Nested loop fails in version 0.59

Not sure if this is a bug or if I’m doing something wrong (I’m inclined to assume the latter), but the code bellow (Iterative fitting of OLS model with stability check) works as expected on numba==0.58.1 and fails on numba==0.59.0. Any idea what might be happening?


import os
os.environ['OPENBLAS_NUM_THREADS'] = '1'

import numba
import numpy as np
from sklearn.datasets import make_regression

@numba.jit(nopython=True, cache=True, parallel=True)
def stable_fit(X, y, threshold=3):
    """Fitting stable regressions using an adapted CCDC method

    Models are first fit using OLS regression. Those models are then checked for
    stability. If a model is not stable, the two oldest
    acquisitions are removed, a model is fit using this shorter
    time-series and again checked for stability. This process continues as long
    as all of the following 3 conditions are met:

    1. The timeseries is still unstable
    2. There are enough cloud-free acquisitions left (threshold is 1.5x the
        number of parameters in the design matrix)
    3. The time series includes data of more than half a year

    Stability depends on all these three conditions being true:
    1.             slope / RMSE < threshold
    2. first observation / RMSE < threshold
    3.  last observation / RMSE < threshold

        X ((M, N) np.ndarray): Matrix of independant variables
        y ((M, K) np.ndarray): Matrix of dependant variables
        threshold (float): Sensitivity of stability checking.

        beta (numpy.ndarray): The array of regression estimators
        residuals (numpy.ndarray): The array of residuals
        is_stable (numpy.ndarray): 1D Boolean array indicating stability
    min_obs = int(X.shape[1] * 1.5)
    beta = np.zeros((X.shape[1], y.shape[1]), dtype=np.float64)
    residuals = np.full_like(y, np.nan)
    stable = np.empty((y.shape[1]))
    for idx in range(y.shape[1]):
        y_sub = y[:, idx]
        isna = np.isnan(y_sub)
        X_sub = X[~isna]
        y_sub = y_sub[~isna]
        is_stable = False

        # Run until minimum observations
        # or until stability is reached
        for jdx in numba.prange(len(y_sub), min_obs-1, -2):
            # Timeseries gets reduced by two elements
            # each iteration
            y_ = y_sub[-jdx:]
            X_ = X_sub[-jdx:]
            beta_sub = np.linalg.solve(, X_),, y_))
            resid_sub =, beta_sub) - y_
            # Check for stability
            rmse = np.sqrt(np.mean(resid_sub ** 2))
            slope = np.fabs(beta_sub[1]) / rmse < threshold
            first = np.fabs(resid_sub[0]) / rmse < threshold
            last = np.fabs(resid_sub[-1]) / rmse < threshold
            # Break if stability is reached
            is_stable = slope & first & last
            if is_stable:

        beta[:,idx] = beta_sub
        residuals[-jdx:,idx] = resid_sub
        stable[idx] = is_stable
    return beta, residuals, stable.astype(np.bool_)

# Run on test data
X, y = make_regression(n_samples=200, n_features=4, n_targets=50)

Hey @loicdtx ,

your code works in my environment.
Python: 3.12.0
Numpy: 1.26.4
Numba: 0.59.0

Are you sure this works in parallel code?

            if is_stable:

What is your error message?

Thanks for the quick response @Oyibo

About the break in the parallel loop, that’s a mistake; I tampered with the original code while building the reprex and the prange ended up on the wrong loop.
I setup a test on different python/numba versions combinations using github actions and I seem to be getting different error depending on the versions.

  • 3.12 - 0.58.1 fails on install as expected
  • 3.12 - 0.59 fails (SystemError: CPUDispatcher(<function stable_fit at 0x7f4b3e920e00>) returned a result with an exception set)
  • 3.11 - 0.58.1 passes
  • 3.11 - 0.59 passes
  • 3.10 - 0.58.1 passes
  • 3.10 - 0.59 Fails (TypeError: Failed in nopython mode pipeline (step: convert to parfors))

Here’s the full results

I can replicate this rather consistently now on python 3.9. Locally git bisect is pointing to the merge of Fix SSA to consider variables whose use is not dominated by the definition by sklam · Pull Request #9255 · numba/numba · GitHub as the first bad commit. I’m debugging this.

Tracking this at Non-det bug with prange code · Issue #9490 · numba/numba · GitHub