gpupipe
Get the most out of your CPUs and GPUs without leaving Python: streaming pipelines of multi-worker stages — parallel downloads feeding GPU inference feeding DB writes — with backpressure, batching, and graceful shutdown handled for you.
Installation
# With uv
uv add gpupipe
# Or pip
pip install gpupipe
PyTorch is optional. CPU pipelines need only gpupipe and never import torch, in the
parent or the workers. GPU stages (gpus=, pergpu=, scavenge=) and pipe.web need it:
pip install 'gpupipe[torch]' # torch from PyPI (on Linux, the CUDA build)
# CPU-only torch, for stages that use tensors on a machine without a GPU
pip install gpupipe torch --index-url https://download.pytorch.org/whl/cpu
There is one torch extra rather than a CPU and a CUDA one: which torch build you get
depends on the index you install from, not on the package name, and any torch 2.x build
satisfies it.
Queues between stages run on a shared-memory ring written in Rust (rust/), and large
numpy arrays / CPU tensors are written to shared memory once and passed from stage to stage
by handle, never copied again. numpy arrays of 64 KiB+ therefore arrive read-only —
.copy() before writing in place (see "Payload store" in PIPE_REFERENCE.md). Wheels
include the Rust part; a source install needs a Rust toolchain (https://rustup.rs). If it
is missing, starting a pipeline raises with instructions.
The distribution is gpupipe; the module you import is pipe (sklearn-style):
from pipe import Pipe
Quick Start
from pipe import Pipe
class Generator:
def __call__(self):
# Generator pattern - yield items, exhaustion signals completion
for i in range(100):
yield {"id": i}
class Worker:
def load(self):
# Called after process spawn - initialize heavy resources here
pass
def __call__(self, item):
item["processed"] = True
return item
pipe = Pipe()
pipe.add(Generator(), outqn=20)
pipe.add(Worker(), workers=4, outqn=20)
for result in pipe:
print(result)
Autoscaling (queue-pressure-based worker scaling) is a planned feature — designed and implemented, but not currently wired into
Pipe. SeePLANNED.mdfor the design and re-integration steps.
Capability Tour
GPU pools
pipe.add(GPUWorker(), pergpu=True, outqn=None) # one worker per GPU
pipe.add(GPUWorker(), gpus=[5, 6], workers=2) # 2 workers on each of GPUs 5,6
pipe.add(GPUWorker(), gpu_id=0) # pin every worker to GPU 0
Each process worker is isolated to its GPU via CUDA_VISIBLE_DEVICES; requesting a
GPU that doesn't exist fails loudly instead of silently running on CPU.
CPU affinity
# 16 cores chunked across 4 workers (4 dedicated cores each), BLAS threads sized to match
pipe.add(MelSpectrogram(), workers=4, cpus=range(16))
pipe.add(HeavyCPU(), workers=2, cpu_threads=8) # just lift the 2-thread BLAS cap
Threaded workers (I/O-bound)
pipe.add(IOWorker(), workers=8, thread=True, outqn=20) # one process, 8 threads
Streaming async IO (downloads)
from pipe import AsyncPoolWorker
class Downloader(AsyncPoolWorker):
async def process(self, item): # N in flight continuously,
item["data"] = await fetch(item["url"]) # each result emitted the
return item # moment it lands - no batch barrier
pipe.add(Downloader(max_concurrent=256), workers=1, outqn=200)
Framework batching
class GPUInference:
def __call__(self, batch): # batch = list of up to 16 items,
return run_model(batch) # partial batches included
pipe.add(GPUInference(), pergpu=True, batch=16)
Send items back for retry: push()
class QualityGate:
def __call__(self, item):
if ok(item):
return item
if item.setdefault("tries", 0) < 2:
item["tries"] += 1
self.push("Renderer", item, timeout=5) # back to the Renderer stage
return None
return item # give up, pass through
One pipeline feeding N DDP ranks
pipe = Pipe(expected_consumers=world_size) # rank 0 runs the pipe
pipe.start()
# every rank:
for batch in PipeIterator(shared_queue): # items distributed across ranks,
train_step(batch) # each rank gets its own End
Live stats without log clobbering
pipe = Pipe(stats_interval=3) # pinned one-line display on a TTY
pipe.print("checkpoint saved") # prints ABOVE the stats line
Profiling
pipe = Pipe(profile=True) # per-worker cProfile + peak RSS, summary on stop
Testing
# Run all tests
pytest tests/
# Run a specific test
pytest tests/test_basic.py -v
Key Classes
Pipe- Main pipeline orchestratorEnd- Sentinel a root worker returns to signal completionAsyncPoolWorker- Streaming async IO stage: subclass, implementasync def process(item)Batcher- Batch items togetherBufferAndShuffle- Buffer and shuffle itemsPipeIterator- Read a pipe's output queue from another process (DDP shared mode)
Features
- Multi-worker stages connected by queues, with backpressure
- Threaded workers (
thread=True) for I/O-bound stages - Streaming async IO stages (
AsyncPoolWorker: N requests in flight, no batch barrier) - Framework batching (
batch=N: worker receives a list, partial batches included) - GPU pools:
pergpu=True,gpu_id=N, or explicitgpus=[...]with per-worker isolation - CPU affinity:
cpus=[...]chunked across workers,cpu_threads=for BLAS sizing - Chunked queue transport (
chunk=/chunk_ms=) for small-item edges worker.push(stage, item)- send an item back to an earlier stage (retry/quality gates)- DDP shared mode: one pipeline feeds N training ranks (
expected_consumers+PipeIterator) - Live stats: pinned one-line display on a TTY (
pipe.print()/print_above()write above it), rich mode, or pollget_stats()externally - Health monitoring with worker restart; graceful shutdown with flush semantics
- Per-worker profiling (
profile=True: cProfile + peak RSS summary) - Sequential mode (
sequential=True) for single-process debugging - HTTP serving (
pipe.web) for cross-machine pipelines
Documentation
PIPE_REFERENCE.md- the full reference: worker types, return-value semantics, completion signaling, tensor handling, DDP, env vars, pitfallsexamples/- runnable examples and job templatesPLANNED.md- designed-but-unwired features (autoscaling)
1 maintainer
Metadata
1 maintainer
Release files for gpupipe 0.2.0
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| File | Size | Uploaded | Provenance | Actions |
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Built distributions (wheels)
Total release size: 7.8 MB
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