Tip

Photo Credit Motivation Julia is a promising new language for scientific computing and data science. I’ve demonstrated that doing whole work masking in Julia can be a lot faster (up to 100x) than in Python in this post. The secret of Julia’s speed is from its use of JIT compilers (rather than interpreters used by R and Python). However, this design also impedes Julia’s ambition as a general-purpose language since ten seconds of precompiling time for a simple script is unacceptable for most use cases. ...

Photo Credit Streamlit an increasingly popular tool that allows Python developers to turn data scripts into interactive web applications in a few lines of code. I recently developed and deployed a semantic search app for news articles in Chinese, and I made a mistake not caching the model loading code. The performance was abysmal, and the memory footprint was huge for a TinyBERT-4L model (had to allocate 1GB of memory for the app). ...

Photo Credit Motivation I recently came across this new image data augmentation technique called SnapMix. It looks like a very sensible improvement over CutMix, so I was eager to give it a try. The SnapMix author provides a PyTorch implementation. I made some adjustments to improve the numeric stability and converted it to a callback in PyTorch Lightning. I encountered one major obstacle during the process — SnapMix uses Class Activation Mapping(CAM) to calculate an augmented example’s label weights. It requires access to the final linear classifier’s weight and the model activations before the pooling operation. Some PyTorch pre-trained CV models do implement methods to access these two things, but the namings are inconsistent. We need a unified API to do this. ...

Photo Credit Introduction Recently I was asked this question (paraphrasing): I have a small image dataset that I want to train on Google Colab and its free TPU. Is there a way to do that without having to upload the dataset as TFRecord files to Cloud Storage? First of all, if your dataset is small, I’d say training on GPU wouldn’t be much slower than on TPU. But they were adamant that they wanted to see how fast training on TPU can be. That’s fine, and the answer is yes. There is a way to do that. ...

Photo Credit Introduction You just finished developing your prototype in a Conda environment, and you are eager to share it with stakeholders, who may not have the required knowledge to recreate the environment to run your model on their end. Docker is a great tool that can help in this kind of scenario (p.s: it can utilize GPU via nvidia-docker). Just create a Docker image and share it with the stakeholders, and your model will run on their device the same way it runs on yours. ...