Blog / Introducing Slurm Support: Scale Your ML Workflows with Ease
Introducing Slurm Support: Scale Your ML Workflows with Ease

Introducing Slurm Support: Scale Your ML Workflows with Ease

Tarek Oraby

We're excited to announce that Valohai, the leading MLOps platform, now supports Slurm, the popular open-source workload manager often used in high-performance computing (HPC) environments. With Slurm-based clusters at their disposal, Valohai users can now scale their machine-learning workflows with unprecedented ease and efficiency.

How It Works

Valohai's Slurm support enables teams to easily connect their Slurm clusters with Valohai by running a simple setup script. Once Slurm clusters are connected with Valohai, data scientists can run their machine-learning experiments on Slurm clusters directly from the Valohai platform's UI or command line. This integration streamlines the process of managing and tracking machine learning experiments, ensuring that teams can focus on innovation rather than infrastructure management.

Here's how Valohai's Slurm support simplifies the MLOps workflow:

  1. Seamless Integration: Valohai effortlessly integrates with Slurm clusters, allowing users to conduct machine learning experiments without the hassle of manual setup or management. This means less time spent on setup and more on innovation.
  2. Scalable Machine Learning Pipelines: With Slurm's robust scheduling capabilities, Valohai empowers users to scale their machine learning pipelines easily. This scalability ensures that computational workloads are handled efficiently, thereby optimizing resource usage and accelerating the experimentation process.
  3. Transparent Job Management: Valohai's intuitive pipeline management capabilities make the orchestration of ML experiments on Slurm clusters transparent. This transparency enables users to manage their jobs easily, ensuring that every experiment is tracked and executed as planned.
  4. Cost Efficiency: Leveraging Slurm clusters through Valohai not only maximizes resource utilization but also significantly reduces operational costs. By enabling a simple approach to integrating ML workflows with Slrum clusters, Valohai ensures that teams can leverage high-performance computing environments to overcome the challenges of limited GPU resources and their rising costs.
  5. Hybrid Environment Support: Combining Slurm, on-prem infrastructure, and cloud environments can be complex. Valohai streamlines the scaling and management of machine-learning experiments across these varied setups, enabling teams to easily scale and manage their experiments across various platforms.

Valohai's Slurm support marks a milestone in our ongoing commitment to simplifying the complexities of machine learning workflows.

Are you interested to learn more? Request a meeting with us today to see how Valohai's Slurm support can help you scale your ML workflows effortlessly.

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