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Answer: A Deep Learning VM featuring an n1-standard-2 machine and 1 GPU, with all necessary libraries pre-installed. This option is optimized for machine learning tasks, offering a good balance between performance and ease of use., A Deep Learning VM with more powerful CPU e2-highcpu-16 machines, all libraries pre-installed. This option focuses on CPU performance but may not provide the GPU acceleration your project needs.
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: - **Pre-installed Libraries**: Eliminates setup time, allowing you to focus on training. - **GPU Acceleration**: Offers a significant speedup over CPU-only setups, enhancing convergence rates. - **Optimized for ML**: Specifically designed for machine learning tasks, ensuring an efficient training environment. - **Cost Efficiency**: While TPUs and multiple GPUs offer high performance, they're cost-prohibitive for smaller projects. A single GPU provides a practical balance between cost and performance. 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.
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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.
A
A VM on Compute Engine paired with 1 TPU, requiring manual installation of all dependencies. This option offers high performance but may introduce complexity and additional setup time.
B
A VM on Compute Engine equipped with 8 GPUs, necessitating manual setup of all dependencies. While this provides significant computational power, it may not be cost-effective for your project's scale.
C
A Deep Learning VM featuring an n1-standard-2 machine and 1 GPU, with all necessary libraries pre-installed. This option is optimized for machine learning tasks, offering a good balance between performance and ease of use.
D
A Deep Learning VM with more powerful CPU e2-highcpu-16 machines, all libraries pre-installed. This option focuses on CPU performance but may not provide the GPU acceleration your project needs.
E
Both options C and D provide a balance between ease of setup and performance, but for different aspects of machine learning tasks. Consider which aspect is more critical for your project.