FastGPU took on a challenge to utilize the capabilities of FastGPU Stack to cover UCU's educational and research goals.
FastGPU proceeded to adapt and deploy FastGPU Stack on AWS VPC. It received an individual domain to facilitate access.
The participants' access to the study materials through FastGPU's platform was set up and enabled. To ensure smooth performance, the platform was integrated with ownCloud, which was deployed on a separate EC2 instance, with a voluminous EBS storage. A separate folder for lecturers was created; they could move educational content to that folder so that it could be instantly available to the students.
Then, an AMI was configured. It featured Windows Server, Matlab, and NICE DCV for desktop access via the browser. On top of that, a separate Docker image featuring Jupyter Notebook/Lab and other libraries, such as TensorFlow, Theano, Pandas, OpenCV, Keras, and Matplotlib was configured.
The participants were given the tools to run, stop, or delete environments created on top of a ready-to-use image and a specific EC5 C5 instance, with an automatically added storage on top. They could also run, stop, or delete Jupyter environments created on top of a Docker image and a standardized AMI image with nvidia-docker and EC2 P2/P3 instances, with an automatically supplied storage on top.
Along the way, FastGPU provided tech support to the UCU. Instances of the prepared environments, with all the required libraries, were kept track of to implement updates when required. The participants of the course could reach out to the FastGPU team for guidance.