Google Professional Machine Learning Engineer Question 335
Single answerGoogle Cloud PlatformYou are designing a machine learning pipeline to train a deep learning model on a large image dataset. The model architecture involves convolutional layers, and training involves performing millions of matrix multiplications. The training process needs to be completed as quickly as possible, and the dataset fits into memory. Which hardware option would be the most appropriate for this task?
- A
CPUs with high core count
- B
Standard GPUs
- C
TPUs
- D
Edge devices with custom accelerators
Show answer and explanation
Correct answer: C
Explanation
For training a deep learning model on a large dataset with convolutional layers, hardware that can perform parallelized matrix multiplications efficiently is critical. While GPUs are a good option, TPUs are specifically optimized for TensorFlow and deep learning workloads, making them the best choice for this scenario. CPUs and edge devices are not suitable for this high-computation, large-scale training task.
- A. Incorrect.
CPUs with high core count are general-purpose processors and are not optimized for the massively parallel operations required for deep learning tasks like matrix multiplications. They are slower compared to GPUs and TPUs for this use case.
- B. Incorrect.
Standard GPUs are optimized for parallel processing and can accelerate deep learning workloads significantly. However, TPUs are specifically designed for TensorFlow-based deep learning tasks and often outperform GPUs in terms of training speed and efficiency for such tasks.
- C. Correct.
TPUs (Tensor Processing Units) are specialized hardware designed for deep learning tasks, particularly for TensorFlow. They excel at performing matrix multiplications and are the most appropriate option for training a convolutional neural network on a large dataset efficiently.
- D. Incorrect.
Edge devices with custom accelerators are usually designed for inference tasks rather than training. They are not suitable for training large-scale models due to lower computational power compared to GPUs or TPUs.