Effective cluster management can make or break an AI enterprise. When clusters are unreliable, unhealthy, and have inconsistent performance, they directly impact and compromise one of the most important goals of any AI team: getting to market fast.
For AI teams, getting to market quickly isn’t just a matter of driving company growth. It’s a matter of survival. Take the major boom behind ChatGPT, for example. According to Forbes, the groundbreaking LLM had one million users within the first five days of launch, demonstrating a clear relationship between service availability and consumer demand.
The adoption rates speak for themselves: The most capable AI models and solutions in the market usually see the biggest wins.
Here’s the Catch-22: AI enterprises need to build highly performant training clusters at supercomputing scale to evolve their AI models, but managing those clusters can be their biggest roadblock to a fast time to market.
That’s where CoreWeave Mission Control comes in. The following three critical features assist in addressing major challenges to cluster health management—and help teams get their products to market quickly and efficiently.
1. Continuous testing and validation for high-quality hardware
Think of AI supercomputers as ultra-sophisticated race cars. They’re built for ultimate speed and high performance. They consist of tens of thousands of critical hardware components and require consistent fine-tuning and maintenance for maximum results.
Just like race cars need consistent maintenance, AI supercomputers need continuous testing and validation as a central part of cluster management. What separates good from great cluster management? Checking that all working parts (whether that’s engines and wheels or nodes and GPUs) are healthy throughout their lifecycle—and keeping interruptions to a minimum.
With CoreWeave Mission Control, your teams won’t be burdened with the responsibility of continuous testing, validation, and remediation. Our solution handles that for you.
- Fleet Lifecycle Controller: We put each and every node through the trenches of testing specifically for AI’s requirements of high-performance computing. Fleet Lifecycle Controller maintains node health upon bring-up and throughout the entire node lifecycle. Meanwhile, automated processes built into our stack continuously monitor nodes for discrepancies or anomalous behaviors—helping to stop major interruptions before they even happen.
- FleetOps: Our FleetOps team stays on the lookout for common signs of deterioration throughout our fleet. Get the benefits of extensive research in maintaining cluster health across the vast AI space. That bedrock of experience enables us to identify and remediate issues as subtle as GPUs solving 1+1=1.999999.
2. Enhanced transparency into hardware state
Unless you’re working with resources on-prem, your teams might find themselves building AI projects in a black box, like when using traditional cloud service providers with virtualized GPU nodes. In other words, when issues or interruptions happen, it’s extremely difficult to pinpoint what exactly went wrong with the speed and efficiency that AI projects need.
While working in a black box might seem convenient, there are a few key reasons why black boxes cannot best serve AI enterprises.
- You won’t know what went wrong. Black boxes inherently gatekeep data on node and cluster health that indicate an issue’s root cause.
- You’re dependent on vendors for action. Because your teams don’t have access to the root cause, they can’t take action when interruptions happen. Only those with access—i.e., cloud providers—to the right data can.
- You can’t learn from prior interruptions. Without transparency into the source of each issue or interruption, AI teams cannot implement targeted preventative measures to keep away repeat offenders.
CoreWeave Mission Control provides AI enterprises with the transparency they need to build to bigger and better heights. Leveraging our managed bare metal compute stack, our customers can collect and receive alerts on metrics across their compute including CPU, GPU, NVLink, Storage and Networking—with dashboards visualizing anything from entire clusters to individual jobs.
Visibility helps keep interruptions to a minimum—and lets you know why they happen when they do. Additionally, your teams can choose from a flexible sliding scale of transparency. Opt to receive data as detailed as GPU temperatures and performance speed, or choose a plug-and-play experience where our teams monitor all those details for you.
3. Proactive reliability
Responding to issues as they arise, even if responses are quick, is not enough if AI enterprises want to get models to market faster than the rest. They need an infrastructure that enables proactive reliability. That means identifying—and anticipating—the root cause to prevent it from happening.
Hardware issues and compromised node health cause job interruptions. These issues can be persistent, pervasive, and compounding. They both slow down model training and affect overall training efficiency, increasing the time, resources, and cost of training large language models.
These issues can mean more spent waiting around and firefighting and less time getting models ready to go to market.
CoreWeave Mission Control utilizes previous learnings to grow beyond those issues—so they have a better chance of not happening again. We employ a seasoned team of FleetOps engineers with extensive experience identifying and remediating node issues for peak AI cluster management. Additionally, our CloudOps team helps to ensure resilient, reliable, and performant cloud infrastructure and rigorously maintains all cloud operations.
Here’s specifically how Mission Control helps unlock superior reliability:
- Node Lifecycle Controller (NLCC): We mitigate interruptions by continuously monitoring nodes with proactive health checks. As soon as unhealthy nodes are detected, NLCC swaps out and replaces problematic nodes—making interruptions shorter, less frequent, and less expensive.
- Observability: We deliver cutting-edge capabilities to measure, monitor, and diagnose issues with node and cluster health. Our observability platform enables best-in-class visibility into the metrics your teams need to monitor nodes efficiently and identify root cause of interruptions—to help prevent them from happening in the first place.
Cluster management: It’s all about layers
Ultimately, efficient cluster management comes down to a strong foundation of reliability engineering—or the systematic process of creating reliable systems. But one helpful feature alone does not make a reliable system. True reliability depends on overlapping layers working together to increase total reliability and decrease risk.
CoreWeave Mission Control is purpose-built to provide your teams with a consistent, convenient, and reliable training job experience through layers of processes, systems, and people.
Relax and step away from the Jira ticket form. Mission Control’s got it covered.
Interested in seeing how Mission Control could work for your teams? Drop us a line.
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