Which approaches will meet this requirement?
(Choose two.)
Replace On-Demand Instances with Spot Instances.
Configure model auto scaling dynamically to adjust the number of instances automatically.
Replace CPU-based EC2 instances with GPU-based EC2 instances.
Use multiple training instances.
Use a pre-trained version of the model. Run incremental training.
Explanations:
While Spot Instances can be more cost-effective, they do not inherently decrease training time. Training time is primarily dependent on instance type and configuration rather than the pricing model used.
Auto-scaling adjusts the number of instances based on demand but does not necessarily reduce the training time for a single model training job. The training process on each instance remains unchanged.
Replacing CPU-based EC2 instances with GPU-based instances can significantly decrease training time, especially for algorithms like DeepAR that can benefit from parallel processing capabilities offered by GPUs.
Using multiple training instances allows the workload to be distributed across several instances, which can reduce overall training time by parallelizing the training process.
Using a pre-trained model and running incremental training can be efficient, but it does not guarantee a decrease in training time for the initial model training phase. The approach may not be applicable if the model is not already pre-trained.
I classify that the answer is:
Replace CPU-based EC2 instances with GPU-based EC2 instances.
I assess that the answer is:
Replace CPU-based EC2 instances with GPU-based EC2 instances.