TraceML integrations
Diagnose training in the stack you already use.
Framework integrations add TraceML's step-level diagnosis without replacing your model, dataset, or training workflow.
Keep the framework your team already knows. Add the measurements needed to explain slow steps and idle GPUs.
Integrated today
A callback or wrapper that leaves your loop in charge.
Hugging Face Trainer
TraceMLTrainerCallback
PyTorch Lightning
TraceMLCallback
Ray Train
TraceMLTorchTrainer
Available now
Choose the adapter that matches your training loop.
Hugging Face Trainer
Initialize TraceML once, then add TraceMLTrainerCallback to your existing Trainer. Try the real ResNet-50 input-pipeline experiment in Colab.
View walkthrough
PyTorch Lightning
Add TraceMLCallback to your Trainer. Keep your LightningModule, callbacks, accelerator settings, and training workflow.
Read integration docs ↗
Ray Train
Wrap your TorchTrainer with TraceMLTorchTrainer. Keep Ray scheduling, scaling, worker startup, and rank setup.
Read integration docs ↗