# `Snakepit.WorkerProfile.Thread`
[🔗](https://github.com/nshkrdotcom/snakepit/blob/v0.13.0/lib/snakepit/worker_profile/thread.ex#L1)

Multi-threaded worker profile (Python 3.13+ optimized).

Each worker is a Python process with a thread pool, providing:
- **Shared memory**: Zero-copy data sharing within worker
- **CPU parallelism**: True multi-threading without GIL (Python 3.13+)
- **Lower memory**: One interpreter vs many
- **High throughput**: Optimal for CPU-bound tasks

## Configuration

    config :snakepit,
      pools: [
        %{
          name: :hpc_pool,
          worker_profile: :thread,
          pool_size: 4,                    # 4 processes
          threads_per_worker: 16,          # 16 threads each = 64 total capacity
          adapter_module: Snakepit.Adapters.GRPCPython,
          adapter_args: ["--mode", "threaded", "--max-workers", "16"],
          adapter_env: [
            # Allow multi-threading in libraries
            {"OPENBLAS_NUM_THREADS", "16"},
            {"OMP_NUM_THREADS", "16"}
          ],
          worker_ttl: {3600, :seconds},    # Recycle hourly
          worker_max_requests: 1000,       # Or after 1000 requests
          thread_safety_checks: true       # Enable runtime validation
        }
      ]

## Requirements

- Python 3.13+ for optimal performance (free-threading)
- Thread-safe Python adapters
- Thread-safe ML libraries (NumPy, PyTorch, etc.)

## Status

Thread profile is fully supported when paired with Python 3.13+ and thread-safe adapters.

## Implementation Notes

The thread profile:
1. Starts fewer Python processes (4-16 instead of 100+)
2. Runs a ThreadPoolExecutor per worker process
3. Tracks per-worker capacity via `threads_per_worker` for pool scheduling
4. Supports optional CapacityStore telemetry with `capacity_strategy: :hybrid`
5. Allows concurrent requests to the same worker via HTTP/2 multiplexing

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*Consult [api-reference.md](api-reference.md) for complete listing*