How job scheduling systems can enhance your HPC data science workloads
Job scheduling systems offer a powerful solution for managing complex high-performance workloads. One innovative use case for data science and research is integrating a job scheduler like Slurm, with an integrated scripting environment like JupyterHub. Slurm’s resource management capabilities and JupyterHub’s collaboration make these resources an apt combination for streamlining these types of workflows.
How these applications work
Slurm is a cluster management and job scheduling system. By allocating HPC resources to user jobs, it helps your workflows to run more efficiently. It also provides features such as job submission, queueing, and resource management.
For data science and research workloads, this job scheduler works best when paired with JupyterHub. This multi-user web application allows you to launch and share interactive Python notebooks containing live code, equations, visualizations, and narrative text. It’s highly flexible (with uses including data cleaning and transformation, numerical simulation, statistical modeling, machine learning and more) as well as scalable, which makes it a popular choice for collaborative data science projects.
Using JupyterHub, you can easily deploy your scripts to Slurm for efficient scheduling.
The integration of these application offers several key advantages, such as:
Enhanced Resource Utilisation
Slurm's ability to effectively allocate resources offers efficient performance for intensive HPC tasks. This helps you run your workloads in a more sustainable way, saving you time, money and energy.
Improved Workflow Efficiency
Combining efficient job scheduling with interactive project management allows you to streamline your workflows.
Scalability
Both applications are scalable, so they can meet growing computational demands.
How to implement these applications
Integrating Slurm and JupyterHub involves several key steps:
Deploy JupyterHub on a suitable infrastructure, configuring user authentication and authorisation.
Configure Slurm to manage the HPC cluster's resources and define job scheduling policies.
Establish communication between Slurm and JupyterHub to enable job submission and monitoring.
Thoroughly test the integration and fine-tune configuration parameters for optimal performance.
Overall, the integration of these applications provides an efficient platform for intensive workloads. By managing resources and streamlining workflows, your business can make the most of its HPC infrastructure. Whilst the setup process may require technical expertise, it’s worth it for the boost in efficiency and productivity of your data science and research workloads.
If you’d like to set up a job scheduler but you’re not sure where to start, an experienced member of our team will be able to help.