Accessing GPU-enabled workstations can present some challenges for data scientists in their day-to-day work. If an organization decides to provide laptops with GPUs, it can result in higher costs for maintenance and the hardware may be comparatively under-powered for the workloads that need to be run during development. Desktop workstations are another option, but they are not as convenient, portable, or available wherever the data scientist needs to work. To provide data scientists with the power they require for their daily development tasks an ideal solution would include the ability to run a GPU-enabled environment on-demand in the cloud while still maintaining the Integrated Development Environment workflow. This can be achieved by combining a GPU-enabled instance within OCI, Visual Studio Code, the Remote Development Extension, and the Python extension.
Visual Studio Code is growing in popularity due to its extensibility via single-click downloadable extensions. The Remote Development Extension seamlessly integrates a cloud-hosted development environment with the entire feature-set of the IDE. Combined with the Python extension, data scientists can use a familiar set of tools with a cloud-hosted GPU workstation from any device.
Compute instance deployed with Visual Studio Code Remote Workstation image from the Marketplace into a subnet with public internet access that allows ingress connections on port 22.
IP Address of OCI Instance - Wait for the instance to deploy and then copy the public IP address.
Connecting to the remote workstation
The GPU-enabled remote workstation for VSCode image includes all of the common packages and drivers required to get started. Once the instance is running, open Visual Studio Code. At the bottom left, if the Remote Development extension is installed correctly there will be a green icon. If not, click the link in the prerequisites and install the extension.
This is a GPU-enabled workload, which runs through two epochs over the CIFAR dataset.
To get direct access to the instance click “Terminal” > “New Terminal” or use another terminal application and connect through SSH
That’s it! You are ready to begin developing Artificial Intelligence workloads on a OCI GPU-enabled instance!