What are the best practices for implementing parallelism in machine learning?
An example of an AI chatbot conversation about best practices for parallelism in machine learning.
What are the best practices for implementing parallelism in machine learning?
When implementing parallelism in ML, consider data parallelism, model parallelism, and efficient resource management.
Can you explain data parallelism and how it differs from model parallelism?
Data parallelism involves splitting the dataset across multiple processors, while model parallelism splits the model itself.
What tools or frameworks can help with parallelism in ML?
Popular tools include TensorFlow, PyTorch, and Dask, which provide built-in support for parallel processing.
How do I decide which parallelism strategy to use for my project?
Evaluate your model size, dataset, and available resources to choose the most suitable parallelism strategy.
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