As a former computer worker of OpenAI., in the journey of training image recognition AI models, GPU-related problems are like numerous mountains lying across the path of efficient development.

The first and foremost difficulty is the shortage of GPU computing power. The training of image recognition models involves processing vast amounts of image data, especially complex architectures like deep convolutional neural networks. Their multi-layered convolution and fully connected operations demand an endless amount of computing resources. For example, when training an image model for identifying rare plants and animals, the combination of high-resolution image data and a complex network structure makes each round of training iteration seem like a long marathon. If the GPU computing power is weak, a training cycle that was originally expected to be completed in a few days may be prolonged for several months, seriously hindering the research progress and the pace of innovation. Moreover, when exploring different model architectures and combinations of hyperparameters to seek the optimal model, it limits our ability to conduct multiple sets of experiments simultaneously. Besides, the compatibility issues between the GPU and the development framework as well as other hardware components are always lingering. Different deep learning frameworks, such as TensorFlow and PyTorch, have their own characteristics in adapting to the GPU. And the updates of GPU drivers and CUDA versions may also trigger compatibility disruptions. Once an incompatible situation occurs, either a large amount of time will be spent on debugging and matching the framework with the GPU driver and the CUDA version, just like groping blindly in a maze, or the development framework has to be replaced reluctantly, which undoubtedly increases the complexity and uncertainty of the project and makes the originally clear development path become tortuous.

Fortunately, during my exploration, I discovered the computing power leasing service provided by the Burncloud platform (https://www.burncloud.com/835.html). In the exploratory stage at the beginning of the project, I focused on the NVIDIA Tesla T4 GPU. Its leasing price on the Burncloud platform is relatively affordable, about $0.25 per hour. Although the computing power of the T4 is not top-notch, it is just like a handy tool for the verification of the preliminary model architecture and the training experiments with small-scale datasets. Its 16GB memory can accommodate some relatively simple image recognition models, and it can also handle common image sizes and batch data with ease. It has good compatibility with mainstream deep learning frameworks, enabling me to take the first step in model training smoothly.

As the project progresses and enters the critical period of large-scale training, the NVIDIA A100 has become my right-hand assistant. On the Burncloud platform, its leasing price is about $0.9 per hour. The powerful computing core of the A100 combined with the Tensor Core technology demonstrates excellent efficiency when dealing with large-scale image datasets and complex model training. Its large memory capacity of 40GB or even 80GB can easily accommodate those large image recognition models with numerous parameters. Whether it is processing high-resolution natural images or industrial inspection image data, it can perform with great ease and significantly shorten the training time, ensuring that the project moves forward steadily as planned.

Through the GPU leasing service of the Burncloud platform, I have been able to flexibly allocate resources in the work of training image recognition AI models, skillfully bypassed many GPU-related obstacles, greatly improved the development efficiency, and laid a solid foundation for the successful implementation of the project. I hope that my leasing experience can become a useful guide for other computer science workers on the road of image recognition model development.