Dev tools · September 16, 2025
Pyproc – Call Python from Go Without CGO or Microservices
Hi HN!I built *pyproc* to let Go services call Python like a local function — *no CGO and no separate microservice*. It runs a pool of Python worker processes and talks over *Unix Domain Sockets* on the same host/pod, so you get low overhead, process isolation, and parallelism beyond the GIL. *Why this exists* * Keep your Go service, reuse Python/NumPy/pandas/PyTorch/scikit-learn. * Avoid network hops, service discovery, and ops burden of a separate Python service. *Quick try (\~5 minutes)* Go (app): ``` go get github.com/YuminosukeSato/pyproc@latest ```…
In plain words
Pyproc enables Go applications to call Python functions directly without requiring CGO or separate microservices. It manages a pool of Python worker processes communicating over Unix Domain Sockets on the same host or pod, providing low overhead and process isolation. This allows developers to keep their Go services while reusing Python libraries like NumPy, pandas, PyTorch, and scikit-learn, avoiding the operational complexity of maintaining separate Python services and network communication.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN!I built *pyproc* to let Go services call Python like a local function — *no CGO and no separate microservice*. It runs a pool of Python worker processes and talks over *Unix Domain Sockets* on the same host/pod, so you get low overhead, process isolation, and parallelism beyond the GIL. *Why this exists* * Keep your Go service, reuse Python/NumPy/pandas/PyTorch/scikit-learn. * Avoid network hops, service discovery, and ops burden of a separate Python service. *Quick try (\~5 minutes)* Go (app): ``` go get github.com/YuminosukeSato/pyproc@latest ``` Python (worker): ``` pip install pyproc-worker ``` Minimal worker (Python): ``` from pyproc_worker import expose, run_worker @expose def predict(req): return {"result": req["value"] * 2} if __name__ == "__main__": run_worker() ``` Call from Go: ``` import ( "context" "fmt" "github.com/YuminosukeSato/pyproc/pkg/pyproc" ) func main() { pool, _ := pyproc.NewPool(pyproc.PoolOptions{ Config: pyproc.PoolConfig{Workers: 4, MaxInFlight: 10}, WorkerConfig: pyproc.WorkerConfig{SocketPath: "/tmp/pyproc.sock", PythonExec: "python3", WorkerScript: "worker.py"}, }, nil) _ = pool.Start(context.Background()) defer pool.Shutdown(context.Background()) var out map[string]any _ = pool.Call(context.Background(), "predict", map[string]any{"value": 42}, &out) fmt.Println(out["result"]) // 84 } ``` *Scope / limits* * Same-host/pod only (UDS). Linux/macOS supported; Windows named pipes not yet. * Best for request/response payloads ≲ \~100 KB JSON; GPU orchestration and cross-host serving are out of scope. *Benchmarks (indicative)* * Local M1, simple JSON: \~*45µs p50* and *\~200k req/s* with 8 workers. Your numbers will vary. *What’s included* * Pure Go client (no CGO), Python worker lib, pool, health checks, graceful restarts, and examples. *Docs & code* * README, design/ops/security docs, pkg.go.dev: [https://github.com/YuminosukeSato/pyproc](https://github.com/YuminosukeSato/pyproc) *License* * Apache-2.0. Current release: v0.2.x. *Feedback welcome* * API ergonomics, failure modes under load, and priorities for codecs/transports (e.g., Arrow IPC, gRPC-over-UDS). --- Source for details: project README and docs. ([github.com][1]) [1]: https://github.com/YuminosukeSato/pyproc "GitHub - YuminosukeSato/pyproc: Call Python from Go without CGO or microservices - Unix domain socket based IPC for ML inference and data processin"
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