News & Insights for the AI Journey|Thursday, October 17, 2019
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Kubernetes and Containers in HPC and AI 

Software containers and Kubernetes are important tools for building, deploying, running and managing modern enterprise applications at scale and delivering enterprise software faster and more reliably to the end user — while using resources more efficiently and reducing costs.

Recently, high performance computing (HPC) and AI have moved closer to the enterprise and can therefore benefit from a container and Kubernetes ecosystem, with new requirements to quickly allocate and de-allocate computational resources to HPC workloads. Compute capacity, be it for enterprise, HPC or AI workloads, can no longer be planned years in advance.

Getting on-demand resources from a shared compute resource pool has never been easier as cloud service providers and software vendors are continuously investing and improving their services. In enterprise computing, meanwhile, packaging software in container images and running containers is standard. Kubernetes has become the most widely used container and resource orchestrator among Fortune 5000 companies. The HPC community, led by efforts in the AI community, is picking up the concept and applying it to batch jobs and interactive applications.

Containers and HPC: Why Should We Care?

HPC and AI leaders have a hard time. There are lots of changes and new ways of thinking in software technology and IT operations. Containerization have become ubiquitous; container orchestration with Kubernetes is the new standard. Deep learning workloads are continuously increasing their footprint, and Site Reliability Engineering (SRE) has been adopted on many sites. It is hard for each of the new technologies to judge their usefulness for HPC and AI workloads.

Introduction to Kubernetes

If your engineers or operators are running a single container on their laptop, they probably use Docker for doing that. But when having multiples of containers potentially on dozens or hundreds of machines, it becomes difficult to maintain them. Kubernetes simplifies container orchestration by providing scheduling, container life-cycle management, networking functionalities and more in a scalable and extensible platform.

Major components of Kubernetes are the Kubernetes master which contains an API server, scheduler, and a controller manager. Controllers are a main concept: they watch out for the current state of resources and compare them with the expected state. If they differ, they take actions to move to an expected state. On the execution side we have the kubelet, which is in contact with the master as well as a network proxy. The kubelet manages containers by using the container runtime interface (CRI) for interacting with runtimes like Docker, containered, Singularity, or CRI-O.

Running Kubernetes or HPC Schedulers?

Kubernetes is doing workload and resource management. Sounds familiar? Yes, in many ways it shares lots of functionalities with traditional HPC workload managers (which now also manage deep learning workloads). The main differences are the workload types they focus on. While HPC/DL workload managers are focused on running distributed memory jobs and support high-throughput scenarios, Kubernetes is primarily built for orchestrating containerized microservice applications.

Compute-intensive workload managers like Univa Grid Engine added a huge number of features in the last decades. Some notable functionalities are:

–  Support for shared and distributed memory (like MPI based) jobs

–  Advance reservations for allocating and blocking resources in advance

–  Fair-share to customize resource usage patterns across users, projects, and departments

–  Resource reservation for collecting resources for large jobs

–  Preemption for stopping low prior jobs in favor for running high prior jobs

–  NUMA aware scheduling for automatically allocating cores and sockets

–  Adhere to standards for job submission and management (like DRMAA and DRMAAv2)

Workload managers are tuned for speed, throughput, and scalability, being capable of running millions of batch jobs a day and supporting the infrastructure of the most powerful systems in the world. What HPC workload managers lack are means for supporting microservice architectures, deeply integrated container management capabilities, network management, and application life-cycle management. They are primarily built for running batch jobs in different scenarios like high-throughput, MPI jobs spanning hundreds or thousands of nodes, jobs running for weeks, or jobs using special resource types (GPUs, FPGAs, licenses, etc.).

Kubernetes on the other hand is built for containerized microservice applications from the bottom- up. Notable features include:

–  Management of sets of pods. Pods consist of one or more co-located containers.

–  Networking functionalities through a pluggable overlay network

–  Self-healing through controller concept comparing expected with current state

–  Declarative style configuration

–  Load balancing functionalities

–  Rolling updates of different versions of workloads

–  Integrations in many monitoring and logging solutions

–  Hooks to integrate external persistent storage in pods

–  Service discovery and routing

Deep Learning: Simulated (left) versus predicted flow field

What Kubernetes lacks at this time is a proper high-throughput batch job queueing system with a sophisticated rule system for managing resource allocations. But one of the main drawbacks we see is that traditional HPC engineering applications are not yet built to interact with Kubernetes. This can change in the future. New kinds of AI workloads on the other hand are supporting Kubernetes already very well – in fact many of these packages are targeted to Kubernetes. The leading Kubernetes providers published a reproducible collection of reference implementations and benchmark suite for machine learning frameworks, algorithms, and systems named MLBench. At UberCloud we demonstrated the following deep learning fluid flow prediction running in containers.

Containerizing Deep Learning for Steady-State Fluid Flow Prediction
This project between Renumics and UberCloud was established to explore the benefits of containerized applications running on cloud computing resources that can be used to create a large amount of data samples in a fraction of the time a desktop computer would need to create them. More specifically, the goal of this case study was to apply artificial neural networks to solve fluid flow problems to significantly decrease time-to-solution while preserving much of the accuracy of a traditional CFD solver. Creating a large number of simulation samples is paramount to letting the neural network learn the dependencies between simulated design and the flow field around it. In this project, we wanted to explore whether the overall accuracy of the neural network can be improved when more samples are being created in our OpenFOAM fluid dynamics container on the Advania Data Centers HPCFLOW Cloud, and then used during the training of the neural network. The resulting predictions reached 98 percent accuracy with a speed up of 1600 over the detailed CFD simulation.

Can HPC Workload Be Managed by Kubernetes?

We should combine both Kubernetes and HPC workload-management systems to fully meet HPC and AI requirements. Kubernetes will be used for managing HPC containers along with all the required services. Inside the containers, not just the engineering application can be run, but also the capability to either plug into an existing HPC cluster or run an entire HPC resource manager installation (like SLURM or Univa Grid Engine) needs to be provided. In that way, we can provide compatibility to the engineering applications and can exploit the extended batch scheduling capabilities often required for training and large-scale testing of the AI algorithms. At the same time our whole deployment can be operated in all Kubernetes-enabled environments with the advantages of standardized container orchestration. We evaluated all this with using our ANSYS, COMSOL, and OpenFOAM/SLURM HPC containers and the OpenFOAM container for Deep Learning CFD mentioned above.

Ease of Administration

HPC and AI environments consist of a potentially large set of containers. The higher abstraction of container orchestration compared to self-managing container in single runtime engines provides the necessary flexibility we need to fulfill different customer requirements. Management operations like scaling the deployment are much simpler to implement and execute.

The Run-Time for Hybrid and Multi-Cloud Offers True Portability

Portability is a key value of containers. Kubernetes provides portability for fleets of containers. We can have the same experience on-premises as well as on different cloud infrastructures. Engineers can seamlessly switch the infrastructure without changes in their user interfaces. In that way we can choose the infrastructure by criteria like price, performance and capabilities. When running on premises we can start offering a true hybrid-cloud experience by providing a consistent infrastructure with the same operational and HPC application experience and seamlessly use on-demand cloud resources when required.

What’s Next?

Embracing Kubernetes for the specific requirements of HPC and engineering workload is not straightforward. But due to the success of Kubernetes and its open and extensible architecture the ecosystem is opening up for HPC applications primarily driven by the demand of new AI workloads and HPC containers.

Daniel Gruber, Burak Yenier, and Wolfgang Gentzsch are with UberCloud, a company that started in 2013 with developing HPC container technology and containerized engineering applications, to facilitate access and use of engineering HPC workload in a shared, on-premise or on-demand cloud environment. This article is based on a white paper they wrote detailing their experience using UberCloud HPC containers and Kubernetes.

 

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