LakeTide in Silicon Valley

In late October parts of the LakeTide team went to Silicon Valley for an extended field trip. The main event for the week was to attend the inaugural “Omnisci Converge” event. Omnisci is our go-to platform when it comes super high performance analysis and...

LakeTide @Øredev

Did you ever wonder about the fundamentals of Deep Learning? Listen in to this inspiring presentation that Robert delivered at Øredev. Creativity can not be valued enough when it comes to applied data...

LakeTide @ Data Innovation Summit 2019

On March 19-20 Data Innovation Summit took place at Kistamässan. As always, LakeTide took a big part in making this years edition the best so far. With close to 2000 attendees, speakers, partners and exhibitors the event was bigger than ever before. With 8 dedicated...

Julia vs R vs Python

Hi everyone! It’s been over a year since I first blogged about Julia and why its mathematical syntax, metaprogramming abilities, and blazing speed make it an awesome language for data scientists. In this post I want to follow up by comparing it directly with R...

Custom networks in MXNet on Julia – part 2

Hi, Albin here again! Part of the work I do at LakeTide is making our machine learning models more computationally efficient. For instance, sometimes the neural networks we develop end up being big enough that they don’t fit on GPUs anymore. Other times we just...

Setting up GPU passthrough with KVM on Fedora

In a recent LakeTide project, we wished to emulate a 12-server rack by setting up VMs on our beefy local workstations. The aim was to include one VM with 1 GTX 1080 and another with 4 GTX 1080s. Ultimately, the combination of Fedora, KVM, and nvidia-docker made...

DC/OS and the Mesos scheduler

At LakeTide, we constantly evaluate technologies that can deploy computation-heavy code to resource clusters and DC/OS is one of the heavyweight contenders in the container orchestration space right now. Though it has a smaller community than Docker, Kubernetes,...

Custom networks in MXNet on Julia – part 1

  This is a small example of how to build a MNIST classifier with MXNet and Julia using executors with customized optimizer and accuracy functions. MXNet is a deep learning framework that is very well suited for parallel computations over several GPUs, i.e. it’s a...

LakeTide @ Nordic Data Science Summit

A few weeks ago, the inaugural Nordic Data Science Summit (NDSS) took place in Stockholm. With more than 300 attendees, this “first” conference was jam-packed with passionate smart people, interesting sessions, and engaged partners, providing a very...