Model Parallelism vs Data Parallelism
Developers should learn and use model parallelism when training or deploying very large neural network models that exceed the memory capacity of a single GPU or TPU, such as transformer-based models with billions of parameters (e meets developers should learn data parallelism when working with computationally intensive tasks on large datasets, such as training machine learning models, processing big data, or running scientific simulations, to reduce execution time and improve scalability. Here's our take.
Model Parallelism
Developers should learn and use model parallelism when training or deploying very large neural network models that exceed the memory capacity of a single GPU or TPU, such as transformer-based models with billions of parameters (e
Model Parallelism
Nice PickDevelopers should learn and use model parallelism when training or deploying very large neural network models that exceed the memory capacity of a single GPU or TPU, such as transformer-based models with billions of parameters (e
Pros
- +g
- +Related to: distributed-training, data-parallelism
Cons
- -Specific tradeoffs depend on your use case
Data Parallelism
Developers should learn data parallelism when working with computationally intensive tasks on large datasets, such as training machine learning models, processing big data, or running scientific simulations, to reduce execution time and improve scalability
Pros
- +It is essential for leveraging modern hardware like GPUs, multi-core CPUs, and distributed clusters, enabling efficient use of resources in applications like deep learning with frameworks like TensorFlow or PyTorch, and data processing with tools like Apache Spark
- +Related to: distributed-computing, gpu-programming
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Model Parallelism if: You want g and can live with specific tradeoffs depend on your use case.
Use Data Parallelism if: You prioritize it is essential for leveraging modern hardware like gpus, multi-core cpus, and distributed clusters, enabling efficient use of resources in applications like deep learning with frameworks like tensorflow or pytorch, and data processing with tools like apache spark over what Model Parallelism offers.
Developers should learn and use model parallelism when training or deploying very large neural network models that exceed the memory capacity of a single GPU or TPU, such as transformer-based models with billions of parameters (e
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