Accessing GPU/CPU powered instances
There are several ways you can access your GPU/CPU-powered instances, including Jupyter Lab, Command Line Interface (SSH, Terminal), or connecting through your favorite editor like VS Code.
Connect to JupyterLab
Click on the ▶️ button to launch JupyterLab. JupyterLab is a powerful open source development environment for notebooks, code and data. Some of the things you can do using JupyterLab are
- Open a terminal (command line interface)
- Upload and Download files.
- Open CSV, PDF, image files. (The supported file size could vary based on the computer you are running).
- Build powerful dashboards
Check out the amazing demo video on how to use JupyterLab, you can also explore more about it in the docs.
Connect via SSH
Before launching the instance, make sure to add your computer SSH keys here to connect from the terminal.
To obtain the required string to SSH into the instance, click on the SSH button
If you have choosen a custom location to store your ssh key, then do not forget to add the location as
ssh -i /path/to/your/key/id_rsa -P port_number domain_name
If you are new and all you need is a terminal to install some libraries, we recommend using the terminal from JupyterLab.
+ button on upper left corner of the JupyterLab and launch a terminal.
If you have instances that were launched before adding your SSH keys, you can do one of the following to connect to those instances:
- Pause and Resume the instance.
- Destroy and launch a new instance.
How to get my local ssh keys?
If you have already created ssh keys, you can access them using
Creating a new key is also very simple. Execute the following commands on your terminal/PowerShell to generate an SSH key pair.
ssh-keygen -t rsa -b 4096
It will ask a couple of questions, you can simply press
enter to most of them.
Connect via VS Code
You can also access and run your deep learning programs straight from Visual Studio Code in 3 easy steps. To do that, you should have updated your public_ssh keys as mentioned earlier.
- Install Remote - SSH Extension.
- Add New SSH Host.
- Connect to Host.
Install the Remote-SSH Extension from the Visual Studio code extenstions.
Add & Connect to New Host
⇧⌘P for Mac and
Ctrl+Shift+P for windows to open
Connect via Jetbrains DataSpell IDE
You can also connect to the instance through Jetbrains DataSpell.
- Copy your instance JupyterLab URL using right-click on jupyter button and copying link address.
- From DataSpell Extentions -> add New Jupyter Connection
- Choose Connect to Jupyter Server using URL
- Paste Jupyter URL into Server URL
- You will find to JarvisLabs.ai notebook server Running under Files Tab.
- Thanks to Isaac for the below demonstration.