autozyme
Drop-in accelerators for scientific Python packages. Each accelerator is a validated, monkey-patched fast replacement for a hot function in an upstream library (scanpy, scvelo, etc.). Install once; existing scripts run faster automatically. It bundles no datasets; it accelerates your own data in place.
Install
pip install autozyme
autozyme itself has no hard dependencies on upstream packages. A patch activates only when its upstream is importable in your environment.
Use
import autozyme
# autozyme 0.3.0: 16 patches available, 4 subsets — autozyme.activate(<name>) to enable
autozyme.activate("scvelo")
import scvelo as scv
scv.tl.recover_dynamics(adata) # uses fast version transparently
Supported parameter scope
Each accelerator is validated for a specific parameter envelope. Supported
parameters (n_comps, resolution, the data itself, …) can be set freely; a
handful of parameters per patch are not on the fast path and fall back to the
upstream implementation automatically (correct result, just not accelerated).
A few documented approximations are validated only at their stated
configuration. Each patch's supported scope and out-of-scope behavior are
documented in a SCOPE.md next to its source in the repository, at
autozyme_py/src/autozyme/<patch>/SCOPE.md
(github.com/ElliotXie/autozyme).
A few patches accelerate version-fragile upstreams that bind one release's
internal API (e.g. cell2location, pinned to 0.1.5). Those carry a tested
install recipe under "Tested environment" in that SCOPE.md and an opt-in
extra, e.g. pip install "autozyme[cell2location]".
Opt-out levels
# per-call (only namespace-fn patches; class-method patches don't expose this)
fn(..., zyme=False)
# per-block (works for ALL patches incl. class-method — hard kill switch)
with autozyme.disabled():
fn(...) # uses captured original
other_fn(...) # also original
# per-package
autozyme.restore("scvelo")
autozyme.activate("scvelo")
# session-wide
autozyme.restore_all()
# never activate (set before import; `activate()` returns False without binding)
import os; os.environ["AUTOZYME_DISABLED"] = "1"
# suppress per-activation stderr marker (keeps logs clean; banner unaffected)
import os; os.environ["AUTOZYME_QUIET"] = "1"
Multiprocessing workers
activate() binds patches into the upstream namespace of the current process. Fork-mode workers (default on Linux for multiprocessing) inherit the bindings via copy-on-write memory. Spawn-mode workers (default on macOS / Windows, and what loky, joblib, concurrent.futures.ProcessPoolExecutor(mp_context="spawn") use) start fresh — they import autozyme but no patch is active.
For spawn-mode pools, activate inside each worker via the pool's initializer:
from multiprocessing import Pool
def _init():
import autozyme
autozyme.activate("cell2location")
with Pool(4, initializer=_init) as pool:
pool.map(work_fn, items)
joblib.Parallel(backend="loky") uses the same pattern via the initializer= arg on its underlying executor.
Threading
autozyme.set_threads(8)
Status and introspection
python -m autozyme # one-screen dashboard of patches + upstream availability
autozyme.list_patches() # ['cell2location', 'obspy', 'prody', ...]
autozyme.list_patches(installed=True) # only patches whose upstream is importable
autozyme.status() # {'cell2location': 'inactive', 'prody': 'active', ...}
autozyme.inspect("prody") # detailed binding info for one patch
autozyme.env_snapshot() # structured dict for provenance / Methods capture
Citation
autozyme releases on GitHub get a Zenodo DOI. Cite the version + the patch name(s) you activated. The activation marker logged to stderr is the canonical provenance record, e.g.
[autozyme] activated cell2location -> 2 target(s) in cell2location 0.1.5
In Methods: "We used autozyme v0.3.0 with the cell2location patch (tested against cell2location 0.1.5)."
License
MIT
1 maintainer
Release files for autozyme 0.3.1
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| autozyme-0.3.1.tar.gz | 262.0 kB | Details |
Built distributions (wheels)
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