AI training acceleration supports a variety of frameworks and models, including but not limited to TensorFlow, PyTorch, Keras, and MXNet. These frameworks are widely used in the field of deep learning and AI research.
For example, TensorFlow is an open-source platform for machine learning and artificial intelligence, which can be used to build and train various neural networks. PyTorch is another popular open-source machine learning library that is widely used in research and industry, known for its dynamic computational graph and efficient memory usage.
In addition, AI training acceleration also supports various pre-trained models, such as ResNet, VGG, Inception, etc., which are commonly used in image recognition, natural language processing and other tasks. These pre-trained models can be fine-tuned and adapted to specific tasks through transfer learning, thereby improving training efficiency and accuracy.
When it comes to cloud services, Tencent Cloud provides a comprehensive suite of AI acceleration services. For instance, Tencent Cloud's AI Training Acceleration (ATA) leverages high-performance computing resources and optimized algorithms to significantly speed up the training process of deep learning models. It supports popular frameworks like TensorFlow, PyTorch, and MXNet, and offers features like automatic model tuning and distributed training to further enhance training efficiency.