Getting started with Jarvislabs
One step close to AGI 😊
Access instance through JupyterLab, VSCode and SSH
Pause your instance and pay only for storage
Resume instance, start from where you left
Time to let go
Launch an instance
Choose the framework of your choice and configure the instance with
- Instance type - (On-demand, Spot, Weekly and Monthly)
- GPU type (RTX 6000Ada, A100, A6000, A5000 and RTX 5000)
- Storage - Scale up to 2TB of storage. Need more? Contact us.
- Startup script - Run your own startup script to install any library or package you want.
and hit launch. We do all the required work for you, in less than 20 seconds your instance is up and running.
On-demand instances are charged per minute. You can pause and resume the instance anytime you want. Once you pause the instance, the compute is released and can be used by other users. You will be charged only for the reserved storage.
Spot instances are discounted and work similarly to On-demand instances. These instances can get automatically paused when there is a high demand. So ensure you save your progress time to time through checkpoints.
These instances are charged per week/month. They are discounted and are ideal for long running experiments. Check the pricing here.
Accessing an instance
Once you launch an instance, you can access it in multiple ways.
- API - You can run services like Gradio, Streamlit, FastAPI, Flask, etc on port 6006, and access it using the API endpoint.
- SSH - You can SSH to the instance using the provided DNS. Ensure you have added your public key here.
All the GPU powered instances comes with required Cuda libraries and required libraries pre-installed. You can also install any library or package you want using the terminal.
Always store the data under /home, any data stored outside /home will be lost when you pause and resume the instance.
The libraries installed are lost, if you pause and resume the instance. To prevent it, you can create a separate conda environment and install the required libraries. Check here for more details.
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
A good nap 😴 can always help with new ideas 💡.
You can pause your instance by clicking the pause button. Pausing an instance frees up compute resources like GPU, CPU, and RAM. All the data will be retained and will be available when you resume.
Any libraries installed outside a custom conda environment will be lost.
You will be still charged for the storage, so if you do not plan to use the instance anytime soon, consider deleting the instance.
Resume an instance
Time to save the planet 🌎.
Click resume on a paused instance to start with your work. A new instance will be created with all your previous data. You can also modify the instance parameters like GPU type, GPU quantity and storage while resuming the instance.
Delete an instance
Time for fresh thoughts 🤔
Make sure you have backed up all the required data before deleting the instance. Once you delete the instance, all the data will be lost and cannot be recovered.