# Introduction
**Torc** is a distributed workflow orchestration system for managing computational pipelines ranging
from simple workflows needing to parallelize independent jobs to complex workflows with job
dependencies, mixed resource requirements, and multiple stages.
```mermaid
flowchart LR
subgraph input [" "]
A[Prepare Data]
end
subgraph parallel [" "]
direction TB
B1[Process 1]
B2[Process 2]
B3[Process 3]
B4[Process 4]
end
subgraph output [" "]
C[Aggregate Results]
end
A --> B1 & B2 & B3 & B4
B1 & B2 & B3 & B4 --> C
style A fill:#4a9eff,stroke:#2171c7,color:#fff
style B1 fill:#28a745,stroke:#1e7e34,color:#fff
style B2 fill:#28a745,stroke:#1e7e34,color:#fff
style B3 fill:#28a745,stroke:#1e7e34,color:#fff
style B4 fill:#28a745,stroke:#1e7e34,color:#fff
style C fill:#6f42c1,stroke:#5a32a3,color:#fff
style input fill:none,stroke:none
style parallel fill:none,stroke:none
style output fill:none,stroke:none
```
## Key Features
- **Declarative Workflow Definitions** — Define workflows in YAML, JSON, JSON5, or KDL
- **Job Parameterization** — Create parameter sweeps with simple syntax like `index: "1:100"` to
expand one job definition into hundreds ([tutorial](./core/tutorials/simple-params.md))
- **Automatic Dependency Resolution** — Dependencies inferred from file and data relationships. No
explicit `depends_on` declarations needed—Torc builds the dependency graph from your data flow
([tutorial](./core/tutorials/diamond.md))
- **Distributed Execution** — Run jobs across local machines, HPC clusters, and networked compute
nodes
- **Resource Management** — Track CPU and memory usage across all jobs
- **Automatic Failure Recovery** — Detect OOM/timeout failures and retry with adjusted resources
- **Fault Tolerance** — Resume workflows after failures without losing progress
- **AI-assisted configuration and management** — Use AI tools like Claude Code and Copilot to
configure workflows and diagnose problems
## Who Should Use Torc?
Torc is designed for:
- **HPC Users** who need to parallelize jobs across cluster resources
- **Computational Scientists** running parameter sweeps and simulations
- **Data Engineers** building complex data processing pipelines
- **ML/AI Researchers** managing training workflows and hyperparameter searches
- **Anyone** who needs reliable, resumable workflow orchestration
## Documentation Structure
This documentation is organized to help you find what you need quickly:
**Core Documentation** (for all users):
- **Getting Started** — Installation and local quick start
- **Core Concepts** — Architecture, job states, and dependencies
- **Working with Workflows** — Creating and managing workflows
- **Tutorials** — Step-by-step workflow patterns
- **Monitoring & Debugging** — TUI, reports, and troubleshooting
- **Reference** — CLI commands, formats, and configuration
**Specialized Topics** (below the separator in the sidebar):
- **HPC & Slurm** — Running on compute clusters
- **Fault Tolerance & Recovery** — Failure handlers, checkpointing, automatic recovery
- **Administration & Security** — Server deployment, authentication, access control
- **Design & Architecture** — Internal design for contributors
## Next Steps
- **New to Torc?** Start with [Quick Start (Local)](./getting-started/quick-start-local.md)
- **Want to understand how it works?** Read the
[Architecture Overview](./core/concepts/architecture.md)
- **Deploying on HPC or remote systems?** See
[Network Connectivity](./core/concepts/network-connectivity.md) for server configuration
- **Ready to create workflows?** Jump to
[Creating Workflows](./core/workflows/creating-workflows.md)
- **On an HPC cluster?** See [Quick Start (HPC)](./getting-started/quick-start-hpc.md)
- **Need a quick reference?** The [CLI Cheat Sheet](./core/reference/cli-cheatsheet.md) has all
common commands at a glance