runq-sdk 0.5.0rc1

Lab GPU scheduler — in-task Python SDK

runq Python SDK

In-task SDK for the runq GPU scheduler.

Install

From the repo root:

pip install -e ./sdk/python

Optional extras:

pip install -e "./sdk/python[wandb]"   # auto wandb integration
pip install -e "./sdk/python[dev]"     # for pytest

Quick start

import runq

ctx = runq.context()
runq.log_metric("loss", 0.42, step=epoch)

See demo/l2c/stage2_sdk_design.md for the full API design.

Three modes

Detected automatically at runq.context():

• daemon — running under a runq daemon (RUNQ_TASK_ID + RUNQ_SOCKET_PATH set)
• no_daemon — running on HPC compute node (RUNQ_TASK_ID set, no socket / RUNQ_NO_DAEMON=1)
• manual — running outside runq (no RUNQ_TASK_ID); useful for local debugging

ctx.mode exposes which mode is active.

Stage 2 status

This is incremental — Tier A first, Tier B/C as we go.

• Step 1: package skeleton + context() + tri-mode + log_metric
• Step 2: transport.py (unix socket HTTP client)
• Step 3: safe_save core (path resolution + TOCTOU + freeze)
• Step 4: safe_save decorator + size walker
• Step 5: safe_save manifest + keep_last_n / keep_best
• Step 6: report + Decision + @early_stop
• Step 7: YAML early_stop policies
• Step 8: loop + @epoch + @log_group
• Step 9: wandb integration (L3)
• Step 10: daemon-side preflight (Go side, parallel track)