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This session will cover late-breaking developments in Charliecloud, such as recent/upcoming new features, notable bugs, and requests for feedback. The specific agenda is TBD.
I am a staff scientist at Los Alamos National Laboratory. Prior to Los Alamos, I was a research staff member at IBM Research. I hold a Ph.D. in computer science from the University of Minnesota and a B.A., also in computer science, from Macalester College.My work focuses on large-scale... Read More →
Additional Authors: Tolulope Olatunbosun, Phil Romero & Mike Mason, Los Alamos National Laboratory
Retrieval Augmented Generation (RAG) systems improve the response relevance of LLMs (Large Language Models) by limiting the context to a document corpus. RAG systems have seen broad deployment as document summarization engines and AI chatbots. However, deploying these systems often assumes a privileged and “cloudy” environment with multi-container orchestration (i.e. docker compose) and unfettered internet access to pull resources (e.g. software, data, and models) on-the-fly. As an alternative, we leveraged Charliecloud’s NVIDIA GPU support capabilities to deploy a RAG chatbot in an unprivileged HPC environment where resources are pre-staged. We demonstrate the deployment of AI Chatbots using Charliecloud on a variety of hardware and software versioning.
This talk will present a setup using Charliecloud to spawn virtual HTCondor compute nodes inside of jobs submitted to a SLURM cluster. The actual containers are distributed via CernVM-FS and mounted unprivilegedly at the HPC site using cvmfsexec. The spawned HTCondor nodes integrate into a larger overlay batch system to run High-Throughput compute jobs from the Worldwide LHC Computing Grid community.
Charliecloud allows to make this setup very portable with its lightweight design, minimal system dependencies and simplicity of use. CernVM-FS which is optimized for distribution of large numbers of small files of which only few might be accessed proves an ideal fit for distribution of directory-format container images. In combination with HTCondor which focuses on optimizing the total throughput and easily handles large numbers of jobs, compute resources can be used opportunistically by integrating them fully unprivilegedly into an overlay batch system. The workloads themselves can again use unprivileged containers to enable the use of user-defined software stacks.
Charliecloud is used in conjunction with Gitlab-CI to build out a matrix of system-representative containers that can be used for building target system-compatible binaries for automated building and testing production codes on NNSA lab machines. This presentation covers the method of generating the base containers as well as a couple of use cases where they have proven helpful.
Charliecloud has been in the Debian archive since the early development days of Charliecloud. It was initially packaged by Lucas Nussbaum at the end of 2017/beginning of 2018. The speaker joined the packaging effort in January 2018 and continuously contributed to it since then. This talk will give a brief introduction into Debian and describe how its tool set was useful to improve the Debian Charliecloud package and feed improvements back into the upstream project. But also the information flow from upstream authors to package maintainers has been exemplary. This presentation will provide a few examples showing this fruitful interplay.
Kubernetes automates container deployment and management across environments. HPC users can benefit from Kubernetes to support the increasing demand for novel workflows, especially in AI. Kubernetes' declarative approach allows users to schedule, scale, and maintain metrics on containers while supporting multiple container runtimes. Charliecloud can enhance HPC workloads when integrated with Kubernetes. However, Kubernetes only supports container runtimes that implement the Container Runtime Interface (CRI), which Charliecloud does not. To address this, we developed a prototype CRI-compatible server for Charliecloud, allowing Kubernetes to manage pods and to create, start, and track Charliecloud containers. Despite Kubernetes expecting certain features that Charliecloud does not use, such as network namespaces, we show that the two systems can still communicate effectively. Our implementation requires 700 lines of new code, fewer than 50 lines of modification to Charliecloud, and no changes to Kubernetes. This demonstrates that Kubernetes and Charliecloud are compatible tools, advancing scientific workflows that require large compute power.
Build and Execution Environment (BEE) is a workflow orchestration system designed to build containerized HPC applications and orchestrate workflows across HPC and cloud systems. BEE integrates with existing tools and infrastructure in the scientific computing ecosystem, making it an ideal choice for large-scale scientific simulations. The use of these tools and standards allows for efficient management of the provisioning, scheduling, and monitoring of individual tasks, as well as providing flexibility, portability, and reproducibility of workflows. This presentation will highlight how BEE leverages Charliecloud—as one of its container runtimes—to facilitate unprivileged builds, pull images from registries, and run containerized applications.
I am a staff scientist at Los Alamos National Laboratory. Prior to Los Alamos, I was a research staff member at IBM Research. I hold a Ph.D. in computer science from the University of Minnesota and a B.A., also in computer science, from Macalester College.My work focuses on large-scale... Read More →
