Optimize Slurm Job Scheduling with Intelligent Memory Estimation
SlurmSlim is a lightweight and efficient tool designed to optimize job scheduling in Slurm by accurat ely estimating the required memory for scripts and programs. By leveraging Model Context Protocol (MCP), LLM models, file sizes, and system information, SlurmSlim helps reduce computing costs by preventing over-allocated memory requests.
- ✅ Intelligent Memory Estimation – Uses MCP and LLM models to predict the optimal memory allocation.
- ✅ Cost Reduction – Prevents excessive memory requests, lowering overall compute costs.
- ✅ File & System-Aware – Considers file sizes and system specs for precise estimation.
- ✅ Lightweight & Fast – Designed for efficiency with minimal overhead.
git clone https://github.com/JianYang-Lab/SlurmSlim.git
cd SlurmSlim
uv sync # If applicableuv run client.py server.pyEstimated Memory: 8.2 GB
Suggested Slurm Command: sbatch --mem=8500M job_script.sh
- 🔹 Saves Money – No more over-provisioning, reducing unnecessary cloud or HPC costs.
- 🔹 Improves Efficiency – Ensures jobs run smoothly without excessive memory requests.
- 🔹 Seamless Integration – Works directly with Slurm job scripts and scheduling workflows.
- Extend support for CPU & GPU resource optimization
- Integration with other job schedulers (e.g., PBS, LSF)
- Advanced machine learning models for prediction
📜 MIT License