Blackfire Continuous Profiler
Low-overhead continuous profiling for app performance optimization.
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Fast line‑level CPU, GPU, and memory profiling with AI suggestions
Scalene delivers ultra‑fast line‑level CPU, GPU, and memory profiling for Python, with AI‑powered optimization suggestions and an interactive web UI, all with minimal overhead.
Scalene is a high‑performance profiler for Python that measures CPU, GPU (NVIDIA only), and memory usage at the line level. By using sampling instead of instrumentation, it keeps overhead typically between 10‑20%, allowing developers to profile real‑world workloads without significant slowdown.
Run Scalene from the command line, integrate it into VS Code, or embed it programmatically with a simple decorator. After profiling, a self‑contained HTML report opens automatically, offering sortable tables, per‑line heatmaps, and clear distinctions between Python and native code time. For deeper insight, enable AI‑powered optimization suggestions from providers such as OpenAI, Azure, Amazon Bedrock, or local Ollama models. Click the lightning‑bolt icon next to any hotspot to receive a GPT‑4 generated refactor, copy it, and iterate.
Scalene works on Linux, macOS, and Windows (with NVIDIA GPUs) and can be installed via pip or conda, making it easy to add to CI pipelines or local development environments.
When teams consider Scalene, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Find CPU hotspots in a web service
Identify and refactor the slowest functions, reducing request latency by up to 30%.
Detect memory leaks in a data pipeline
Pinpoint lines causing unexpected memory growth, enabling targeted fixes and stable long‑run processing.
Optimize GPU kernels in a deep‑learning model
Measure per‑line GPU time, reveal inefficient data transfers, and improve training throughput.
Generate AI‑driven refactoring suggestions
Receive concrete code changes from GPT‑4 that can halve execution time for identified bottlenecks.
Run `python3 -m pip install -U scalene` or `conda install -c conda-forge scalene`.
Yes, use the `--cli` flag to get a text‑only report.
GPU profiling works on NVIDIA GPUs; other vendors are not currently supported.
They need access to the chosen AI provider—cloud services require internet, while local models via Ollama can run offline.
Add the `@profile` decorator to the functions you want to monitor and run Scalene as usual.
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