Documentation
Guides, API references, and troubleshooting for the Pyvorin platform.
AOT vs JIT for Python: An Honest Comparison
Warm-up, peak performance, compatibility and running cost: the AOT vs JIT trade-offs for Python, the cases where each wins, and Pyvorin's position.
8 min read
Artifact Caching, Explained
Where pyvorin-native 1.0.9 stores compiled code, how the cache key is built, what eviction costs, and the measured gap between a cold and a warm compile.
Benchmarking Correctly
Cold compiles, micro-workloads and single runs: the measurement mistakes that make benchmark numbers meaningless, and the verified commands that avoid them.
Benchmarking with the Pyvorin Edge SDK
Benchmark Pyvorin Edge pipelines honestly: cost-model assumptions, warm-up discipline, a CI regression example, and micro-benchmark limits.
13 min read
Benchmarks
Every measured CPython vs Pyvorin result across 71 workloads — the wins, the losses, and how to reproduce every number yourself.
3 min read
Compiling Python to Native Code: A Measured Walkthrough
One real Python function compiled to native code end to end — verified commands, captured output, a measured before-and-after, and the honest boundary cases.
10 min read
Custom Benchmark Suites
Turn pyvorin bench --json into a repeatable suite: capture raw_times_ms, p95 and stddev per function, aggregate across a directory, and gate regressions in CI.
Example Workload: ETL Pipeline
An illustrative ETL pipeline with acceleratable stages (transforms, windowed aggregates) and non-acceleratable ones (CSV parsing, joins) separated by evidence.
Example Workload: Financial Risk Calculations
An illustrative financial-risk batch dissected stage by stage: Monte Carlo, correlations and moving averages — what a compiler accelerates, what it cannot.
Example Workload: Log Analytics
An illustrative log-analytics pipeline dissected stage by stage: where native compilation wins, where parsing-bound stages do not, and the measured anchors.
Example Workload: ML Feature Engineering
An illustrative ML feature pipeline split into what a compiler accelerates — aggregations, transforms — and what it cannot: histograms, string features, I/O.
High-Frequency Data Feeds
On high-frequency feeds, Pyvorin accelerates ingestion-side batch transforms, not the wire path. Measured anchors and where to draw the boundary.
How to Benchmark a Function
From a Python file to a defensible speedup number in minutes: the verified commands, the fields that matter, and the checks before you trust it.
How to Run a Speed Proof
The measurement a sceptical CFO could audit: captured baseline, warmed runs, median-of-N, correctness check, compile-time amortisation, and an honest write-up.
How While Loops Compile
While loops compile natively in Pyvorin 1.0.9, but their trip count is data — this page shows the verified lowering and which optimisations that fact excludes.
Implementing Pyvorin in Microservices
Compile CPU-bound services at build time, ship the artefacts in the image, and skip the compiler sidecar. Which service shapes benefit, which do not, and why.
Making Python ETL Pipelines Faster, Measured
Measured per-transform ETL results: windowed aggregates to 134.62x, merge joins to 0.64x — which transforms accelerate, which do not, and where the seam sits.
8 min read
Migrating from Numba to Pyvorin
What @njit gave you, what Pyvorin compilation gives you instead, which kernels should stay on Numba, and how to verify each migrated function.
Profiling Edge Pipelines and System Metrics
Profile edge pipelines in place: cProfile on hot rules, memory measurement, system metrics, and how to read results without fooling yourself.
12 min read
Python Log Processing Performance
Parsing log lines is already fast under CPython — measured 0.86x compiled. The wins live in the aggregation layer behind the parser; here is the measured map.
8 min read
Pyvorin for Algorithmic Execution
Slicing schedules, market-impact research and pre-trade analytics are legitimate compiler targets; the hot order loop on a microsecond budget is not.
Pyvorin for Fraud Detection
Batch scoring grids, feature aggregation and anomaly loops are strong measured matches; inline authorisation-path scoring is not a compiler problem.
Pyvorin for Quantitative Trading
Backtests, signal generation and portfolio simulations accelerate well; market-data parsing and the live order path do not. A candid map of the boundary.
Pyvorin for Regulatory Reporting
Report pipelines split cleanly: enrichment, validation and aggregation loops have strong measured anchors; parsing, joins and formatting regress.
Pyvorin for Risk Management
Monte Carlo simulation, correlation matrices and stress-test grids are strong measured matches; intraday limit monitoring on a latency budget is not.
Pyvorin vs Codon
Codon compiles Python-syntax code to standalone native binaries; Pyvorin compiles the Python you already run, in-process, beside CPython. Where each fits.
Pyvorin vs CPython
Pyvorin runs your existing Python as native machine code beside CPython. Where that helps, where it does not, and the measured results from 71 workloads.
Pyvorin vs Cython
Cython asks you to maintain a second language for speed. Pyvorin compiles the Python you already have. The right choice depends on who owns the code.
Pyvorin vs Mojo
Mojo and Pyvorin both promise Python-like ergonomics with native performance, but they answer different questions. Goals, fit, and honest trade-offs.
Pyvorin vs Nuitka
Nuitka's deliverable is a binary you can hand to a customer. Pyvorin's deliverable is speed inside the Python environment you already run. Choose accordingly.
Pyvorin vs Numba
Both tools turn Python into machine code through LLVM. The deciding factor is what your slow code looks like — and this page shows how to find out.
Reducing Python CPU Usage Without Rewriting
Profile first, compile what the profile points at: a measured workflow for lower Python CPU usage, the workloads where it works, and a labelled capacity model.
8 min read
Supported Workloads
Measured across 71 workloads: numerical, object-manipulation, image and financial code win big; string, parsing and compression do not. Check yours in minutes.
Warm-Up and Caching Strategies
Where pyvorin-native 1.0.9 caches compiled code, what a cache hit is worth in measured milliseconds, and how to key the cache in CI.
What AOT Compilation Means for Python
Interpreters, JITs and AOT compilers for Python: how each works, when each wins, and where Pyvorin Native 1.0.9's local compilation fits.
10 min read
Why Python Is Slow — and What Actually Helps
Bytecode dispatch, boxing and reference counting: where Python's time actually goes, the honest hierarchy of fixes, and what measurement says each one buys.
9 min read