WrapSlurm 0.1.10

A utility for managing SLURM jobs and nodes with enhanced display features.

WrapSlurm

WrapSlurm is a powerful and user-friendly wrapper for SLURM job management, designed to simplify job submission, resource querying, log monitoring, and cancellation in SLURM environments. With a suite of commands like wrun, wlog, wqueue, winfo, and wk, WrapSlurm enhances productivity for researchers and engineers working in high-performance computing (HPC) clusters.

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Features

• Simplified Job Submission (wr):

  • Automatically detect optimal resources (nodes, partitions, CPUs, memory, GPUs) based on the cluster's configuration.
  • Friendly summaries before each run highlight auto-detected values and log locations.
  • Persist preferred defaults (e.g., partition, account, log directory) with --save-defaults.
  • Automatically use the partition's maximum runtime when no explicit --time is provided.
  • Support for interactive and non-interactive SLURM jobs, plus a convenient --dry-run preview mode.
  • Customizable SLURM settings like time, tasks per node, exclusions, job names, and output directories.

• Log Monitoring (wl):

  • Watch real-time SLURM logs for specific job IDs or the latest job.

• Job Cancellation (wk):

  • Quickly terminate jobs (optionally with a signal) using a friendly wrapper around scancel.

• Queue Visualization (wq):

  • View and analyze job queues in a prettified table format with color-coded states.

• Node Resource Querying (wi):

  • Display detailed SLURM node information, including memory, CPU, and GPU usage.

• Help / Usage (ws):

  • Display a summary of all WrapSlurm commands and their usage.

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Installation

WrapSlurm is available on PyPI and can be installed using pip:

pip install wrapslurm

Post-Installation Notes

If the scripts wrun, wlog, wqueue, winfo, and wk are installed in a directory not included in your system's PATH (e.g., ~/.local/bin), you may need to update your PATH environment variable:

1. Add the following line to your shell configuration file (~/.bashrc or ~/.zshrc):

   export PATH="$PATH:$HOME/.local/bin"
   

2. Reload your shell:

   source ~/.bashrc  # or source ~/.zshrc
   

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Usage

1. Submit a Job (wrun)

Basic Usage:

Submit a script with auto-detected resources:

wr ./train_script.py --epochs 10

wr now shows a colorized summary of the resources that will be requested, including values auto-detected from sinfo and those loaded from saved defaults.

wr now shows a colorized summary of the resources that will be requested, including values auto-detected from sinfo and those loaded from saved defaults.

Specify Resources:

Submit a job with explicit resources:

wr --nodes 2 --partition gp4d --account ENT212162 --cpus-per-task 8 --memory 200G --gpus 4 ./train_script.py

You can also name the job, change where helper scripts are stored, or choose a custom log directory:

wr --job-name my-training --script-dir ./sbatch --report-dir ./logs python train.py

Interactive Mode:

Start an interactive session:

wr

Use wr --interactive --nodes 2 to override the automatic detection while still launching an interactive shell.

Save Your Defaults:

You can persist frequently used settings (e.g., partition, account, log directory) so future runs pick them up automatically:

wr --save-defaults --partition gp4d --account ENT212162 --report-dir ./slurm-report

Defaults are stored in ~/.config/wrapslurm/defaults.json. Running wr --save-defaults stores the provided flags and exits without submitting a job.

Full Help:

View all available options:

wr --help

Preview the Generated Script:

wr --dry-run python train.py

Dry runs print the exact sbatch script so you can review the environment setup before submitting.

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2. Monitor Logs (wlog)

wlog streams SLURM output with tail -n 20 -f so you can follow job progress without the extra load from watch.

Logs are written to ./slurm-report/%j.out and ./slurm-report/%j.err by default.

Watch the Latest Log File:

wl

Watch Logs for a Specific Job ID:

wl --job-id 12345678

To inspect stderr instead, open ./slurm-report/12345678.err with your preferred tool.

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3. Cancel a Job (wk)

Send scancel commands without memorizing flags:

wk 12345678

Cancel multiple jobs in one go:

wk 12345678 12345679

Pass through additional options such as a signal or user scope:

wk 12345678 --signal SIGINT
wk --user alice 12345680

All options are forwarded to scancel, so you can combine them as needed.

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4. View Job Queue (wqueue)

Display the job queue in a table format:

wqueue

Filter or split GPU vs. CPU-only jobs:

wq --gpu     # only jobs that request one or more GPUs
wq --cpu     # only CPU-only jobs (no GPUs)   (alias: --no-gpu)
wq --split   # GPU jobs and CPU-only jobs shown in two separate tables

Short flags -g (--gpu), -c (--cpu) and -s (--split) are also supported. Run wq --help for the full list of options.

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5. Query Node Resources (winfo)

Basic Usage:

winfo

Include Down or Drained Nodes:

winfo --include-down

Display GPU Usage Graph:

winfo --graph

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Example Workflow

1. Query available resources:

   wi
   

2. Submit a job:

   wr --account xxxxxx --time 2-00:00:00 ./train_script.py
   

3. Monitor job logs:

   wl
   

4. Check the queue:

   wq
   

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Development

Cloning the Repository

git clone https://github.com/yourusername/wrapslurm.git
cd wrapslurm

Install Dependencies

Install the required Python packages:

pip install -r requirements.txt

Run Tests

Execute unit tests:

pytest

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Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature-name
   

3. Commit your changes:

   git commit -m "Add feature-name"
   

4. Push to your fork:

   git push origin feature-name
   

5. Submit a pull request.

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License

This project is licensed under the MIT License.

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Acknowledgments

Special thanks to the SLURM community for making HPC resource management accessible to researchers worldwide.

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