Predicting Point Spread and Over/Under: Using Multiple GPUs in TensorFlow for Sports Gambling | YourCompany

Using Multiple GPUs for Training Deep Learning Models in TensorFlow

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Question

You are a machine learning specialist working for a sports gambling company where you are responsible for building a machine learning model to predict the point spread and over/under of NCAA and NFL games.

You have built your custom deep learning model using TensorFlow in SageMaker.

You have attempted to train your model on a single GPU, but you have noticed that the amount of game data you need to train with exceeds the single GPU capacity. How can you change your machine learning code to get it to use multiple GPUs with the least amount of effort on your part?

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B. C. D.

Answer: C.

Option A is incorrect.

The Factorization Machines algorithm is used for classification and regression problems, not deep learning predictions.

Option B is incorrect.

Rewriting your model in PySpark would require more work compared to using the Horovod framework.

Option C is correct.

Using the Horovod distributed deep learning training framework for TensorFlow allows you to distribute your training across many GPUs in parallel easily.

Option D is incorrect.

Rewriting your code to use the DeepAR algorithm would require more work compared to using the Horovod framework.

Reference:

Please see the GitHub page titled Horovod (https://github.com/horovod/horovod), the Amazon SageMaker developer guide titled Use Amazon SageMaker built-in algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html), and the Medium article titled 3 Methods for Parallelization in Spark (https://towardsdatascience.com/3-methods-for-parallelization-in-spark-6a1a4333b473)

The correct answer is C. Add Horovod to your code and use its distributed deep learning training framework for TensorFlow.

Explanation: When training a deep learning model on large datasets, it can take a lot of time and resources to train on a single GPU. One solution to this problem is to distribute the training across multiple GPUs. To do this, you need to use a distributed training framework.

Horovod is a popular distributed deep learning training framework that can be used with TensorFlow. It allows you to train deep learning models on multiple GPUs with minimal code changes. It uses a technique called ring-allreduce, which allows for efficient communication between GPUs during training.

To use Horovod, you first need to install it on your SageMaker instance. You can do this by running the following command in a terminal:

pip install horovod

Next, you need to modify your TensorFlow code to use Horovod. Here is an example of how to do this:

python
import tensorflow as tf import horovod.tensorflow as hvd  # Initialize Horovod hvd.init()  # Pin GPU to be used to process local rank (one GPU per process) config = tf.ConfigProto() config.gpu_options.visible_device_list = str(hvd.local_rank()) tf.Session(config=config)  # Define your TensorFlow model model = tf.keras.Sequential([...])  # Wrap your optimizer with Horovod opt = tf.keras.optimizers.Adam(0.001 * hvd.size())  # Wrap your model with Horovod model = hvd.DistributedOptimizer(opt).minimize(loss)  # Train your model model.fit([...])

In this example, we first import Horovod and initialize it. We then pin each GPU to a specific process using TensorFlow's ConfigProto. We define our TensorFlow model as usual, but we wrap our optimizer and model with Horovod using DistributedOptimizer. Finally, we train our model as usual with fit.

By using Horovod, you can distribute the training of your deep learning model across multiple GPUs, allowing you to train on much larger datasets in a reasonable amount of time.

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