# Building Intelligent SLURM Workflows: How srunx Revolutionizes HPC Job Orchestration

High-Performance Computing (HPC) powers modern scientific research, machine learning, and data analysis. Yet, orchestrating workflows on SLURM clusters often feels like navigating a maze blindfolded, filled with bottlenecks and inefficient resource management. Imagine bringing the seamless orchestration and intelligence of modern cloud workflows to your HPC environment - this is exactly what [**srunx**](https://github.com/ksterx/srunx) does.

## Traditional HPC Workflow Pain Points

Many researchers and engineers recognize these common struggles:

- **Sequential Bottlenecks**: Entire workflow stages must finish before subsequent jobs begin, causing unnecessary delays.
- **Resource Waste**: Jobs frequently idle, waiting for unrelated dependencies.
- **Complex Scripting**: Managing intricate job dependencies often demands cumbersome bash scripts.
- **Poor Visibility**: It's challenging to monitor workflow progress clearly and efficiently.

Consider a machine learning pipeline: data preprocessing, model training, evaluation, and publishing results. Traditionally, even if training and evaluation could technically run independently post-preprocessing, they're held back by rigid dependency management.

## Introducing srunx: Intelligent Dependency Resolution

srunx revolutionizes HPC workflows with fine-grained parallel execution, starting jobs the moment their specific dependencies are met, rather than waiting for entire workflow stages.

Here's an intuitive workflow defined using srunx:

```yaml
name: ml_pipeline
jobs:
  - name: preprocess  # Data processing
    command: ["python", "preprocess.py"]
    resources:
      nodes: 1
      memory_per_node: "16GB"
    environment:
      sqsh: /home/user/megatron.sqsh

  - name: train  # Model training
    command: ["python", "train.py"]
    depends_on: [preprocess]
    resources:
      nodes: 1
      gpus_per_node: 2
      memory_per_node: "32GB"
      time_limit: "8:00:00"
    environment:
      conda: ml_env

  - name: evaluate  # Model evaluation
    path: ["/home/user/playground/evaluate.sh"]  # You can use slurm file
    depends_on: [train]

  - name: publish_results  # Publish final results
    command: ["python", "publish.py"]
    depends_on: [train, evaluate]
    environment:
      venv: /home/user/repo/.venv
```

With srunx, jobs execute exactly when they should - no earlier, no later. Once preprocess completes, train starts immediately, freeing subsequent jobs like evaluate and publish_results to run as soon as they're individually ready, dramatically improving efficiency.

## Scalable and Reliable Architecture

### Type-Safe Configuration

Leveraging Python's powerful Pydantic framework, srunx ensures configurations are validated at runtime, avoiding common pitfalls:

```python
from srunx import Job, JobResource, JobEnvironment

job = Job(
    name="distributed_training",
    command=["mpirun", "-np", "16", "python", "train.py"],
    resources=JobResource(
        nodes=4,
        ntasks_per_node=4,
        gpus_per_node=2,
        memory_per_node="128GB",
        time_limit="12:00:00"
    ),
    environment=JobEnvironment(
        conda="pytorch_distributed",
        env_vars={"CUDA_VISIBLE_DEVICES": "0,1"}
    )
)
```

### Customizable Job Templates

Organize your SLURM job submissions cleanly and flexibly with Jinja2 templates:

```bash
#!/bin/bash
#SBATCH --job-name={{ job_name }}
#SBATCH --nodes={{ nodes }}
{% if gpus_per_node > 0 -%}
#SBATCH --gpus-per-node={{ gpus_per_node }}
{% endif -%}
#SBATCH --time={{ time_limit }}

{{ environment_setup }}

srun {{ command }}
```

### Comprehensive Environment Support

Whether your workflow depends on Conda, virtual environments, or Sqsh, srunx supports them seamlessly:

```bash
# Conda environment example
conda create -n ml_env python=3.11 pytorch torchvision cudatoolkit=11.8 -c pytorch -c nvidia
```

## Beyond Basic Job Scheduling

srunx supports programmatic control for dynamic workflows, providing unprecedented flexibility:

```python
from srunx import WorkflowRunner, Slurm

# Load and run workflow
runner = WorkflowRunner.from_yaml("workflow.yaml")
results = runner.run()

# Monitor jobs programmatically
client = Slurm()
for job_name, job in results.items():
    status = client.retrieve(job.job_id)
    print(f"{job_name}: {status}")
```

## Real-World Performance Improvements

srunx doesn't just simplify workflows - it substantially enhances cluster efficiency and performance:

- **Reduced Wall-clock Time**: Parallelizing independent jobs reduces total execution time.
- **Optimized Resource Utilization**: Jobs run precisely when they're ready, minimizing wasted compute hours.
- **Accelerated Iteration**: Faster pipelines mean quicker development cycles and scientific discovery.
- **Enhanced Cluster Throughput**: Better-managed resources increase overall HPC facility productivity.

## Get Started with srunx Today

Installation is simple:

### Recommended (uv)

```bash
uv add srunx
```

### Using pip

```bash
pip install srunx
```

Try an example workflow in seconds:

```bash
cd examples
srunx flow run sample_workflow.yaml
```

## The Future of HPC Workflows Is Here

**srunx** represents a significant shift - from traditional, sequential workflows to intelligent, adaptive HPC orchestration. It combines cloud-native flexibility with supercomputing power, delivering an efficient, scalable, and intuitive experience.

No more waiting unnecessarily. It's time for your HPC workflows to evolve.

Ready to supercharge your productivity?

Explore srunx on GitHub and help shape the future of intelligent scientific computing.
