You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?
Configuring an n1-standard-4 VM with 4 NVIDIA P100 GPUs and using MultiWorkerMirroredStrategy is the most suitable option. NVIDIA P100 GPUs provide significant computational power, which helps in reducing model training time. MultiWorkerMirroredStrategy enables synchronous training across multiple GPUs, which facilitates better workload distribution and makes debugging more straightforward. This setup balances the need for efficient training and ease of debugging.
Given the need to balance ease of debugging and reduce training time for complex models in TensorFlow, option D - "Configure an n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model" appears to be more suitable. This setup utilizes NVIDIA P100 GPUs for computational power and employs MultiWorkerMirroredStrategy, which can distribute the workload across GPUs efficiently, potentially reducing training time while maintaining a relatively straightforward environment for debugging.
My choice is D. While TPUs offer faster training, they can be less convenient for debugging due to limitations in tooling and visualization, such as the lack of support for some debuggers and limited visualization options. Comparing options C and D, MultiWorkerMirroredStrategy uses synchronous distributed training across multiple workers, making it easier to inspect intermediate states and variables during debugging. In contrast, ParameterServerStraregv utilizes asynchronous multi-machine training, which can be less intuitive to debug. However, it's important to note that ParameterServerStraregv might be more efficient for training extremely large models. Therefore, considering the specific need for ease of debugging in this scenario, MultiWorkerMirroredStrategy appears to be the more suitable choice.
the need to balance ease of debugging and reduce training time
Debugging Ease: SSHing into a VM provides a familiar environment for researchers to use familiar debugging tools within the VM for their complex TensorFlow models. This maintains ease of debugging compared to TPUs which require special considerations. Faster Training: Utilizing 4 NVIDIA P100 GPUs within the VM leverages parallel processing capabilities to significantly accelerate training compared to a CPU-only VM.
D. Cannot be B, because node architecture make it difficult to debug: https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu-node-arch While TPUs are faster than GPUs for certain scenarios, and never slower, they are less easy to debug. Parallelizing the training across different workers (GPUs) using MultiWorkerMirroredStrategy makes most sense to me.
It says "state-of-art" and TPU is more recent than GPU. No need to log using Cloud Shell into VM and there's no mention about cost. So TPU + SSH directly into VM could be the choice.
Option D Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model. is indeed a correct answer. MultiWorkerMirroredStrategy: This strategy allows you to distribute your training process across multiple machines (in this case, the 4 NVIDIA P100 GPUs) while maintaining synchronization between them. NVIDIA P100 GPUs: These high-performance GPUs are well-suited for computationally intensive tasks like deep learning model training.