Data science acceleration

Data science in Python means spending time waiting. A DataFrame groupby on 10 million rows. A NumPy reduction across 100 million elements. A scikit-learn cross-validation grid search that runs overnight.

Epochly accelerates these workloads without changing your code.

Where Data Science Time Goes

A typical data science workflow breaks down into:

The bottleneck shifts depending on your stage. Data loading is I/O-bound (Epochly won't help). But feature engineering, custom transformations, and numerical computations are CPU-bound Python -- exactly what Epochly optimizes.

Accelerating NumPy Workflows

Large array operations benefit from GPU acceleration (Level 4).

import epochly
import numpy as np
@epochly.optimize
def compute_features(data):
    """Feature engineering on large arrays."""
    normalized = (data - np.mean(data, axis=0)) / np.std(data, axis=0)
    correlations = np.corrcoef(normalized.T)
    eigenvalues = np.linalg.eigvalsh(correlations)
    return eigenvalues

Expected speedups by data size

The threshold is 10M+ elements for elementwise operations. Below 1M elements, GPU kernel launch overhead exceeds the benefit.

Accelerating Pandas Workflows

Pandas operations that call back into Python (apply with custom functions, iterrows, itertuples) are prime targets for JIT compilation (Level 2).

import epochly
import pandas as pd
@epochly.optimize
def process_transactions(df):
    """Custom transformation on DataFrame."""
    results = []
    for _, row in df.iterrows():
        score = row['amount'] * 0.05 + row['frequency'] ** 1.5
        if score > row['threshold']:
            results.append(score)
        else:
            results.append(0.0)
    return results

The iterrows loop above runs as interpreted Python. Epochly's Level 2 JIT compiles the numerical inner loop to native code: 58-193x speedup (113x average on numerical loops).

When Pandas won't speed up

• df.groupby().sum() -- already uses C internally (~1.0x)
• df.merge() -- already optimized in Pandas C engine (~1.0x)
• df.read_csv() -- I/O-bound, no CPU optimization helps

Profile first. If the bottleneck is a Python-level loop or custom apply function, Epochly helps. If it's a built-in Pandas operation, it's already fast.

Accelerating scikit-learn

scikit-learn training involves numerical computation that can benefit from multi-core parallelism (Level 3).

import epochly
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
@epochly.optimize
def evaluate_model(X, y):
    """Cross-validation with parallel execution."""
    clf = RandomForestClassifier(n_estimators=100)
    scores = cross_val_score(clf, X, y, cv=5, scoring='accuracy')
    return scores.mean(), scores.std()

Level 3 parallel execution distributes work across CPU cores: 8-12x on 16 cores for CPU-bound workloads (ProcessPool, 50-60% parallel efficiency).

Note: scikit-learn already supports n_jobs=-1 for built-in parallelism. Epochly helps most when you have custom preprocessing pipelines or evaluation loops that aren't covered by scikit-learn's own parallelism.

What Epochly Does NOT Help in Data Science

Transparency means acknowledging limitations:

• Pandas built-in operations: groupby, merge, pivot_table use C engines internally. ~1.0x improvement.
• Data loading: I/O-bound by disk/network speed. ~1.0x.
• Small datasets: Arrays under 1M elements or DataFrames under 10K rows. Overhead exceeds benefit.
• Already vectorized NumPy: Operations like np.matmul already use optimized BLAS libraries internally. ~1.0x.

Getting Started

# Install Epochly
pip install epochly
# Add to your data science workflow
import epochly
@epochly.optimize
def your_feature_engineering(data):
    # Your existing code -- no changes needed
    pass

Start with Level 0 monitoring to understand where your time goes. Then let Epochly graduate to the appropriate optimization level.

Benchmark conditions: Python 3.12.3, Linux WSL2, 16 cores, NVIDIA Quadro M6000 24GB (CUDA 12.1). January 29, 2026 comprehensive benchmark report.