Drop-in Python acceleration

Make Python up to 193x faster.
Change nothing.

JIT compilation, GPU acceleration, and multicore parallelism — installed with pip, activated with one line. Epochly optimizes when safe, yields when it can't help.

Benchmarks |Safety Model

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Why Epochly

Speed, safety, and visibility — without changing a line of code.

Velocity

Up to 193x JIT, 70x GPU, 8x parallel

JIT compilation, GPU acceleration, and multicore parallelism — install with pip, run your scripts unchanged. Numerical loops, large array operations, and CPU-bound work run dramatically faster.

Safety

Progressive enhancement

Monitors first, optimizes only when verified safe, and falls back automatically if anything looks wrong. No data corruption, ever.

Insight

Built-in observability

See what's optimized, why, and how much time you're saving. Real-time telemetry across your infrastructure with configurable detail levels.

Adoption

Zero friction deployment

No rewrites. No new APIs. Disable with one env var. Uninstall leaves no trace. Works with your existing CI/CD.

Get started in 2 minutes

No decorators. No config files. No new API.

# Install
$ pip install epochly
# Run your existing code
$ python your_script.py
# Check what Epochly is doing
$ python -c "import epochly; epochly.stats()"
# Disable instantly
$ EPOCHLY_DISABLE=1 python your_script.py
# Uninstall cleanly
$ pip uninstall epochly

Full documentation

Where Epochly delivers the most value

Reclaim engineering time and reduce compute costs on CPU-bound Python workloads.

High-impact workloads

Numerical Python loops(58–193x (JIT))
Polynomial evaluations, iterative algorithms, and mathematical hot loops compiled to native code via JIT
Large array operations (10M+ elements)(36–70x (GPU))
Elementwise math, reductions, and transformations on GPU
Deep learning batch operations(7–19x (GPU))
Convolutions, matrix multiplies, and batched GPU workloads
Heavy CPU-bound parallel work(8–12x (parallel))
Monte Carlo simulations, prime sieves, embarrassingly parallel tasks
Large matrix operations (4K+)(7–10x (GPU))
Matrix multiplication and linear algebra on GPU

Already optimized

Network and disk I/O
Already limited by hardware, not CPU
Vectorized NumPy
Already using optimized C libraries
GPU-accelerated code
Already running on specialized hardware
Numba/Cython code
Already compiled to native code
Sub-millisecond workloads
Optimization overhead exceeds benefit

Benchmarks

Validated on real hardware, reproducible methodology

193x

Peak JIT compilation (Level 2)

70x

GPU acceleration (Level 4)

<5%

Overhead when not helping

GPU example: 100M-element array operation: 1,427ms → 21ms (68x)

Reproducible Results

These benchmarks use our open methodology. Run them yourself: pip install epochly && python -m epochly.benchmark

How we measured Reproduce the benchmark Known limitations

Correctness first. Always.

Performance is worthless if it changes your results. Epochly is built from the ground up to be safe.

Progressive Enhancement

Monitors first, optimizes only after stability is confirmed. Your code runs unchanged until Epochly is certain it's safe.

Automatic Fallback

Detects problems and reverts to standard Python automatically. No data corruption, no silent failures.

Instant Kill Switch

Set EPOCHLY_DISABLE=1 to turn everything off immediately. Uninstall leaves no trace.

Learn more about our safety model

What's next

Epochly Runtime ships today. Here's what we're building next.

In Development

Epochly Sandbox

Isolated Python execution environments with memory limits, CPU constraints, and network isolation. Run untrusted or experimental code safely.