Published in Towards Data Science·Jan 12, 2021Member-onlyGetting Started with SSHEverything a data scientist should know to start using SSH — At some point as a data scientist you will need to work a dataset that requires more computational resources (i.e., disk space, CPU memory, number of CPUs or GPUs) than what you have available on your laptop or workstation. When this happens you will need to know how to access…Towards Data Science6 min readTowards Data Science6 min read
Published in Towards Data Science·Sep 7, 2020Member-onlyManaging JupyterLab-based data science projects using Conda (+pip)A collection of “best practices” for the discerning data scientist — This article discusses two approaches for managing JupyterLab-based data science projects using Conda (+pip): a “system-wide” approach where Conda (+pip) are used to manage a single JupyterLab installation that is shared across all projects, and a “project-based” approach where Conda (+pip) are used to manage separate JupyterLab installations for each…Towards Data Science6 min readTowards Data Science6 min read
Published in Towards Data Science·Jun 12, 2020Member-onlyConda + Google ColabA guide to installing Conda when using Google Colab — Conda is the recommended environment and package management solution for a number of popular data science tools including Pandas, Scikit-Learn, PyTorch, NVIDIA Rapids and many others. Conda also dramatically simplifies the process of installing popular deep learning tools like TensorFlow. Google Colab is a free service providing interactive computing resources…Conda4 min readConda4 min read
Published in Towards Data Science·Jun 4, 2020Member-onlyInstalling TensorFlowIt isn’t as difficult as you may have heard — I have seen a growing number of articles floating around suggesting that installing TensorFlow is difficult. …Tensor Flow2 min readTensor Flow2 min read
Published in Towards Data Science·May 14, 2020Member-onlyManaging CUDA dependencies with Conda“Best practices” for managing CUDA dependencies on your next data science project — Transitioning your data science projects from CPU to GPU can seem like a daunting task. In particular, there is quite a bit of unfamiliar additional software, such as NVIDIA CUDA Toolkit, NVIDIA Collective Communications Library (NCCL), and NVIDIA Deep Neural Network Library (cuDNN) to download and install. If you go…Conda8 min readConda8 min read
Published in Towards Data Science·May 7, 2020Member-onlyRendering OpenAI Gym Envs on Binder and Google ColabNotes on solving a tedious (but important) problem — I am currently using my COVID-19 imposed quarantine to expand my deep learning skills by completing the Deep Reinforcement Learning Nanodegree from Udacity. Almost immediately I ran into the tedious problem of getting my simulations to render properly when training on remote servers. In particular, getting OpenAI Gym environments to…Colab5 min readColab5 min read
Published in Towards Data Science·Apr 28, 2020Member-onlyConda (+ pip) and Docker FTW!A solution to the environment and package management problems that plague data science projects. — Enter the whale! As this post focuses on incorporating Docker into an existing Conda (+ pip) workflow, I assume that you are already using Conda (+ pip) to manage environments for your data science projects. …Conda7 min readConda7 min read
Published in Towards Data Science·Apr 27, 2020Member-onlyBuilding a Conda environment for HorovodHow to get started with distributed training of DNNs using Horovod. — What is Horovod? Horovod is an open-source distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. Originally developed by Uber for in house use, Horovod was open sourced a couple of years ago and is now an official Linux Foundation AI (LFAI) project. In this post I describe how I build Conda…Conda6 min readConda6 min read
Published in Towards Data Science·Apr 25, 2020Member-onlyManaging your data science project environments with CondaA minimal set of “best practices” for the discerning data scientist. — Conda “Best Practices” In the this post I detail a minimal set of “best practices” for using Conda to manage project-specific environments that I use in my own data science work. This article assumes a basic familiarity with Conda and focuses on communicating a small set of “best practices” for managing data science…Conda3 min readConda3 min read
Published in Towards Data Science·Apr 25, 2020Member-onlyGetting Started with CondaJust the basics. What is Conda? Why should you use Conda? How do you install Conda? — What is Conda? Conda is an open source package and environment management system that runs on Windows, Mac OS and Linux. Conda can quickly install, run, and update packages and associated dependencies. Conda can create, save, load, and switch between project specific software environments on your local computer.Conda4 min readConda4 min read
