traceml-ai 0.2.10

TraceML: Lightweight training runtime health monitor.

TraceML

Catch PyTorch training slowdowns early, while the job is still running.

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

TraceML is an open-source tool for catching PyTorch training slowdowns early, so bad runs do not quietly waste costly compute.

It gives you lightweight step-level signals while the job is still running, so you can quickly tell whether the slowdown looks input-bound, compute-bound, wait-heavy, imbalanced across ranks, or memory-related.

Use TraceML when you want a fast answer before reaching for a heavyweight profiler.

⭐ If TraceML helps you, please consider starring the repo.

Upcoming rename: TraceML will transition to TraceOpt in a future release. For now, the active package remains traceml-ai and Python imports remain traceml. The future PyPI package name traceopt-ai is now in place as we prepare the migration.

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The fastest way to try it

Install:

pip install traceml-ai

Wrap your training step:

import traceml

for batch in dataloader:
    with traceml.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.

Start with traceml run train.py. Most users do not need watch or deep first.

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Core workflows

1. Live diagnosis

Use the default workflow when you want live step-aware diagnosis during training plus the end-of-run summary.

traceml run train.py

2. Low-noise summary runs

Use summary mode when you mainly want the structured final summary for logging into W&B or MLflow.

traceml run train.py --mode=summary

Then call traceml.final_summary() near the end of your script.

TraceML also writes canonical summary artifacts for the run, including final_summary.json, which is the intended machine-readable output for downstream logging and later run comparison.

3. Compare two runs

If you have final_summary.json from two runs, compare them directly:

traceml compare run_a.json run_b.json

TraceML writes both a structured compare JSON and a compact text report.

See docs/compare.md.

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What TraceML helps you see

TraceML is currently strongest at surfacing:

• step-time slowdowns while training is still running
• whether the pattern looks input-bound, compute-bound, or wait-heavy
• whether work is uneven across distributed ranks
• whether memory is drifting upward over time
• where time is showing up across dataloader, forward, backward, and optimizer phases

It is designed to help you decide quickly whether a run looks healthy or whether it is worth digging deeper.

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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 while a run is still in progress
• spotting throughput drift during a run
• checking for rank imbalance or straggler patterns
• checking for memory creep or pressure signals
• structured final summaries you can forward into W&B or MLflow
• simple run-to-run comparison from saved TraceML summary JSON files

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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Learn more

• Quickstart
• Compare Runs
• 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

Need a lighter zero-code first look or a deeper follow-up run? See the Quickstart and FAQ for watch and deep.

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Feedback

If TraceML helped you catch 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.