Optimum + ONNX Runtime:讓 Hugging Face 模型訓練更簡單、更快速
Original: Optimum+ONNX Runtime - Easier, Faster training for your Hugging Face models
As the scale of deep learning models (such as Transformers) continues to grow, training these models demands enormous computational…
Hugging Face 介紹了 Optimum 庫與 ONNX Runtime (ORT) 的整合,為開發者提供更高效的訓練解決方案。透過將標準的 Trainer 替換為 ORTTrainer,開發者可以輕鬆啟用 ORT 的圖優化與記憶體管理技術。此方案在不犧牲模型精度的前提下,能顯著提升訓練吞吐量(通常可達 20%-40%)並降低 GPU 顯存佔用。
As the scale of deep learning models (such as Transformers) continues to grow, training these models demands enormous computational resources and time. To help developers reduce training costs and improve efficiency, Hugging Face partnered with Microsoft to integrate ONNX Runtime (ORT) training acceleration capabilities into Hugging Face's Optimum toolkit. ONNX Runtime is not only exceptional at model inference — its training module (ORT Training) can also significantly accelerate the training of PyTorch models through various graph optimization techniques and efficient memory management.
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