Pytorch

Photo Credit Introduction Model interpretability is crucial if we want to use AI models to make high-stake decisions (e.g., making medical diagnoses, preventing suicides, etc.). In NLP, one common way to get interpretability is to extract information from the trained models. For example, some use gradient-based input attribution techniques, some perturb the input to get explanations, and some use influence functions to find the most influential training examples to this particular input sequence. Another way is to make the model intrinsically explainable (e.g., a decision tree). ...

Photo Credit (This post an expansion of this Kaggle post.) My Solution Thanks to Kaggle, Manchester City F.C., and Google Research for this fantastic competition. Working on this competition was the most fun I’ve had for a while. The tl;dr version of my solution is that I used an MLP model to stochastically imitate WeKick’s agents, with some rules to help it navigate in unfamiliar waters. Why this Approach After I got the GCP coupon, I looked at the competition timeline and thought that there is no way I can train a competitive RL agent from scratch in less than two weeks. I had to find some way to cut the training time shorter. ...

Photo Credit PyTorch Lightning reached 1.0.0 in October 2020. I wasn’t fully satisfied with the flexibility of its API, so I continued to use my pytorch-helper-bot. This has changed since the 1.0.0 release. Now I use PyTorch Lightning to develop training code that supports both single and multi-GPU training. However, one thing that bugged me is that the logging doesn’t work as expected when I set the number of gradient accumulation batches larger than one. The steps recorded in the training loop is still the raw step number, but those recorded in the validation is divided by the number of gradient accumulation batches. The training loop will be flooded with warnings of inconsistent steps being recorded. And it’ll be harder for you to compare the training and validation losses without the same step scale. ...

Photo Credit This is a short post describing how to use half precision in TorchScript. This can speed up models that were trained using mixed precision in PyTorch (using Apex Amps), and also some of the model trained using full precision (with some potential degradation of accuracy). TorchScript is a way to create serializable and optimizable models from PyTorch code. Any TorchScript program can be saved from a Python process and loaded in a process where there is no Python dependency. source ...

Photo Credit Introduction Self-supervised learning made transfer learning possible in NLP [1] (by using language modeling as the pre-training task) and has started to show some potential in CV as well [2, 3, 4]. They make the downstream tasks more label efficient, that is, requires fewer labeled examples to achieve good prediction accuracies. In CV, we are already quite familiar with transfer learning from models pre-trained on the labeled Imagenet dataset. However, if the dataset used in the downstream task is significantly different from the Imagenet, transfer learning/fine-tuning usually would not be very helpful. ...