GPU only being used 1-5 Tensorflow-gpu and Keras
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TensorFlow and Keras are widely used libraries for deep learning applications. They offer both CPU and GPU execution capabilities, allowing developers to leverage the power of Graphics Processing Units (GPUs) for faster computation. However, achieving full utilization of the GPU is often more complex than it seems, and many users experience low GPU usage, typically between 1-5%. This article will explore why this happens, provide technical insights, and suggest ways to potentially increase GPU utilization.
Understanding GPU Utilization
Basic Concepts
When using TensorFlow and Keras, you may expect the heavy mathematical operations characteristic of deep learning to automatically result in high GPU usage. However, three primary components control GPU utilization:
- Model Architecture: The design and complexity of the neural network can influence whether computations are bottlenecked at the model level.
- Batch Size: Larger batch sizes generally result in better GPU utilization as more data is processed in parallel.
- Input Pipeline: The method of feeding data into the model can often be the bottleneck if not optimized for GPU usage.
Why GPUs May Be Underutilized
Several factors contribute to GPUs being underutilized:
- Input Data Pipeline: If data preprocessing is done on the CPU and is slow, or if the data is not being fed in fast enough, the GPU may remain idle while waiting for data.
- Model Complexity: Simple models do not require many computational resources, leading to lower GPU usage.
- GPU Configuration: TensorFlow may not be configured correctly to make full use of the GPU's capabilities, such as memory allocation issues or improper device placement.
- Inter-Process Communication: Overheads in transferring data between CPU and GPU can hinder performance.
- Kernel Launch Overheads: Each GPU task needs to be launched as a kernel, and excessive operations with many kernel launches can result in overheads.
Improving GPU Utilization
Techniques and Best Practices
- Optimize Input Pipeline: Use `tf.data` API for efficient data loading and processing. Prefetch data to overlap the preprocessing and model execution on the GPU:
