Computing Power Requirements for AI Training
computing power
Model training requires a lot of computing resources, including CPU (
Central Processing Unit), GPU (Graphical Processing Unit), TPU (Tensor Processing Unit), among which GPU is the most common hardware accelerator. In addition, the efficiency of model training can be improved through algorithm optimization. For example, distributed training technology (distribute data and model parameters to multiple machines for calculation), and model compression technology can also be used to compress the size of the model to a minimum.
GPU
Also known as image processor, it is a hardware device specially used in image processing, computer vision, deep learning and other fields. Compared with cpu, GPU has powerful parallel computing capability
Hardware configuration requirements for AI training
The memory and storage space required for model training will increase with the size of the dataset and the complexity of the model. Dataset size and model complexity need to be considered when choosing a hardware configuration.
Computing power requirements for Ai model reasoning
Reasoning is also called prediction. After the model is trained, there is no need for a lot of calculations, so the requirements for computing power are relatively low.
Optimization of Algorithmic Reasoning
The optimization of algorithmic reasoning can be achieved through model pruning technology (deleting some unnecessary parameters in the model) and model quantization technology (converting floating-point numbers in the model to integers).
Computing power optimization method
- Use cloud computing services to
dynamically adjust the scale and configuration of computing resources based on demand - Use distributed training technology
to distribute the training process of the model to multiple machines for calculation, thereby accelerating the calculation speed. Currently, deep learning frameworks such as tensorflow and pytorch support distributed training techniques. - Use algorithm optimization techniques such as
model pruning, model quantization, and dynamic calculation graphs to optimize algorithms. Automated machine learning techniques can also be used to automatically select optimal algorithms and hyperparameters to improve model accuracy and efficiency.
Computer Configuration
- High-performance GPU: NGIDIA's GetForce RTX 3080, 3090 (3999 yuan) or Tesla V100 (12000). These are the current best configurations (2023.02.01)
- Memory: try to be above 16G
- Fast hard drive: SSD (solid state disk) solid state drive
- cpu: Inter Core i9 or AMD Ryzen 9