Blackfire Continuous Profiler
Low-overhead continuous profiling for app performance optimization.
Scalene
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.
Overview
Overview
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.
Capabilities & Deployment
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.
Highlights
Line‑level CPU, GPU, and memory profiling with low overhead
AI‑driven optimization suggestions from multiple providers
Interactive web‑based GUI with sortable, color‑coded reports
Supports @profile decorator and programmatic control
Pros
- Minimal performance impact (10‑20% overhead)
- Accurate separation of Python vs. native code time
- AI suggestions can reveal non‑obvious improvements
- GPU profiling for NVIDIA devices
Considerations
- GPU profiling limited to NVIDIA hardware
- AI suggestions require external API keys or local model setup
- Quality of AI recommendations may vary
- Sampling still adds some overhead for ultra‑tight loops
Managed products teams compare with
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.
Fit guide
Great for
- Python developers seeking fast, detailed performance insights
- Data scientists optimizing NumPy or CUDA‑accelerated code
- Teams that want visual profiling reports for stakeholders
- CI pipelines that need automated, low‑impact profiling
Not ideal when
- Projects without any GPU usage
- Environments without internet access for AI services
- Languages other than Python
- Scenarios requiring full instrumentation rather than sampling
How teams use it
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.
Tech snapshot
Python53%
JavaScript40%
C++6%
HTML1%
Makefile1%
CSS1%
Frequently asked questions
How do I install Scalene?
Run `python3 -m pip install -U scalene` or `conda install -c conda-forge scalene`.
Can I use Scalene without the web UI?
Yes, use the `--cli` flag to get a text‑only report.
Which GPUs are supported?
GPU profiling works on NVIDIA GPUs; other vendors are not currently supported.
Do AI suggestions require an internet connection?
They need access to the chosen AI provider—cloud services require internet, while local models via Ollama can run offline.
How do I profile only specific functions?
Add the `@profile` decorator to the functions you want to monitor and run Scalene as usual.
Project at a glance
Active- Stars
- 13,235
- Watchers
- 13,235
- Forks
- 431
LicenseApache-2.0
Repo age6 years old
Last commit3 hours ago
Primary languagePython
Last synced 3 hours ago