mini-slorm 0.1.4

A lightweight, local job scheduler inspired by SLURM for managing AI/ML workloads

Mini-SLURM: A Local HPC Scheduler for AI/ML Workloads

Mini-SLURM is a lightweight, local job scheduler inspired by SLURM (Simple Linux Utility for Resource Management). It's designed to run and manage realistic AI/ML workloads (hyperparameter sweeps, CPU-bound simulations, data preprocessing) on a single-node local machine, providing a sandbox for experimenting with scheduling policies relevant to real systems.

Features

• Job Submission: Submit jobs with CPU and memory requirements via CLI
• Resource Management: Track and enforce CPU and memory constraints
• Priority Scheduling: Priority-based scheduling with FIFO within priority levels
• Persistent Queue: SQLite-based persistent job queue
• Comprehensive Logging: Per-job stdout/stderr logging with metrics
• Rich CLI Interface: Multiple commands for job management and monitoring
• Cross-Platform: Works on macOS and Linux

Key Features

Elastic Jobs

Mini-SLURM supports elastic/auto-resizing jobs that can dynamically scale their resource allocation - a feature that traditional SLURM does not support. Elastic jobs can scale up when resources are available and scale down when high-priority jobs arrive.

Example:

mini-slurm submit --elastic --cpus 2 --min-cpus 2 --max-cpus 8 --mem 4GB python elastic_training.py

Topology-Aware Scheduling

Mini-SLURM supports topology-aware scheduling similar to SLURM's topology plugin. The scheduler understands network switch hierarchy and prefers allocating nodes that are "close" (same leaf switch) over nodes that are "far" (crossing core switches), optimizing for network locality.

Requirements

• Python 3.8+
• psutil (for enhanced CPU/memory monitoring)

Installation

Install from PyPI

pip install mini-slorm

Install from source

1. Clone or download this repository:

git clone https://github.com/InduVarshini/mini-slurm.git
cd mini-slurm

2. Install in development mode:

pip install -e .

Or install normally:

pip install .

Install dependencies only

pip install psutil

Quick Start

1. Start the Scheduler

In one terminal, start the scheduler daemon:

mini-slurm scheduler

The scheduler will run continuously, managing and executing jobs.

2. Submit Jobs

In another terminal, submit jobs:

# Submit a simple job
mini-slurm submit --cpus 2 --mem 4GB --priority 0 python train.py

# Submit a high-priority job
mini-slurm submit --cpus 4 --mem 8GB --priority 10 python hyperparameter_sweep.py

# Submit a CPU-intensive simulation
mini-slurm submit --cpus 8 --mem 2GB python run_simulation.py

3. Monitor Jobs

# View all jobs in queue
mini-slurm queue

# View only pending jobs
mini-slurm queue --status PENDING

# View job details
mini-slurm show <job_id>

# View system statistics
mini-slurm stats

Examples

Basic Usage

# Submit jobs
$ mini-slurm submit --cpus 2 --mem 4GB --priority 5 "echo 'Hello from mini-slurm!'"
Submitted job 15
  cpus=2, mem=4096MB, priority=5

# View queue
$ mini-slurm queue
  ID     STAT CPU MEM(MB) PRI  WAIT(s)   RUN(s)  ELASTIC              SUBMIT COMMAND
  11  PENDING   2    4096   0      0.0      0.0          2025-12-09 19:05:33 python train.py

# View job details
$ mini-slurm show 1
Job 1
  Status:      COMPLETED
  CPUs:        2
  Mem (MB):    2048
  Wait time:   38.84s
  Runtime:     5.60s

CLI Commands

Get help for any command:

$ mini-slurm --help
usage: mini-slurm [-h] {submit,queue,show,cancel,scheduler,stats} ...

Mini-SLURM: a tiny local HPC-style job scheduler

positional arguments:
  {submit,queue,show,cancel,scheduler,stats}
    submit              Submit a job
    queue               Show job queue
    show                Show job details
    cancel              Cancel a pending job
    scheduler           Run the scheduler loop
    stats               Show system statistics and job metrics

options:
  -h, --help            show this help message and exit

submit

Submit a new job to the queue.

mini-slurm submit --cpus <num> --mem <size> [--priority <num>] <command>

Arguments:

• --cpus: Number of CPUs required (integer)
• --mem: Memory required (e.g., 8GB, 1024MB, 2g, 512m)
• --priority: Job priority (higher = scheduled earlier, default: 0)
• command: Command to execute (can be multiple words)

Examples:

mini-slurm submit --cpus 4 --mem 8GB python train.py --epochs 100
mini-slurm submit --cpus 2 --mem 4GB --priority 5 bash preprocess.sh

queue

Display the job queue.

mini-slurm queue [--status <status>]

Options:

• --status: Filter by status (PENDING, RUNNING, COMPLETED, FAILED, CANCELLED)

Output columns:

• ID: Job ID
• STAT: Job status
• CPU: CPUs requested
• MEM(MB): Memory requested (MB)
• PRI: Priority
• WAIT(s): Wait time in seconds
• RUN(s): Runtime in seconds
• SUBMIT: Submission timestamp
• COMMAND: Command executed

show

Display detailed information about a specific job.

mini-slurm show <job_id>

Output includes:

• Job metadata (user, status, priority, command, resources)
• Timestamps (submitted, started, ended)
• Performance metrics (wait time, runtime, return code)
• Log file paths (stdout, stderr)
• CPU usage statistics (if psutil available)

cancel

Cancel a pending job.

mini-slurm cancel <job_id>

Note: Only PENDING jobs can be cancelled. Running jobs cannot be cancelled in the current version.

scheduler

Run the scheduler daemon.

mini-slurm scheduler [--total-cpus <num>] [--total-mem <size>] [--poll-interval <seconds>]

Options:

• --total-cpus: Override detected total CPUs (default: auto-detect)
• --total-mem: Override total memory (e.g., 16GB, default: 16GB)
• --poll-interval: Scheduler poll interval in seconds (default: 1.0)

Example:

mini-slurm scheduler --total-cpus 8 --total-mem 32GB --poll-interval 0.5

stats

Display system statistics and job metrics.

mini-slurm stats [--total-cpus <num>] [--total-mem <size>]

Output includes:

• System resource usage (CPUs, memory)
• Job statistics (total, running, pending, completed, failed)
• Performance metrics (average wait time, average runtime)
• Status breakdown

Job States

• PENDING: Job is queued and waiting for resources
• RUNNING: Job is currently executing
• COMPLETED: Job finished successfully (return code 0)
• FAILED: Job finished with an error (return code != 0)
• CANCELLED: Job was cancelled before execution

Scheduling Policy

Mini-SLURM uses a priority + FIFO scheduling policy:

1. Jobs are sorted by priority (higher priority first)
2. Within the same priority, jobs are scheduled in FIFO order (first submitted, first scheduled)
3. Jobs are scheduled when sufficient resources (CPUs and memory) are available
4. Resource constraints are enforced at scheduling time

Resource Enforcement

CPU Limits

• Linux: Uses taskset to pin jobs to specific CPU cores
• macOS: Sets environment variables (OMP_NUM_THREADS, MKL_NUM_THREADS, NUMEXPR_NUM_THREADS) to limit thread counts for common libraries

Memory Limits

• Uses resource.setrlimit() to set memory limits (RSS)
• On macOS, memory limits may be advisory depending on system configuration
• Jobs that exceed memory limits will be terminated by the OS

Logs and Output

Each job's output is logged to:

• Stdout: ~/.mini_slurm_logs/job_<id>.out
• Stderr: ~/.mini_slurm_logs/job_<id>.err

Logs persist after job completion for debugging and analysis.

Database

Job metadata is stored in SQLite at ~/.mini_slurm.db. The database persists across scheduler restarts, allowing you to:

• View historical job information
• Analyze job performance metrics
• Track resource usage over time

Example Workloads

# Hyperparameter sweep
for lr in 0.001 0.01 0.1; do
    mini-slurm submit --cpus 2 --mem 4GB \
        python train.py --learning-rate $lr
done

# CPU-intensive simulation
mini-slurm submit --cpus 8 --mem 2GB python run_simulation.py

# High-priority preprocessing
mini-slurm submit --cpus 4 --mem 8GB --priority 10 \
    python preprocess_data.py

Limitations

• Single-node only (no multi-node support)
• No job dependencies or workflows
• No preemption of running jobs (except elastic scale-down)
• Memory limits may be advisory on macOS
• CPU affinity on macOS relies on library-level thread limits

Future Enhancements

Potential extensions for advanced scheduling policies:

• Job dependencies and workflows
• Preemption and checkpointing
• Fair-share scheduling
• Backfill scheduling
• Multi-node support
• GPU resource management
• Job arrays

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please visit the GitHub repository for more information.