Docker

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. ...

Photo Credit Why Use Mutli-Stage Build? Starting from Docker 17.05, users can utilize this new “multi-stage build” feature [1] to simplify their workflow and make the final Docker images smaller. It basically streamlines the “Builder pattern”, which means using a “builder” image to build the binary files, and copying those binary files to another runtime/production image. Despite being an interpreted programming language, many of Python libraries, especially the ones doing scientific computing and machine learning, are built upon pieces written in compiled languages (mostly C/C++). Therefore, the “Builder pattern” can still be applied. ...

A_Different_Perspective from Pixabay RAPIDS RAPIDS is a collection of Python libraries from NVIDIA that enables the users to do their data science pipelines entirely on GPUs. The two main components are cuDF and cuML. The cuDF library provides Pandas-like data frames, and cuML mimics scikit-learn. There’s also a cuGRAPH graph analytics library that have been introduced in the latest release (0.6 on March 28). The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. RAPIDS is incubated by NVIDIA® based on years of accelerated data science experience. RAPIDS relies on NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. ...

Photo Credit R is awesome. In my opinion it’s the best (free) tool for telling great stories with data. My first post on Medium was about R. Although what I wrote here mostly involves Python, I still try to get back to R from time to time. I briefly mentioned my preferred R setup in this previous post “Analyzing Tweets with R” (in “R tips” section), which includes _Microsoft R Open _(MRO) and the checkpoint package. Unfortunately, checkpoint doesn’t work well with RStudio, and some weird issues with MRO become more and more annoying to me. Therefore I decided to find a new setup that can work more smoothly and reliably. After some trial and error, here is a configuration that I ended up most satisfied with: ...

Photo Credit [The 2nd YouTube-8M Video Understanding Challenge](http://The 2nd YouTube-8M Video Understanding Challenge) has just finished. Google generously handed out $300 Google Cloud Platform(GCP) credits to the first 200 eligible people, and I was lucky enough to be one of them. I wouldn’t be able to participate in this challenge at a higher level otherwise. My local hardware can barely handle the size of the dataset and is not strong enough to handle the size of the model. The least I can do to return the favor is to write a short tutorial on how to set up deep-learning-ready VMs on GCP and about some tips that I’ve learned. ...