MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to train a deep learning model. The training job is failing with an error 'CUDA out of memory'. The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model architecture and batch size are appropriate for this instance size. What is the most likely cause of this error?
⚠ Common exam trap
The trap here is that candidates may incorrectly assume the solution is to reduce epochs (Option A) or scale out to more GPUs (Option B), when the root cause is memory exhaustion per GPU, which is best addressed by mixed precision training to halve the memory footprint without altering the model or batch size.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable automatic mixed precision (AMP) training to reduce memory usage.
Enabling automatic mixed precision (AMP) training reduces GPU memory usage by storing tensors in half-precision (FP16) where possible, while keeping critical operations in full precision (FP32). This directly addresses the 'CUDA out of memory' error on an ml.p3.2xlarge instance (16 GB GPU memory) without changing the model architecture or batch size, which are already appropriate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of epochs.
Why it's wrong here
Epochs do not affect per-step memory usage; they affect training time.
- ✗
Increase the number of GPUs by using a distributed training instance type.
Why it's wrong here
Adding more GPUs may not help if the memory per GPU is the same; the error is per GPU.
- ✓
Enable automatic mixed precision (AMP) training to reduce memory usage.
Why this is correct
AMP uses FP16 where possible, cutting memory usage roughly in half, which often resolves out-of-memory errors.
- ✗
Use a smaller instance type to force lower memory usage.
Why it's wrong here
A smaller instance has even less memory, making the problem worse.
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