Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises…

In this scenario, the goal is to speed up model training for a CNN-based architecture on Google

Cloud. The code does not include any manual device placement and has not been wrapped in

Estimator model-level abstraction. Given these constraints, the best environment to train the model

on would be a Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-

installed. Option C is the correct answer.

Option C: A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-

installed. This option is the most suitable for the scenario because it provides a ready-to-use

environment for deep learning on Google Cloud. A Deep Learning VM is a specialized VM image that

is pre-installed with popular deep learning frameworks such as TensorFlow, PyTorch, Keras, and

more. A Deep Learning VM also comes with NVIDIA GPU drivers and CUDA libraries that enable GPU

acceleration for model training. A Deep Learning VM can be easily configured and launched from the

Google Cloud Console or the Cloud SDK. An n1-standard-2 machine is a general-purpose machine

type that provides 2 vCPUs and 7.5 GB of memory. This machine type can be sufficient for running a

CNN-based architecture. A GPU is a specialized hardware accelerator that can speed up the

computation of matrix operations and convolutions, which are common in CNN-based architectures.

By using a Deep Learning VM with an n1-standard-2 machine and 1 GPU, the model training can be

significantly faster than on an on-premises CPU-only infrastructure.

Option A: A VM on Compute Engine and 1 TPU with all dependencies installed manually. This option

is not suitable for the scenario because it requires manual installation of dependencies and device

placement. A TPU is a custom-designed ASIC that can provide high performance and efficiency for

TensorFlow models. However, to use a TPU, the code needs to include manual device placement and

be wrapped in Estimator model-level abstraction. Moreover, to use a TPU, the dependencies such as

TensorFlow, Cloud TPU Client, and Cloud Storage need to be installed manually on the VM. This

option can be complex and time-consuming to set up and may not be compatible with the existing

code.

Option B: A VM on Compute Engine and 8 GPUs with all dependencies installed manually. This option

is not suitable for the scenario because it requires manual installation of dependencies and may not

be cost-effective. While using 8 GPUs can provide high parallelism and speed for model training, it

also increases the cost and complexity of the environment. Moreover, to use GPUs, the

dependencies such as NVIDIA GPU drivers, CUDA libraries, and deep learning frameworks need to be

installed manually on the VM. This option can be tedious and error-prone to set up and may not be

necessary for the scenario.

Option D: A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries

pre-installed. This option is not suitable for the scenario because it does not leverage GPU

acceleration for model training. While using more powerful CPU machines can provide more

compute resources and memory for model training, it may not be as fast and efficient as using GPU

machines. CPU machines are not optimized for matrix operations and convolutions, which are

common in CNN-based architectures. Moreover, using more powerful CPU machines can also

increase the cost of the environment. This option can be suboptimal and wasteful for the scenario.

Reference:

Deep Learning VM Image documentation

Compute Engine documentation

Cloud TPU documentation

Machine types documentation

GPUs on Compute Engine documentation