How do you implement data parallelism in model training for resource-constrained environments

Question

Can you tell me how to implement data parallelism in model training for resource-constrained environments using Python programming?

Answer 1

In order to implement data parallelism in resource-constrained environments, you can use TensorFlow’s tf.distribute.MirroredStrategy, which distributes batches across multiple GPUs to optimize memory usage.

Below is the code explaining the same:

In the code above, we are using tf.distribute.MirroredStrategy() manages data replication across GPUs, Scope ensures that model variables are mirrored across devices, and Batching splits each batch across GPUs, optimizing resource use.

Hence, by using the above technique, you can implement data parallelism in model training for resource-constrained environments.