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As deep learning {datasets, models, scale of experiments} grow, so do the storage requirements, and we increasingly find ourselves running out of space on our SSDs. I recently added a new 8TB HDD to my workstation to act as a new scratch volume. While adding a disk would be a standard “sysadmin” work, I found the process of handling permissions for a shared research group on a domain-connected Linux machine to be a bit more involved than I expected. So, for the sake of my own documentation and on the off chance that someone else might find it useful, I’ll document the process here. Since I am using Ubuntu 22.04 on this workstation, I used the Ubuntu instrcutions as a starting point and modified them as needed. ...

This term, I really wanted to present this one ICASSP paper I found very interesting, but then I realized that the authors followed up their work with another journal paper (IEEE TIP), so I decided to discuss both. Both of these papers deal with the topic of unsupervised image segmentation: Kanezaki, “Unsupervised Image Segmentation by Backpropagation”, ICASSP 2018 [URL]. Kim et al. “Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering”, IEEE Transactions on Image Processing, 2020 [URL]. Here are the slides I made to present this topic in our reading group: ...

Note: This post was originally written in 2021, but I have since updated it to reflect the latest changes in HuggingFace Accelerate (last update November 2025 using accelerate==1.11.0). For a grad course that recently concluded, the course project required me to train and evaluate a large number of models. Our school’s local SLURM cluster has new GPUs that support fp16, which meant I could take advantage of PyTorch’s Automatic Mixed Precision (AMP) training. And honestly, there is no reason not to use it: we get reduced memory usage, faster training, and all of this without virtually any loss in performance. ...

We recently got an email from our IT department that our workstation OSes will be getting upgraded from Ubuntu 18.04 MATE to Ubuntu 20.04 GNOME. As much as I love MATE and how lightweight it is (LinuxScoop makes wonderful OS overview videos), I also like the “visuals” of GNOME. My personal laptop already runs Ubuntu 20.04 GNOME, so I am excited to have it on my lab workstation as well. However, this OS upgrade also means that we have to backup our workstations since the drives will be wiped. Our research group has a generous storage space allocation on Compute Canada’s Cedar, so storage is not a big issue. The problem is: Cedar’s long-term storage space is a “tape-based backup system”, so there is a strict limit on the number of files we can store there. Therefore, the best strategy is to create tar archives of our data and store those on Cedar. ...

Acoustic filters are used to “produce a desired sound pitch or to attenuate undesired noise”, and find many applications in the real world, from wind instruments (e.g., flutes, trumpets, clarinets, etc.) and mufflers (e.g., engine noise muffler, acoustic earmuffs) to hearing aids. However, designing customized acoustic filters with specific properties remains a challenge, with the current approach(s) being “complicated and unintuitive” due to their iterative trail-and-error based process. While the design space for these acoustic filters has traditionally been limited to simple geometric shapes, the recent strides in additive manufacturing have enabled the fabrication of complex geometries, thus opening up new possibilities for the expanding the design space. Motivated by this, this paper presents Acoustic Voxels, a computational method for the design of acoustic filters, which relies on modular design based on a simple shape primitive (a hollow cube with circular holes on some or all of its faces) to build a complex assembly. Moreover, the modular design enables a “fast and accurate” estimation of a given assembly’s acoustic performance, which in turn allows for optimization of its structure to achieve desired filtering properties. While the paper provides detailed mathematical formulations of the proposed method, in the interest of brevity, the key details are presented in this summary. ...