traceml-ai 0.2.7

TraceML: Lightweight training runtime health monitor.

TraceML

Find why PyTorch training is slow while the job is still running.

Quickstart • How to Read Output • FAQ • Use with W&B / MLflow • Issues

TraceML helps you find training bottlenecks in PyTorch while the job is still running. It helps you catch:

• input bottlenecks
• compute-bound steps
• DDP stragglers
• wait-heavy training
• memory creep over time

without jumping straight to a heavyweight profiler.

Why this exists: dashboards show utilization and curves. TraceML shows why throughput is poor inside the training step.

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The fastest way to try it

Install:

pip install traceml-ai

Wrap your training step:

from traceml.decorators import trace_step

for batch in dataloader:
    with trace_step(model):
        optimizer.zero_grad(set_to_none=True)
        outputs = model(batch["x"])
        loss = criterion(outputs, batch["y"])
        loss.backward()
        optimizer.step()

Run:

traceml run train.py

During training, TraceML opens a live terminal view alongside your logs.

At the end of the run, it prints a compact summary you can review or share.

For full setup details, see docs/quickstart.md.

Not sure how to interpret the output? Read How to Read TraceML Output.

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What TraceML tells you

TraceML helps answer questions like:

• Is training input-bound or compute-bound?
• Is one DDP rank slower than the others?
• Is the job wait-heavy because of uneven progress?
• Is memory drifting upward over time?
• Is the slowdown coming from dataloader, forward, backward, or optimizer work?

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When to use TraceML

Use TraceML when training feels:

• slower than expected
• unstable from step to step
• imbalanced across distributed ranks
• fine in dashboards but still underperforming

Start with TraceML when you need a fast answer in the terminal. Reach for torch.profiler once you know where to dig deeper.

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How it fits with your stack

TraceML is designed to work alongside tools like W&B, MLflow, and TensorBoard.

Use those for:

• experiment tracking
• artifacts
• dashboards
• team reporting

Use TraceML for:

• bottleneck diagnosis
• rank imbalance / straggler detection
• memory trend debugging

See Use TraceML with W&B / MLflow.

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Current support

Works today:

• single GPU
• single-node DDP/FSDP

Not yet:

• multi-node
• tensor parallel
• pipeline parallel

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Next steps

• Quickstart
• Examples
• How to Read TraceML Output
• FAQ
• Use TraceML with W&B / MLflow
• Hugging Face integration: docs/huggingface.md
• PyTorch Lightning integration: docs/lightning.md

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Feedback

If TraceML helped you find a slowdown, please open an issue and include:

• hardware / CUDA / PyTorch versions
• single GPU or multi-GPU
• whether you used run, watch, or deep
• the end-of-run summary
• a minimal repro if possible

GitHub issues: https://github.com/traceopt-ai/traceml/issues

Email: [e-mail]

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Contributing

Contributions are welcome, especially:

• reproducible slowdown cases
• bug reports
• docs improvements
• integrations
• examples

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License

Apache 2.0. See LICENSE.