Causes of TensorFlow Crashing due to Memory
- Excessive Model Size: Large neural networks with numerous layers and parameters can utilize significant GPU and CPU memory. Ensure that your architecture is as compact as possible without compromising performance.
- Batch Size: Utilizing a batch size that is too large can result in memory overload. If crashing occurs, attempt to reduce the batch size as a solution.
- High Resolution Inputs: Training with high-resolution images or data can quickly consume available memory. Resize and normalize your input data properly.
- Memory Leaks: Unintentional retention of references to model variables or tensors in Python code can lead to memory leaks. Use careful practices to ensure references are removed and memory is freed.
- Inappropriate Memory Allocation: TensorFlow tries to allocate essentially all available GPU memory by default. Mismanagement in memory allocation can lead to crashes.
Solutions and Optimization Techniques
- Model Checkpoints: Always save model checkpoints, so you can resume training later without starting over. This helps avoid data loss after a crash.
- Gradient Checkpointing: To reduce memory usage, use gradient checkpointing to avoid storing all intermediate activations during backpropagation.
- Memory Growth: Enable memory growth for GPU to allocate memory on demand instead. Use this TensorFlow option with caution:
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
except RuntimeError as e:
print(e)
- Monitoring: Use monitoring options in TensorFlow, such as TensorBoard, to keep track of memory usage and performance issues.
- Reduce Redundant Operations: Reuse model layers and operations whenever possible instead of defining new ones with the same configuration.
- Profiling Tools: Use TensorFlow Profiler to analyze device utilization and operations that use excessive resources:
tf.profiler.experimental.start(logdir="<log_directory>")
# Perform training here
tf.profiler.experimental.stop()
