Exam Professional Machine Learning Engineer topic 1 question 103 discussion

Ans D: Check this link https://www.tensorflow.org/guide/gpu_performance_analysis for details on how to Optimize the performance on the multi-GPU single host

When using distributed training with tf.distribute.MirroredStrategy, each GPU processes a slice of the batch. If you keep the batch size constant, each GPU receives a smaller effective batch, which might not fully utilize the computational power of each device. Increasing the batch size allows each GPU to process more data in parallel, which can lead to improved training speed and better resource utilization without modifying your training loop or switching strategies

I will go with A. By using tf.distribute.Strategy.experimental_distribute_dataset we can ensure that the dataset is effectively split across the GPUs, which will help fully utilize the GPUs and achieve faster training times. Increasing the batch size can improve training performance on GPUs by allowing them to process more data in parallel. However, if the dataset is not properly distributed across GPUs, simply increasing the batch size won't lead to improved training times. In fact, using a larger batch size can lead to memory bottlenecks if not handled correctly. The key here is to first ensure proper data distribution before tweaking batch size.

A is the right answer

when using tf.distribute.MirroredStrategy, TensorFlow automatically takes care of distributing the dataset across the available devices (GPUs in this case). To make sure that the data is efficiently distributed across the GPUs, you should increase the global batch size. This ensures that each GPU receives a larger batch of data to process, effectively utilizing the additional computational power. The global batch size is the sum of the batch sizes for all devices. For example, if you had a batch size of 64 for a single GPU, you would set the global batch size to 256 (64 * 4) when using 4 GPUs.

When you distribute the training across multiple GPUs using tf.distribute.MirroredStrategy, the training time may not decrease if the dataset loading and preprocessing become a bottleneck. In this case, option A, distributing the dataset with tf.distribute.Strategy.experimental_distribute_dataset, can help improve the performance.

A. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset When you distribute the training across multiple GPUs using tf.distribute.MirroredStrategy, you need to make sure that the data is also distributed across the GPUs to fully utilize the computational power. By default, the tf.distribute.MirroredStrategy replicates the model and uses synchronous training, but it does not automatically distribute the dataset across the GPUs.

When going from training with a single GPU to multiple GPUs on the same host, ideally you should experience the performance scaling with only the additional overhead of gradient communication and increased host thread utilization. Because of this overhead, you will not have an exact 2x speedup if you move from 1 to 2 GPUs. Try to maximize the batch size, which will lead to higher device utilization and amortize the costs of communication across multiple GPUs. Using the memory profiler helps get a sense of how close your program is to peak memory utilization. Note that while a higher batch size can affect convergence, this is usually outweighed by the performance benefits.

Went with D

If distributing the training across multiple GPUs did not result in a decrease in training time, the issue may be related to the batch size being too small. When using multiple GPUs, each GPU gets a smaller portion of the batch size, which can lead to slower training times due to increased communication overhead. Therefore, increasing the batch size can help utilize the GPUs more efficiently and speed up training.

Answer D

D: it is best https://www.tensorflow.org/guide/distributed_training#use_tfdistributestrategy_with_keras_modelfit Each epoch will then train faster as you add more GPUs. Typically, you would want to increase your batch size as you add more accelerators, C is rule out because of GPU A and B , as reading on https://www.tensorflow.org/guide/distributed_training#use_tfdistributestrategy_with_custom_training_loops To use custom loop , we have call If you are writing a custom training loop, you will need to call a few more methods, see the guide: Start by creating a tf.data.Dataset normally. Use tf.distribute.Strategy.experimental_distribute_dataset to convert a tf.data.Dataset to something that produces "per-replica" values. If you want to https://www.tensorflow.org/api_docs/python/tf/distribute/Strategy

To speed up the training of the deep learning model, increasing the batch size. When using multiple GPUs with tf.distribute.MirroredStrategy, increasing the batch size can help to better utilize the additional GPUs and potentially reduce the training time. This is because larger batch sizes allow each GPU to process more data in parallel, which can help to improve the efficiency of the training process.

TPUs are Google's specialized ASICs designed to dramatically accelerate machine learning workloads. Hence it should be C.

I think it's D

I think its A

B (not sure) - https://www.tensorflow.org/guide/keras/writing_a_training_loop_from_scratch -https://www.tensorflow.org/guide/distributed_training#use_tfdistributestrategy_with_custom_training_loops