Google Professional Machine Learning Engineer
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Your team is developing a convolutional neural network (CNN) from scratch for a project that requires rapid iteration to meet a tight deadline. Initial tests on your on-premises CPU-only setup showed promise but are slow to converge, impacting your ability to iterate quickly. To accelerate training and reduce time-to-market, you're considering Google Cloud's virtual machines (VMs) for more robust hardware. Your code lacks manual device placement and isn't wrapped in an Estimator model-level abstraction. Given these constraints, which Google Cloud environment should you choose for training to ensure a balance between ease of setup, cost efficiency, and performance? Choose the best option.
Explanation:
The optimal choice for accelerating your CNN training on Google Cloud, considering the need for rapid iteration, ease of setup, and cost efficiency, is a Deep Learning VM with an n1-standard-2 machine and 1 GPU, pre-loaded with all libraries (Option C). This option stands out because:
Option D, focusing on CPU performance, may not meet the acceleration needs for CNN training. Option E suggests considering both C and D, but given the need for GPU acceleration, C is the superior choice. Thus, the selected option optimally combines ease of use with performance improvements.