metaflow-profiler 0.1.0

Interactive flamegraph profiling for Metaflow steps — pluggable backends, beautiful cards

metaflow-profiler

Add one decorator to any Metaflow step and get an interactive flamegraph card in the UI.

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When a Metaflow step is slow or runs out of memory, finding the cause means adding profiling code, re-running locally, deciphering cProfile tables, and correlating a separate top session — all before you can start actually fixing anything.

@profile_card wraps any step with a single decorator. It captures the CPU call tree, memory allocations, and system resource usage, then renders a self-contained interactive card directly in the Metaflow UI — visible even when the step crashes.

Quick start

pip install metaflow-profiler[pyinstrument]

from metaflow import FlowSpec, step
from metaflow_extensions.profiler.plugins.profile_decorator import profile_card

class MyFlow(FlowSpec):

    @profile_card(profiler="pyinstrument")
    @step
    def train(self):
        # ... your heavy computation ...
        self.next(self.end)

    @step
    def end(self):
        pass

if __name__ == "__main__":
    MyFlow()

python flow.py run
python flow.py card view --id profile_card_train

What you get

Stats grid

Duration, sample count, peak/avg CPU, peak/avg memory — plus disk I/O, network, and GPU stats when present. Stat cards appear automatically and hide when zero.

CPU Flamegraph

Every function call is a coloured block; width represents time spent.

Search highlights matching frames across the whole tree while dimming everything else — useful for tracking down a specific function across multiple call paths.

Click to zoom into any frame. A breadcrumb trail lets you navigate back up the call stack.

Memory Flamegraph

When memray is installed (pip install metaflow-profiler[memray]), a second flamegraph shows which functions allocated the most memory at peak RSS, in MB.

Resource Timeline + I/O Timeline

Dual-axis time-series charts polled every 500 ms throughout the step.

• Resource Timeline — CPU % (left axis) and RSS memory in MB (right axis)
• I/O Timeline — Disk read/write MB/s (left axis) and network recv/sent MB/s (right axis)

Both charts share the same time axis so you can correlate spikes across metrics.

cProfile backend

The cprofile backend uses Python's built-in profiler — no extra dependencies. It captures every function call so sample counts are much higher. The flamegraph is otherwise identical.

Failed steps

The card renders even when the step raises an exception — it shows the full profile up to the point of failure with a red banner at the top.

Backends

Backend	Install	Overhead	Notes
pyinstrument	pip install metaflow-profiler[pyinstrument]	~1%	Statistical; recommended
cprofile	(built-in)	Medium	Deterministic; captures every call

Optional extras

Extra	Install	Adds
memray	pip install metaflow-profiler[memray]	Memory allocation flamegraph
gpu	pip install metaflow-profiler[gpu]	GPU utilisation % + GPU memory timeline
all	pip install metaflow-profiler[all]	Everything above

How it works

@profile_card decorator
    ↓  starts backend in task_pre_step, stops in task_post_step / task_exception
Card renderer (ProfileCard)
    ↓  reads artifact, renders self-contained HTML
Backend registry
    ↓  picks best available backend
Backend implementations (pyinstrument / cprofile)
    ↓  wraps _TimelineCollector (psutil) + _MemoryTracker (memray)
Abstract interface (ProfilerBackend / ProfileData)

No upward imports between layers — enforced by structural tests.

Development

git clone https://github.com/npow/metaflow-profiler
cd metaflow-profiler
pip install -e ".[pyinstrument,dev]"

# Lint + type check
ruff check src/ tests/
mypy src/

# Tests
pytest tests/unit/
pytest tests/structural/ -m structural

License

Apache 2.0 — see LICENSE.