Learn how to optimize object detection models in autonomous cars to reduce memory, battery, and hardware consumption while maintaining accuracy. Discover the best solution using Amazon SageMaker Debugger and model pruning techniques.
Table of Contents
Question
An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK.
The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.
Which solution will improve the computational efficiency of the models?
A. Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.
B. Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.
C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
D. Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.
Answer
C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
Explanation
Answer: C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.
Explanation: Amazon SageMaker Debugger provides insights into the model’s internal state during training, including weights, gradients, biases, and activation outputs. By analyzing this information, you can identify low-ranking filters that contribute less to the model’s performance. Pruning these filters reduces the model’s complexity and computational requirements without significantly sacrificing accuracy.
After pruning the low-ranking filters, you set new weights based on the pruned set of filters and run a new training job with the optimized model. This process allows the model to adapt to the reduced architecture while maintaining its performance.
The other options are not as effective:
A. Amazon CloudWatch metrics do not provide the same level of visibility into the model’s internal state as SageMaker Debugger.
B. Collecting a larger labeled dataset using Amazon SageMaker Ground Truth does not address the computational efficiency issue and may even increase the model’s complexity.
D. Monitoring the model’s latency using Amazon SageMaker Model Monitor and increasing the learning rate do not directly optimize the model’s architecture for computational efficiency.
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