MLOps.community PyTorch for Control Systems and Decision Making // Vincent Moens // #276
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Dec 4, 2024 Vincent Moens, an Applied Machine Learning Research Scientist at Meta and the author behind TorchRL and TensorDict, delves into the fascinating applications of PyTorch in control systems and decision-making. He shares insights on optimizing performance using practical tips, including the nuances of pin memory for CUDA transfers. The discussion covers the pitfalls of in-place tensor modifications and introduces TensorDict as a solution for efficient data handling. Additionally, Vincent emphasizes community collaboration to enhance developer experiences and improve user-friendly APIs in PyTorch.
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PyTorch CUDA Transfer Tip
- Benchmark PyTorch CPU to CUDA transfers.
- Avoid unnecessary .pin_memory() calls for potential speedups.
In-Place Operations in PyTorch
- Avoid in-place tensor operations (e.g., add_ or ReLU with in_place=True).
- Prioritize non-in-place operations for compiler efficiency and potential performance gains.
Module Copies for Efficiency
- Create copies of small, frequently called PyTorch modules.
- Set different attributes (e.g., .eval(), .requires_grad) on copies to avoid repeated overhead.