Accelerate your scikit-learn workloads with AOCL Data Analytics

Oct 09, 2026

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If you use scikit‑learn for classical machine learning, performance can become a bottleneck long before model complexity does. Hyperparameter sweeps, cross-validation, and repeated retraining can turn otherwise straightforward workflows into time-consuming CPU jobs.

Optimizing the algorithm itself can help, but it can also mean rewriting code, adding dependencies, or disrupting a workflow that already works. What if you could accelerate your scikit-learn workloads without changing your Python code?

AOCL‑Data Analytics provides optimized implementations for a broad set of scikit-learn algorithms, allowing developers to accelerate supported workloads while continuing to use familiar scikit-learn APIs.

Drop‑in acceleration: no code changes

Consider a standard scikit‑learn support vector classifier, using the stock API:

		from sklearn import svm
from sklearn.datasets import make_blobs
import numpy as np

# Generate some synthetic data
X, y = make_blobs(n_samples=10000, centers=2, cluster_std=2)

# Compute SVC
clf = svm.SVC(kernel="linear", C=1000)
clf.fit(X, y)

On an AMD EPYC™ 9755 processor (Turin), fitting this model takes approximately 13.8 seconds.

Now let’s compare two ways of running the same script:

		python svm.py

versus

		python -m aoclda.sklearn svm.py

In the second call, the AOCL‑Data Analytics scikit‑learn patch has been enabled, and the same computation takes 1.8 seconds. This is a 7.5x1 speedup with no changes to the Python code, model configuration, or data preprocessing.

Where the performance comes from

Under the hood, the AOCL-Data Analytics scikit-learn patch redirects supported scikit-learn operations to optimized native implementations designed for AMD EPYC Server CPU architectures. You continue to use the familiar scikit-learn APIs while AOCL-Data Analytics handles the underlying computation.

AOCL‑Data Analytics provides optimized implementations for many scikit‑learn algorithms, including clustering (k‑means, DBSCAN), decision forests, linear models, support vector machines, PCA and nearest‑neighbors methods.

The benchmark figures below summarize the achievable speedups across various workloads.

DA- Training
DA - Inference
Performance results based on testing by AMD as of July 10, 2026. Configuration details: AMD EPYC 9755 128-core Turin processor, with benchmarks run in double and single precision, training and inference, using open-source datasets. Tests were run on 1, 8, 16 and 32 threads. Geometric mean of speedups vs scikit-learn reported.

Averaged over all our benchmarks, AOCL-Data Analytics is 2.5x faster than scikit-learn.

Why this matters for real workflows

If you are already comfortable with scikit‑learn, AOCL‑Data Analytics has a lot to offer.

  • Faster hyperparameter sweeps: grid searches that took hours may only take minutes now.
  • Cross‑validation becomes cheap enough to run aggressively
  • You no longer need to train model quality for turnaround time
  • Experimentation cycles are quicker
  • CPU utilization is better, not just on AMD EPYC systems, but on x86 CPUs more widely.

Beyond scikit‑learn

Although the scikit‑learn layer is the easiest entry point, it is not the only way to access AOCL‑Data Analytics.

For workflows that mix Python prototyping with C++ deployment—or that need functionality not available in scikit‑learn—the library also provides:

  • Native Python APIs,
  • C and C++ APIs,
  • R wrappers.

This gives you access to algorithms that are currently outside of scikit‑learn, such as approximate nearest neighbors, interpolation, and nonlinear least‑squares optimization.

Installation and use

The easiest way to obtain AOCL-Data Analytics is from the Python Package Index:

		pip install aoclda

To use the patch, you can simply add -m aoclda.sklearn to your Python call, or add the following lines to the top of your Python script, before your scikit-learn imports:

		from aoclda.sklearn import skpatch
skpatch()

So why not enable the patch for one of your slowest pipelines and measure the difference?

Useful resources

The following links will point you towards our prebuilt binaries (for C/C++ users), our source code and our API documentation.

Note that AOCL-Data Analytics is part of a wider suite of AOCL libraries providing optimized numerical libraries for EPYC CPUs. Python users may also be interested in using NumPy and SciPy Python libraries with AOCL.

Footnotes

Footnote 

Data measured on July 10 2026.

System Configuration

  • AMD EPYC Turin System Model: Supermicro AS-2126HS-TN
  • CPU: 2x AMD EPYC 9755 128-Core Processor (256 cores, 512 threads; SMT enabled)
  • NUMA: 2 NUMA nodes, one per socket. NUMA auto-balancing enabled
  • Memory: 1536 GiB (24x 64 GiB dual-rank x4 DDR5 RDIMMs; 1510.76 GiB OS-visible)
  • Disk: 2x 1.92 TB Samsung NVMe SSDs (3.84 TB / 3.49 TiB raw)
  • Host OS: Red Hat Enterprise Linux 8.10 (Ootpa)
  • Kernel: 4.18.0-553.125.1.el8_10.x86_64
  • System BIOS: 1.5, dated 2025-05-12
  • System BIOS Vendor: American Megatrends International, LLC.
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