Googlielmo's blog

I hope you are going to attend the 8th edition of Big Things 2019 , the data and AI conference which will happen in Madrid (Spain) on November 20th and 21st. This conference started in 2012 and this year changed its name from Big Data Spain to Big Things, as it became focused not only on Big Data, but also on whatever is related to AI. Among the speakers this year there will be big names such as Cassie Kozyrkov, Alberto Cairo, Jesse Anderson, Michael Armbrust, Suneel Marthi, Paco Nathan and many others. My talk will be on Thursday 21st at 1:55 PM local time. I am going to give an update on importing and re-training Keras / TensorFlow models in DL4J and Apache Spark . It is a follow-up of some of the topics covered in my book , considering changes related to new releases of DL4J, Keras and TensorFlow since it has been published in January this year. Please stop by if you are going to attend my talk and the conference. I really appreciated the conversations about Deep Learning

Colab is the Google's platform for working with Python notebooks and practice Deep Learning using different frameworks. It is a powerful platform, there is availability of GPUs or TPUs, it allows to use your Google Drive space for notebooks and data, has a friendly user interface and lots of useful features, but in order to install/update Python packages, it comes by default only with pip and no availability for conda . If you need to import a Python package which is available in Anaconda , but not in PyPi you need to install Anaconda or Miniconda yourself from a notebook. In this post I am explaining the simple steps to do it. Anaconda installation Create your notebook and from a code cell download the Anaconda installer: !wget -c https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh This is the version that works fine for me. I have tried also with the latest release 2019.10, but the configuration then would have extra complexity. Now you need to make the do

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez  who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You