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Your team is developing a convolutional neural network (CNN) from scratch for a project that requires rapid iteration and deployment. Initial tests on your on-premises CPU-only setup showed promising results but are slow to converge, impacting your project's time-to-market. To address this, you're considering Google Cloud's virtual machines (VMs) to leverage more robust hardware. Your current codebase does not include manual device placement and hasn't been wrapped in an Estimator model-level abstraction. Given these constraints, which of the following Google Cloud environments would be the most efficient and cost-effective for accelerating your CNN training while minimizing setup time and manual configuration? Choose the best option.
A
A Deep Learning VM configured with an n1-standard-2 machine and 1 GPU, which comes with all necessary deep learning libraries and frameworks pre-installed, allowing for immediate start of training without the need for manual dependency installation.
B
A Compute Engine VM equipped with 8 GPUs, offering high computational power but requiring manual installation and configuration of all deep learning dependencies, which could delay the start of your training process.
C
A Compute Engine VM with 1 TPU, providing specialized hardware for deep learning tasks but necessitating manual setup of all dependencies and potential code modifications to leverage the TPU's capabilities.
D
A Deep Learning VM with e2-highcpu-16 machines, optimized for CPU performance and pre-installed with all necessary libraries, though lacking GPU acceleration which is crucial for CNN training speed.
E
Both A and C, as using a GPU for initial rapid experimentation and a TPU for scaling up training could offer a balanced approach between speed and scalability, despite the initial setup overhead for the TPU.