What is distributed training in TensorFlow?

What is distributed training in TensorFlow?

Distributed training in TensorFlow allows you to train machine learning models across multiple devices or machines, improving efficiency.

How does it improve training speed?

By parallelizing the training process, distributed training can significantly reduce the time it takes to train large models on large datasets.

What are the different strategies for distributed training?

Common strategies include data parallelism, model parallelism, and hybrid approaches, each suited for different scenarios.

Can I use TensorFlow with cloud services for distributed training?

Yes, TensorFlow integrates well with cloud services like Google Cloud and AWS, allowing for scalable distributed training.

What are some best practices for implementing distributed training?

Best practices include optimizing data pipelines, using appropriate batch sizes, and monitoring resource utilization.