Keras uses way too much GPU memory when calling train_on_batch, fit, etc
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Keras, a popular deep learning library that acts as an interface for TensorFlow, has been widely adopted for its simplicity and high-level APIs. However, users frequently encounter an issue where Keras consumes an excessive amount of GPU memory when training models using functions like `train_on_batch` or `fit`. This article delves into the underlying reasons behind this behavior, offering technical explanations, insights, and potential strategies to address this limitation.
Understanding GPU Memory Usage in Keras
The excessive GPU memory usage primarily stems from TensorFlow's design and Keras's interaction with it. TensorFlow allocates memory for operations on the GPU at runtime, and Keras builds on top of TensorFlow by constructing computational graphs that are executed as sessions. Here are some reasons for high GPU memory utilization:
1. Memory Preallocation
By default, TensorFlow preallocates the entire memory space of all GPUs to avoid the fragmentation problem which occurs due to dynamic memory allocation. This means even if a small model is being trained, it could potentially consume all available GPU memory.
2. Computation Graphs
Keras builds computation graphs for model training. As models become more complex (e.g., larger input sizes, more layers), these graphs consume significant memory resources. This can lead to high memory usage even with smaller batch sizes.
3. Multi-threading
Keras and TensorFlow operations are executed in a multi-threaded environment which can result in duplicate or excessive memory allocation due to thread context switching and management overhead.
4. Caching
Keras's backend might cache datasets and intermediate operations which can contribute to increased memory consumption during training.
Examples of High GPU Memory Usage
When using the `train_on_batch` or `fit` methods, you might encounter scenarios where your model, although simple in structure, appears to consume more GPU memory than anticipated:
- Use Mixed Precision: Employ mixed-precision training using float16 to reduce memory usage and possible speedup training.
- Distributed Training: Utilize multiple GPUs to distribute memory load and enhance performance potentially using strategies like data parallelism.
