Install & Compatibility
Where this runs
tested against v0.4.0 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
build_error
glibcpy 3.10–3.95 runs
installs and imports cleanly · install 16.9s · import 0.000s · 494MB
505MB installed
● package 505MB
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
LogisticRegression
✓ from dask_glm import LogisticRegression
✗ from dask_glm.logistic import LogisticRegression
Quick example: fit a logistic regression model on a Dask array.
import dask.array as da
from dask_glm.logistic import LogisticRegression
# Create synthetic data
np.random.seed(0)
X = da.from_array(np.random.randn(1000, 10), chunks=100)
y = da.from_array((np.random.rand(1000) > 0.5).astype(int), chunks=100)
# Fit logistic regression
model = LogisticRegression()
coef = model.fit(X, y).coef_
print(coef.compute())
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Version history
0.4.0latest on PyPI · released Jan 23, 2026
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Dependencies
daskrequiredCore dependency for distributed computing and array/DataFrame abstractions
scipyrequiredUsed for optimization routines and linear algebra
numpyrequiredRequired for array operations
cupyoptionalOptional: GPU acceleration for GLM fitting