sklbench dashboard

Hardware

CPU

Intel(R) Xeon(R) 6787P

X86_64, 172 physical cores, 344 logical CPUs

499 GB RAM

GPU(s)

No GPU detected.

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (pypi)
numpy 2.4.6 (conda-forge)
scipy 1.17.1 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

sklearn-torch-cpu

Python 3.12.13

Array API active

Packages

scikit-learn 1.9.0 (pypi)
torch 2.10.0+cpu (pypi)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

sklearnex-cpu

Python 3.12.13

Packages

scikit-learn 1.8.0 (conda-forge)
scikit_learn_intelex 2026.1.0 (pypi)
daal 2026.1.0 (pypi)

Threadpools

libopenblas 0.3.33 (128 threads)
libgomp (344 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

linear / fit (61 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - LogisticRegression (11)

◇ Metrics differ from the baseline (2 points)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (10 points)

linear / predict (61 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - LogisticRegression (11)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (10 points)

Detailed results

tree-based / fit (80 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🧺 Scikit-learn intelex uses binning & histogram-based splits while scikit-learn doesn't - RandomForestClassifier (10), ExtraTreesRegressor (9), ExtraTreesClassifier (8), RandomForestRegressor (4)

◇ Metrics differ from the baseline (41 points)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (3 points)

✕ Benchmark run failed, shown at the bottom of its column (2 points)

tree-based / predict (80 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🧺 Scikit-learn intelex uses binning & histogram-based splits while scikit-learn doesn't - RandomForestClassifier (10), ExtraTreesRegressor (9), ExtraTreesClassifier (8), RandomForestRegressor (4)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (3 points)

✕ Benchmark run failed, shown at the bottom of its column (2 points)

Detailed results

clustering / fit (9 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - KMeans (2)

clustering / predict (9 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - KMeans (2)

Detailed results

Hardware

CPU

Intel(R) Core(TM) Ultra X7 358H

X86_64, 16 physical cores, 16 logical CPUs

31 GB RAM

GPU(s)

level_zero:gpu:0: Intel(R) Arc(TM) B390 GPU (29 GB)

sklearn

Python 3.12.13

Packages

scikit-learn 1.9.0 (pypi)
numpy 2.4.6 (conda-forge)
scipy 1.17.1 (conda-forge)
pandas 2.3.3 (conda-forge)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

sklearn-dpnp-gpu

Python 3.12.13

Array API active

Packages

scikit-learn 1.9.0 (pypi)
dpnp 0.20.0 (pypi)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

sklearn-torch-cpu

Python 3.12.13

Array API active

Packages

scikit-learn 1.9.0 (pypi)
torch 2.10.0+cpu (pypi)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

sklearn-torch-xpu

Python 3.12.13

Array API active

Packages

scikit-learn 1.9.0 (pypi)
torch 2.13.0+xpu (pypi)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 300000

Full environment

view pixi env JSON

sklearnex-cpu

Python 3.12.13

Packages

scikit-learn 1.8.0 (conda-forge)
scikit_learn_intelex 2026.1.0 (pypi)
daal 2026.1.0 (pypi)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 1

Full environment

view pixi env JSON

sklearnex-gpu

Python 3.12.13

Packages

scikit-learn 1.8.0 (conda-forge)
scikit_learn_intelex 2026.1.0 (pypi)
scikit_learn_intelex_gpu 2026.1.0 (pypi)
dpnp 0.20.0 (pypi)
daal 2026.1.0 (pypi)

Threadpools

libopenblas 0.3.33 (16 threads)
libgomp (16 threads)

OpenMP

GNU libgomp (OpenMP 4.5)
GOMP_SPINCOUNT: 1

Full environment

view pixi env JSON

linear / fit (123 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - LogisticRegression (14)
↩ Some operations fell back to CPU according to benchmark logs - Ridge (10)

◇ Metrics differ from the baseline (3 points)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (13 points)

✕ Benchmark run failed, shown at the bottom of its column (6 points)

linear / predict (123 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - LogisticRegression (14)
↩ Some operations fell back to CPU according to benchmark logs - Ridge (10)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (13 points)

✕ Benchmark run failed, shown at the bottom of its column (6 points)

Detailed results

tree-based / fit (80 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🧺 Scikit-learn intelex uses binning & histogram-based splits while scikit-learn doesn't - RandomForestClassifier (10), ExtraTreesRegressor (9), ExtraTreesClassifier (8), RandomForestRegressor (4)

◇ Metrics differ from the baseline (39 points)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (3 points)

✕ Benchmark run failed, shown at the bottom of its column (2 points)

tree-based / predict (80 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🧺 Scikit-learn intelex uses binning & histogram-based splits while scikit-learn doesn't - RandomForestClassifier (10), ExtraTreesRegressor (9), ExtraTreesClassifier (8), RandomForestRegressor (4)

● Ran via sklearnex but fell back to stock scikit-learn, no oneDAL acceleration (3 points)

✕ Benchmark run failed, shown at the bottom of its column (2 points)

Detailed results

clustering / fit (9 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - KMeans (4)

clustering / predict (9 points)

● Metrics and benchmark setup match the baseline

■ Metrics match the baseline, but some comparison details are worth reporting:

🔁 Number of iteration differs: this might mean algorithms differ - KMeans (4)

Detailed results