traceml-ai 0.2.2

TraceML: Lightweight training runtime health monitor.

TraceML

Know what’s slowing your (PyTorch) training, while it runs

TraceML provides step-level training visibility for PyTorch workloads. It shows where time and memory go inside each training step so you can quickly understand performance behavior across single-GPU and single-node DDP runs.

Current support

• ✅ Single GPU
• ✅ Single-node multi-GPU (DDP)
• ❌ Multi-node DDP (not yet)
• ❌ FSDP / TP / PP (not yet)

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What You See in Minutes

• System signals (CPU, RAM, GPU)
• Breakdown of each training step:
  • dataloader → forward → backward → optimizer → overhead
• Median vs worst rank (in case of DDP)
• Skew (%) to surface imbalance
• GPU memory (allocated + peak)

Healthy runs are clearly stable. Unstable runs reveal drift, imbalance, or memory creep early.

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Quick Start

Install:

pip install traceml-ai

Wrap your training step:

from traceml.decorators import trace_step

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

Run with cli:

traceml run train.py 

The terminal dashboard opens alongside your logs.

Optional web UI:

traceml run train.py --mode=dashboard

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What TraceML Surfaces

Step-Level Signals

• Dataloader fetch time
• Step time (low-overhead, GPU-aware)
• Step GPU memory (allocated + peak)

Across ranks:

• Median (typical behavior)
• Worst rank (slowest / highest memory)
• Skew (% difference)

This makes rank imbalance and straggler behavior immediately visible.

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Deep-Dive Mode (Optional)

Enable model-level hooks for diagnostic context:

from traceml.decorators import trace_model_instance
trace_model_instance(model)

Use together with trace_step(model) to enable:

• Per-layer memory signals
• Per-layer forward/backward timing
• Lightweight failure attribution (experimental)

If not enabled, ESSENTIAL signals remain unchanged.

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What It Is Not

• Not a replacement for PyTorch Profiler or Nsight
• Not an auto-tuner
• Not a kernel-level tracer

TraceML focuses on step-level visibility that is practical during real training runs.

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Supported Environments

• Python 3.9--3.13
• PyTorch 1.12+
• macOS (Intel/ARM), Linux
• Single GPU
• Single-node DDP

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Hugging Face Integration

TraceML provides a seamless integration with Hugging Face transformers via TraceMLTrainer.

Usage

Replace transformers.Trainer with traceml.hf_decorators.TraceMLTrainer.

from traceml.hf_decorators import TraceMLTrainer

trainer = TraceMLTrainer(
    model=model,
    args=training_args,
    train_dataset=train_ds,
    eval_dataset=eval_ds,
    traceml_enabled=True,         
)

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Roadmap

Near-term: - Single-node DDP hardening - Disk run logging - Compatibility validation (gradient accumulation, torch.compile) - Accelerate / Lightning wrappers

Next: - Multi-node DDP - Initial FSDP support

Later: - Tensor / Pipeline parallel awareness

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Contributing

Contributions are welcome.

When opening issues, include: - Minimal repro script - Hardware + CUDA + PyTorch versions - ESSENTIAL vs DEEP-DIVE - Single GPU vs DDP

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Community & Support

Founding Engineer / Co-Founder track (Berlin/Germany): We are looking for a senior systems+ML builder to help grow TraceML into a sustainable AI infra product. See the GitHub Discussion https://github.com/traceopt-ai/traceml/discussions/36

• 📧 Email: [e-mail]
• 🐙 LinkedIn: Abhinav Srivastav
• 📋 User Survey (2 min): https://forms.gle/KwPSLaPmJnJjoVXSA

Stars help more teams find the project. 🌟

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License

TraceML is released under the Apache 2.0.

See LICENSE for details.

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Citation

If TraceML helps your research, please cite:

@software{traceml2024,
  author = {TraceOpt},
  title = {TraceML: Real-time Training Observability for PyTorch},
  year = {2024},
  url = {https://github.com/traceopt-ai/traceml}
}

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Made with ❤️ by TraceOpt