I recently went to Sheh Zaidi‘s brilliant introduction to Equivariance and Spherical Harmonics and I thought it would be useful to cement my understanding of it with a practical example. In this blog post I’m going to start with serotonin in two coordinate frames, and build a small equivariant neural network that featurises it.
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Handy LaTeX syntax I Googled over the years
In an attempt to ease the transition from Word to LaTeX for some of my colleagues (*cough* Alex *cough*) this blog post covers some LaTeX tricks I use most frequently when preparing manuscripts. It’s pitched at someone who is already familiar with the basic syntax of paragraphs, figures and tables.
Continue readingSome useful pandas functions
Pandas is one of the most used packages for data analysis in python. The library provides functionalities that allow to perfrom complex data manipulation operations in a few lines of code. However, as the number of functions provided is huge, it is impossible to keep track of all of them. More often than we’d like to admit we end up wiriting lines and lines of code only to later on discover that the same operation can be performed with a single pandas function.
To help avoiding this problem in the future, I will run through some of my favourite pandas functions and demonstrate their use on an example data set containing information of crystal structures in the PDB.
Continue readingLet your library design blosum
During the lead optimisation stage of the drug discovery pipeline, we might wish to make mutations to an initially identified binding antibody to improve properties such as developability, immunogenicity, and affinity.
There are many ways we could go about suggesting these mutations including using Large Language Models e.g. ESM and AbLang, or Inverse Folding methods e.g. ProteinMPNN and AntiFold. However, some of our recent work (soon to be pre-printed) has shown that classical non-Machine Learning approaches, such as BLOSUM, could also be worth considering at this stage.
Continue readingConverting pandas DataFrames into Publication-Ready Tables
Analysing, comparing and communicating the predictive performance of machine learning models is a crucial component of any empirical research effort. Pandas, a staple in the Python data analysis stack, not only helps with the data wrangling itself, but also provides efficient solutions for data presentation. Two of its lesser-known yet incredibly useful features are df.to_markdown() and df.to_latex(), which allow for a seamless transition from DataFrames to publication-ready tables. Here’s how you can use them!
What the heck are TPUs?
I recently became curious about TPUs, a specialised hardware for training Machine- and Deep-Learning models, where TPU stands for Tensor Processing Unit. This fancy chip can provide very high gains for anyone aiming to perform really massive parallelisation of AI tasks such as training, fine-tuning, and inference.
In this blog post, I will touch on what a TPU is, why it could be useful for AI applications when compared to GPUs and briefly discuss associated opportunity costs.
What’s a TPU?
Continue readingDeploying a Flask app part II: using an Apache reverse proxy
I recently wrote about serving a Flask web application on localhost using gunicorn. This is sufficient to get an app up and running locally using a production-ready WSGI server, but we still need to add a HTTP proxy server in front to securely handle HTTP requests coming from external clients. Here we’ll cover configuring a simple reverse proxy using the Apache web server, though of course you could do the same with another HTTP server such as nginx.
Continue readingUnderstanding GPU parallelization in deep learning
Deep learning has proven to be the season’s favourite for biology: every other week, an interesting biological problem is solved by clever application of neural networks. Yet, as more challenges get cracked, modern research shifts more and more in the direction of larger models — meaning that increasing computational resources are required for training. Unsurprisingly, NVIDIA, the main manufacturer of GPUs, experienced a significant jump in their stock price earlier this year.
Access to compute is not enough to train good neural networks. As soon as multiple cards enter into play, researchers need to use a completely different paradigm where data and model weights are distributed across different devices — and sometimes even different computers. Though these tools start to be crucial for successful computational biology research, they are generally unknown to researchers. Hence, in this blogpost, I would like to provide a really brief introduction to multi-GPU training.
Continue readingSSH, the boss-fight level: Jupyter notebooks from compute nodes
Secure shell (SSH) is an essential tool for remote operations. However, not everything with it is smooth-sailing. Especially, when you want to do things like reverse–port-forwarding via a proxy-hump or two a Jupyter notebook to your local machine from a compute node on a no-home container . Even if it sounds less plausible than the exploits on Mr Robot, it actually can work and requires zero social-engineering or sneaking in server rooms to install Raspberry Pis while using a baseball cap as a disguise.
Continue readingDeploying a Flask app part I: the gunicorn WSGI server
Last year I wrote a post about deploying Flask apps with Apache/mod_wsgi when your app’s dependencies are installed in a conda environment. The year before, in the dark times, I wrote a post about the black magic invocations required to get multiple apps running stably using mod_wsgi. I’ve since moved away from mod_wsgi entirely and switched to running Flask apps from containers using the gunicorn WSGI server behind an Apache reverse proxy, which has made life immeasurably easier. In this post we’ll cover running a Flask app on localhost using gunicorn; in Part II we’ll run our app as a service using Singularity and deploy it to production using Apache as a HTTP proxy server.
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