GPU-Powered Cloud Development Environment
Hello there! This is Eftal from 1019. We’ve been optimizing unique path for over two months. We’ve observed that our POC customers require a convenient environment to test their AI models and check their progress instantly. Therefore, we’ve launched a new feature, the ‘GPU-Powered Cloud Development Environment (CDE)’.
We’ve built our CDEs using JupyterLab a widely used by Machine Learning engineers and we’ve built our CDEs using it. You don’t have to worry about managing any CUDA Driver or GPU-related Linux packages.
You can select your preferred GPU device and connect it to your JupyterLab. We install all the required packages and drivers you may need to use the system effectively.
We provide a variety of GPU options for running your code. Currently, we’re utilizing GPUs from AWS, Azure, and other third-party secure cloud providers.
Subsequently, you have to configure the volume for this instance. This arrangement allows you to dissociate your container and volume disk, to protect your code from system package interference and models.
Create GPU-Powered Notebook in seconds
We are now ready to launch our instance on the cloud! On the final screen, you can view the pricing details of your GPU-powered instance, along with the hourly rates.
After you hit the Deploy button, we find the GPU instance that you selected and install the required packages for you. You will then see the notebook details that you created.
You are now ready to use your notebook! You can view the current usage details on the 1019 Console.
When you would like to connect to the notebook 1019 configures a load balancer and zero trust tunnel for you.
You can install packages, and run your code on the selected GPU in a few minutes. If you would like to install any package on the OS you can use the Terminal tab on JupyterLab.
If you have any questions or comments you can use our Discord server or fill out the Feedback form on 1019 Console.
We’d love to hear from you!
