python-pytorchHow can I optimize a PyTorch model using ROCm on Python?

Optimizing a PyTorch model using ROCm on Python can be accomplished by leveraging the ROCm-enabled PyTorch library. This library allows for the use of ROCm-specific optimizations and provides support for the ROCm-specific APIs.

Example code

import torch
import torch.cuda.rocm as rocm

# Build the model
model = torch.nn.Sequential(
    torch.nn.Linear(10, 20),
    torch.nn.ReLU(),
    torch.nn.Linear(20, 30),
    torch.nn.ReLU(),
    torch.nn.Linear(30, 1)
)

# Move model to ROCm device
model.cuda(rocm.get_device_id())

# Optimize the model
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

The above code snippet builds a PyTorch model and optimizes it using the Adam optimizer. The model is then moved to the ROCm device for optimization.

The ROCm-enabled PyTorch library provides support for the ROCm-specific APIs. This allows for the use of ROCm-specific optimizations, such as using the ROCm-specific tensor cores, which can improve performance.

Helpful links

• ROCm-enabled PyTorch
• ROCm Tensor Core Support

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