Dockers, Images and Kubernetes
Researchers are typically proficient in working with Docker. Docker is an isolation level above the operating system which allows creating your own bundle of operating system + deep learning environment and packaging it within a single file. The file is called a docker image.
You create a container by starting a docker image on a machine.
Run:AI is based on Kubernetes. At its core, Kubernetes is a an orchestration software above Docker: Among other things, it allows location abstraction as to where the actual container is running. This calls for some adaptation to the researcher's workflow as follows.
If your Kubernetes cluster contains a single GPU node (machine), then your image can reside on the node itself (in which case, when submitting workloads, the researcher must use the flag --local-image).
If your Kubernetes cluster contains more than a single node, then, to enable location abstraction, the image can no longer reside on the node itself. It must be relocated to an image repository. There are quite a few repository-as-a-service, most notably Docker hub. Alternatively, the organization can install a private repository on-premise.
Day to day work with image located remotely is almost identical to local work. The image name now contains its location. For example: nvcr.io/nvidia/pytorch:19.12-py3 is a pytorch image that is located in nvcr.io which is the Nvidia image repository on the web.
Deep learning is about data. It can be your code, the training data, saved checkpoints, etc.
If your Kubernetes cluster contains a single GPU node (machine), then your data can reside on the node itself.
If your Kubernetes cluster contains more than a single node, then, to enable location abstraction, the data must sit outside the machine, typically on network storage. The storage must be uniformly mapped to your container when it starts (using the -v command).
Working with Containers
Starting a container using docker usually involves a single command line with multiple flags. A typical example:
docker run --runtime=nvidia --shm-size 16G -it --rm -e HOSTNAME=`hostname` \
-v /raid/public/my_datasets:/root/dataset:ro \
The docker command docker run should be replaced with a Run:AI command runai submit. The flags are usually the same but some adaptation is required. A complete list of flags can be found here: https://support.run.ai/hc/en-us/articles/360011436120-runai-submit .
There are similar commands to get a shell into the container (runai bash), get the container logs (runai logs) and more. For a complete list see the Run:AI CLI reference.
Schedule an Onboarding Session
It is highly recommended to schedule an onboarding session for researchers with a Run:AI customer success professional. Run:AI can help with the above transition, but adding to that, we at Run:AI have also acquired a large body of knowledge on data science best practices which can help streamline the researcher work as well as save money for the organization.
Researcher onboarding material also appears here: https://support.run.ai/hc/en-us/articles/360012125099-Researcher-Onboarding-Presentation