Heuristic:Gretelai Gretel synthetics Parallel Generation CUDA Disable
| Knowledge Sources | |
|---|---|
| Domains | Optimization, NLP, Debugging |
| Last Updated | 2026-02-14 19:00 GMT |
Overview
Parallel text generation workers force `CUDA_VISIBLE_DEVICES="-1"` to run on CPU only, avoiding GPU memory contention and TensorFlow multi-process CUDA issues.
Description
The parallel generation system uses `loky.ProcessPoolExecutor` to spawn multiple worker processes for text generation. Each worker initializes by setting `os.environ["CUDA_VISIBLE_DEVICES"] = "-1"`, which hides all GPUs from TensorFlow. This forces each worker to run inference on CPU. Additionally, TensorFlow logging is suppressed to WARNING level in workers, and stdout/stderr is redirected to /dev/null (with special handling for Windows/Jupyter environments that may throw `OSError [WinError 1]` during stream redirection).
Usage
This heuristic is applied automatically when using `generate_text()` with `parallelism > 0`. The pending task count is set to `10 * num_workers` as a balance between memory usage and worker idle time. No user configuration is needed.
The Insight (Rule of Thumb)
- Action: Worker processes for parallel generation are forced to CPU via `CUDA_VISIBLE_DEVICES="-1"`.
- Value: `max_pending_tasks = 10 * num_workers` balances memory vs. worker utilization.
- Trade-off: Generation runs on CPU in workers (slower per-worker), but parallelism across multiple workers compensates. Avoids GPU memory fragmentation and CUDA context issues across forked processes.
Reasoning
TensorFlow and CUDA contexts do not fork cleanly across processes. If each worker tried to use the GPU, they would either contend for GPU memory (causing OOM) or encounter CUDA initialization errors in forked processes. Running workers on CPU with parallelism is a more reliable strategy. The `max_pending_tasks` factor of 10 ensures workers rarely sit idle waiting for the main process to dispatch new tasks, while preventing unbounded memory growth from pending results.
Code Evidence
CUDA disable in worker init from `generate_parallel.py:185`:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
TensorFlow logging suppression from `generate_parallel.py:193`:
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
Pending tasks tuning from `generate_parallel.py:96-99`:
# How many tasks can be pending at once. While a lower factor saves memory, it increases the
# risk that workers sit idle because the main process is blocked on processing data and
# therefore cannot hand out new tasks.
max_pending_tasks = 10 * num_workers
Windows/Jupyter stdout handling from `generate_parallel.py:226-231`:
# On Windows in a Jupyter notebook, we have observed an
# 'OSError [WinError 1]: Incorrect function' when trying to sys.stdout/sys.stderr
# after the dup2 invocation. Hence, set the references explicitly to make prevent this
# (the native code writing to stdout/stderr directly seems to be unaffected).
sys.stdout = devnull
sys.stderr = devnull