Google Professional Machine Learning Engineer
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You are tasked with training a high-accuracy computer vision model on a GPU-powered virtual machine in Google Compute Engine to classify government ID types from images. The model is expected to handle a wide variety of ID formats with high precision. During the training phase, you encounter a 'ResourceExhaustedError: Out Of Memory (OOM)' when allocating tensor. Given the constraints of minimizing cost while ensuring the model's performance does not degrade significantly, what are the two most effective actions to resolve this issue? Choose two correct options.
A
Increase the virtual machine's GPU memory by upgrading to a higher-tier GPU, ensuring the model can handle larger batch sizes without memory issues.
B
Decrease the batch size to reduce memory consumption during training, accepting a potential increase in training time.
C
Modify the optimizer settings to 'adam' from 'sgd' to better manage resources, as 'adam' is known for its memory efficiency.
D
Resize the input images to smaller dimensions to lessen memory load, which may slightly reduce the model's accuracy.
E
Implement gradient checkpointing to trade off computation time for memory usage, allowing for larger effective batch sizes.