Flamegraph Visualizer for py-spy Profiles in Python and Jupyter
Profiling your code with Flamegraph is simple.
In Jupyter notebooks, click the 🔥 button above the cell you want to profile:
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For Python scripts, select Flamegraph: Profile file with py-spy
from the dropdown menu next to the ▶️ icon:
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Your code will be profiled with py-spy. You can interrupt the profiling anytime via Ctrl+C
or wait for it to finish.
The profiling results are then visualized next to your code and as a flamegraph in a new tab.
To hide the inline annotions, right-click anywhere in the editor and select Flamegraph: Toggle Inline Profile
.
Usage
The extension visualizes profiling data in two ways:
Inline Code Annotations: Shows timing information for each function scope, with colors indicating the scope level.
Interactive Flamegraph: Displays the complete call stack of your profiled code (see this article about flamegraphs). You can:
- Click any element to zoom in
- Click parent elements to zoom out
Cmd+Click
(Mac) or Ctrl+Click
(Windows/Linux) any element to jump directly to that code.
The flamegraph and inline annotations are linked -
when you select an element in the flamegraph, the corresponding inline annotations are filtered.
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Useful Commands
Open the Command Palette (Command+Shift+P on Mac and Ctrl+Shift+P on Windows/Linux) and type in one of the following commands:
Command |
Description |
Flamegraph: Profile file with py-spy |
Profile the active file with py-spy and display the results |
Flamegraph: Load Profile |
Load a profile from a py-spy file. You may also right-click on .pyspy files in the file explorer and select Flamegraph: Load Profile . |
Flamegraph: Toggle Inline Profile |
Show or hide the inline annotations. This is also accessible via right-click on the editor. |
Flamegraph: Show |
Open a new tab showing the flamegraph |
Flamegraph: Attach py-spy to running process |
Attach py-spy to a running process and display the results. The extension will ask for a Process ID (PID) to attach to |
Flamegraph: Attach py-spy to running process (native) |
Attach py-spy, and also collect profiling data from native (e.g. C++) extensions. This is not supported on all platforms. See this blog post by Ben Frederickson |
Flamegraph: Profile all unit tests with pytest |
Run and profile the pytest command |
Flamegraph: Profile unit tests in file with pytest |
Run and profile the pytest command on the active file |
Flamegraph: Show py-spy top |
Displays a top like view of functions consuming CPU using py-spy |
Contributing
Development
- Clone the repository
git clone https://github.com/rafaelha/vscode-flamegraph.git
- Install dependencies for both the extension and the flamegraph-react UI
npm run install:all
- Build webview UI source code, i.e. the flamegraph react component
npm run build:webview
- In VS Code, press
F5
to open a new Extension Development Host window.
TODO
- [ ] Switch to
speedscope
format. Eventually, this extension should be refactored to be compatible with all profilers that output speedscope
files. Currently, only left-heavy profile view is supported.
- [ ] Unit tests
- [x] Performance tests
- [x] Option to filter the flamegraph by module.
- [ ] Refactor flamegraph react component. Currently, the whole graph is recomputed on every mouse hover event. We could consider using
speedscope
npm package to render the flamegraph.
- [ ] Search in flamegraph
- [ ] Profiling files without opening a workspace/folder. Currently, the extension requires a workspace/folder to be opened.
- [ ] Memray memory profiles
- [ ] Zoom animations in flamegraph
- [ ] Select sampling interval
- [x] Jupyter notebook profiling.