Train Transformers faster with IPUs
Graphcore and Hugging Face are working together to make training of Transformer models on IPUs fast and easy. Contact Graphcore to learn more about leveraging IPUs for your training needs.
Take advantage of the power of Graphcore IPUs to train Transformers models with minimal changes to your code thanks to the IPUTrainer class in Optimum. This plug-and-play experience leverages the full software stack of Graphcore so you can train state of the art models on state of the art hardware.
from optimum.graphcore import IPUConfig, IPUTrainer model_name_or_path = ... # IPUConfig specifying parameters related to the IPU ipu_config = IPUConfig.from_pretrained(model_name_or_path) trainer = IPUTrainer(model, ipu_config, training_args, train_dataset, eval_dataset) # Training train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Evaluation metrics = trainer.evaluate()
Best for NLP & Computer Vision
Accelerate training and inference models with high-performance optimisations across natural language processing, computer vision and more.
Graphcore’s IPU is powering advances in AI applications such as fraud detection for finance, drug discovery for life sciences, defect detection for manufacturing, traffic monitoring for smart cities and for all of tomorrow’s new breakthroughs.
The Poplar SDK is a complete software stack co-designed with the IPU for AI application development.
Graphcore’s Poplar graph toolchain is fully integrated with Transformers so developers can easily port existing models. For maximum performance, Poplar enables direct IPU programming in Python and C++.
Explore: The IPU-POD16 gives you the power and performance you need to fast-track Transformer IPU prototypes from pilot to production.
Build: Ramp up your AI projects, speed up production and accelerate time to business value with IPU-POD64.