Use Case
Let an agent babysit your training jobs
Securing GPUs, submitting jobs, watching logs, retrying failures. AI agents take over the waiting work of model development on Seyval.
The challenge
Provisioning GPU instances, managing cost, and cleaning up eats into development time.
Monitoring jobs and retrying failures is wait-heavy routine work that keeps a human pinned.
On-prem HPC/Slurm clusters and cloud each have different workflows, making it a burden to use both.
How Seyval solves it
Just name your compute
Submit jobs by compute type, from T4 to H100. Provisioning and teardown are automatic.
Agents watch and react
Logs are tracked in real time, failures are triaged as code vs. infrastructure, and the agent can decide to retry.
Cloud and BYO, one interface
Managed GPUs and your own Slurm cluster work through the same interface — pick per workload.
Why Seyval
Zero GPU ops
The platform handles acquiring, configuring, and releasing GPUs so you can focus on the model.
Costs you can see
Every run records its execution cost, so you know what each experiment spent.
Use the compute you own
Connect your existing HPC/Slurm cluster and it becomes a first-class execution target for your agents.