking on the “About Backend.AI” sub-menu located in the preference menu on the upper-right side of the GUI.

For Linux / Mac¶

First, create a compute session, then click the app icon (first button) in Control, followed by SSH / SFTP icon. Then, a daemon that allows SSH/SFTP access from inside the container will be initiated, and the Web-UI app interacts with the daemon through a local proxy service.

Warning

You cannot establish a SSH/SFTP connection to the session until you click the SSH/SFTP icon. When you close the Web-UI app and launch it again, the connection between the local proxy and the Web-UI app is initialized, so the SSH/SFTP icon must be clicked again.

Next, a dialog containing SSH/SFTP connection information will be pop up. Remember the address (especially the assigned port) written in the SFTP URL and click the download link to save the id_container file on the local machine. This file is an automatically generated SSH private key. Instead of using the link, you can also download the id_container file located under /home/work/ with your web terminal or Jupyter Notebook. The auto-generated SSH key may change when new session is created. In that case, it must be downloaded again.

Starting SSH/SFTP daemon inside a compute session (container)

To SSH connect to the compute session with the downloaded SSH private key, you run the following command in the shell environment. You should write the path to the downloaded id_container file after -i option and the assigned port number after -p option. The user inside the compute session is usually set to work, but if your session uses other account, the work part in work@localhost should be changed to the actual session account. If you run the command correctly, you can see that SSH connection is made to the compute session and you are welcomed by the container’s shell environment.

$ ssh -o StrictHostKeyChecking=no \
>     -o UserKnownHostsFile=/dev/null \
>     -i ~/.ssh/id_container \
>     work@localhost -p 52468
Warning: Permanently added '[127.0.0.1]:52468' (RSA) to the list of known hosts.
f310e8dbce83:~$

Connecting by SFTP would almost be the same. After running the SFTP client and setting public key-based connection method, simply specify id_container as the SSH private key. Each FTP client may adopt different way, so refer to each FTP client manual for details.

Note

The SSH/SFTP connection port number is randomly assigned each time when a session is created. If you want to use a specific SSH/SFTP port number, you can input the port number in the “Preferred SSH Port” field in the user settings menu. To avoid possible collisions with other services within the compute session, it is recommended to specify a port number between 10000-65000. However, if SSH/SFTP connections are made by two or more compute sessions at the same time, the second SSH/SFTP connection cannot use the designated port (since the first SSH/SFTP connection has already taken it), so a random port number will be assigned.

Note

If you want to use your own SSH keypair instead of id_container, create a user-type folder named .ssh. If you create authorized_keys file in that folder and append it with the contents of your SSH public key, you can connect by SSH/SFTP through your own SSH private key without having to download the id_container after creating a compute session.

Note

If you receive the following warning message, try again after changing the permission of the id_container to 600. (chmod 600 <id_container path>)

For Windows / FileZilla¶

Backend.AI Web-UI app supports OpenSSH-based public key connection (RSA2048). To access with a client such as PuTTY on Windows, a private key must be converted into a ppk file through a program such as PuTTYgen. You can refer to the following link for the conversion method: https://wiki.filezilla-project.org/Howto. For easier explanation, this section will describe how to connect to SFTP through FileZilla client on Windows.

Refer to the connection method on Linux/Mac, create a compute session, check the connection port and download id_container. id_container is an OpenSSH-based key, so if you use a client that supports only Windows or ppk type keys, you must convert it. Here, we will convert through the PuTTYgen program installed with PuTTY. After running the PuTTYgen, click on the import key in the Conversions menu. Select the downloaded id_container file from the file open dialog. Click the Save private key button of PuTTYGen and save the file with the name id_container.ppk.

After launching the FileZilla client, go to the Settings-Connection-SFTP and register the key file id_container.ppk (id_container for clients supporting OpenSSH).

Filezilla settings to connect to compute session

Open Site Manager, create a new site, and enter the connection information as follows.

When connecting to a container for the first time, the following confirmation popup may appear. Click the OK button to save the host key.

After a while, you can see that the connection is established as follows. You can now transfer large files to /home/work/ or other mounted storage folder with this SFTP connection.

For Visual Studio Code¶

Backend.AI supports to develop with the local Visual Studio Code through SSH/SFTP connection to a compute session. Once connected, you can interact with files and folders anywhere on the compute session. In this section, we will learn how to do it.

First, you should install Visual Studio Code and the Remote Development extension pack.

Link: https://aka.ms/vscode-remote/download/extension

Install remote ssh plugin on Visual Studio Code

After installing the extension, you should configure the SSH connection for the compute sesion. In the SSH/SFTP connection dialog, click “DOWNLOAD SSH KEY” button to download the SSH private key (id_container) for the session. Also, remember the port number.

And then, setting SSH config file. Edit the ~/.ssh/config file (for Linux/Mac) or C:\Users\[user name]\.ssh\config (for Windows) and add the following block. For convenience, we set the hostname to vscode. It can be changed to any alias.

Now in the Visual Studio Code, select the Command Palette… from View menu.

Visual Studio Code can automatically detect the type of host you are connecting to. Let’s choose Remote-SSH: Connect to Host….

You will see the list of hosts in .ssh/config. Please select the host to connect, in this case, vscode.

Selecting the host name will lead you to access the remote compute session. After you are connected, you will see an empty window. You can always refer to the Status bar to see which host you are connected to.

You can then open any folder or workspace on the remote host by accessing File > Open… or File > Open Workspace… menu just as you usually would do!

Establish SSH connection with Backend.AI client package¶

This document describes how to establish an SSH connection to a compute session in environments where a graphical user interface (GUI) cannot be used.

Typically, GPU nodes that runs compute sessions (containers) cannot be accessed directly from the outside. Therefore, in order to establish an SSH or sFTP connection to a compute session, a local proxy that creates a tunnel needs to be launched to relay the connection between the user and the session. Using the Backend.AI Client package, this process is relatively simple to configure.

Prepare Backend.AI Client package¶

Prepare with Docker image¶

The Backend.AI Client package is available as a Docker image. You can pull the image from the Docker Hub with the following command:

$ docker pull lablup/backend.ai-client:22.09.18

The version of Backend.AI server can be found in “About Backend.AI” menu that appears when you click on the person icon on the top right corner of the Web UI.

../_images/check_backend_server_version.png

Run the Docker image with the following command:

$ docker run --rm -it lablup/backend.ai-client:22.09.18 bash

Check if backend.ai command is available in the container. If it is available, the help message will be displayed.

$ backend.ai

Prepare directly from host with a Python virtual environment¶

If you cannot or do not want to use Docker, you can install the Backend.AI Client package directly on your host machine. Prerequisites are:

The required version of Python may vary depending on the Backend.AI Client version. You can check the compability matrix at https://github.com/lablup/backend.ai#python-version-compatibility.
clang compiler might be needed
zstd package might be needed if you are using indygreg Python binary.

It is recommended to use a Python virtual environment to install the packages. One way is to use the statically-built Python binary from the indygreg repository. Download the binary that matches your local machine architecture from the following page and unzip it.

https://github.com/indygreg/python-build-standalone/releases

If you are using a popular x86-based Ubuntu environment, you can download and extract it as follows:

$ wget https://github.com/indygreg/python-build-standalone/releases/download/20230116/cpython-3.10.9+20230116-x86_64-unknown-linux-gnu-pgo-full.tar.zst
$ tar -I unzstd -xvf *.tar.zst

After unarchiving the binary, python directory will be created under the current directory. You can check the version of the downloaded Python by running the following command.

$ ./python/install/bin/python3 -V
Python 3.10.9

To avoid affecting other Python environments on the system, it is recommended to create a separate Python virtual environment. When you run the following command, a Python virtual environment will be created under the directory .venv..

$ ./python/install/bin/python3 -m venv .venv

Activate the virtual environment. Since a new virtual environment has been activated, only the pip and setuptools packages will be installed when you run the pip list command.

$ source .venv/bin/activate
(.venv) $ pip list
Package    Version
---------- -------
pip        21.3.1
setuptools 59.4.0

Now, install the Backend.AI Client package. Install the client package according to the server version. Here, we assume that the version is 22.09. If an installation-related error occurs with the netifaces package, you may need to lower the versions of pip and setuptools. Check if the backend.ai command is available.

(.venv) $ pip install -U pip==22.0.4 && pip install -U setuptools==58.1.0
(.venv) $ pip install -U backend.ai-client~=22.09
(.venv) $ backend.ai

Setting up server connection for CLI¶

Create a .env file and add the following content. Use the same address for webserver-url that you use to connect to the Web UI service from your browser.

BACKEND_ENDPOINT_TYPE=session
BACKEND_ENDPOINT=<webserver-url>

Run the following CLI command to connect to the server. Enter the email and password that you use to log in from your browser. If everything goes well, you will see the message Login succeeded.

$ backend.ai login
User ID: myuser@test.com
Password:
✓ Login succeeded.

SSH/SCP Connection to Computation Session¶

Create a compute session from the browser by mounting the folder where you want to copy the data. You can create the session using CLI as well, but for convenience, let’s assume that you have created it from the browser. Remember the name of the created compute session. Here, we assume it is ibnFmWim-session.

If you simply want to SSH, execute the following command:

$ backend.ai ssh ibnFmWim-session
∙ running a temporary sshd proxy at localhost:9922 ...
work@main1[ibnFmWim-session]:~$

If you want to download the SSH key file and explicitly run the ssh command, you need to first run the following command to launch a local proxy service that relays connection from the local machine to the computation session. You can specify the port (9922) to use on the local machine with the b option.

$ backend.ai app ibnFmWim-session sshd -b 9922
∙ A local proxy to the application "sshd" provided by the session "ibnFmWim-session" is available at:
  tcp://127.0.0.1:9922

Open another terminal window on your local machine. Move to the working directory where the .env file is located, and download the SSH key automatically generated in the compute session.

$ source .venv/bin/activate  # Reactivate the Python virtual environment as this is a different terminal
$ backend.ai session download ibnFmWim-session id_container
Downloading files: 3.58kbytes [00:00, 352kbytes/s]
✓ Downloaded to /*/client.

You can use the downloaded key to SSH as follows. Since you launched the local proxy on port 9922, the connection address should be 127.0.0.1 and the port should be 9922. Use the user account work for the connection.

$ ssh \
    -o StrictHostKeyChecking=no \
    -o UserKnownHostsFile=/dev/null \
    -i ./id_container \
    -p 9922 \
    work@127.0.0.1
Warning: Permanently added '[127.0.0.1]:9922' (RSA) to the list of known hosts.
work@

Similarly, you can use the scp command to copy files. In this case, you should copy the files to the mounted folder within the compute session to preserve them even after the session has been terminated.

$ scp \
    -o StrictHostKeyChecking=no \
    -o UserKnownHostsFile=/dev/null \
    -i ./id_container \
    -P 9922 \
    test_file.xlsx work@127.0.0.1:/home/work/myfolder/
Warning: Permanently added '[127.0.0.1]:9922' (RSA) to the list of known hosts.
test_file.xlsx

When all the tasks are completed, press Ctrl-C on the first terminal to cancel the local proxy service.