In the previous article, we saw how we automated several manual pieces when training a neural...
In the previous article, we saw how we automated several manual pieces when training a neural network. In this article, we will check the final results and also see how we can do hardware acceleration.
For verifying that the final_bias is correctly optimized, we can print out the new value:
trainer.fit(model, train_dataloaders=dataloader)
print(model.final_bias.data)
Enter fullscreen mode
In the previous article, we went through the optimization loop and passed all three training inputs...
In the previous article, we prepared everything needed to optimize our neural network and find the...
In the previous article, we started with the creation of LSTM cell. In this article we will continue...
In this article, we will explore how to implement an LSTM using PyTorch and Lightning. For more...
Author(s): Aditya Kumar Manethia Originally published on Towards AI. Deep Learning Inference: PyTorch, ONNX, and TensorRT…
Benchmark NVIDIA Apex FusedAdam and FusedLayerNorm against PyTorch, then pair fused kernels with torch.amp for faster Transformer…
