Anaconda Python environment

Training machine learning models is spend up greatly by the parallel processing of GPUs. To take advantage of the parallel processing of GPUs in machine learning tool kits such as Pytorch we need to use versions compiled with CUDA. Anaconda Python installs prerequisite CUDA drivers with Pytorch versions simplifying installation. We can search for packages

Docker interpreter in IDE

We put a development environment for training AI models in a Docker container for LearnIoTAI. The Python interpreter with AI packages such as Tensorflow and Pytorch is in the Docker container. To use the Python interpreter inside the Docker container through an Integrated Development Environment (IDE) we need to select the containerized Python interpreter from

Running GPU from Docker containers

Training predictive models, especially neural networks with many parameters takes lots of computing power. To use a GPU the container must be configured to access the GPU from the container and contain drivers and libraries. The container in https://github.com/learniotai/iotai_container_env is configured to use Nvidia GPUs. Currently Tensorflow and Pytorch are built to use only Nvidia

Environment container for training and deploying artificial intelligence (AI)

Containers Containers are software units that package up an application and all of it’s dependencies. The containerized application can portably run in different computing environments. Containers and ran from immutable images. Container properties include: Portable environments. Consistent dependency packages and versions. Currently based on Docker container technology. Build from base images for GPU https://hub.docker.com/r/nvidia/cuda Lighter

Managing Docker images and containers

Docker images take space and we need to remove unused images which will have the tag none. We can find untagged images with docker images | grep none none none 91aea7344ea7 11 minutes ago 2.88GB none none 186d3c3b9d72 2 weeks ago 2.88GB … Remove unused images without containers of that image. docker image rm 91aea7344ea7

Computing environment for Artificial Intelligence (AI) model development

Developing AI includes collecting data, training models, and deploying models. The tools for AI are primarily developed on Ubuntu Linux. Though most of the software for AI development can be used from packaged containers based on Ubuntu Linux. A few pieces of software are needed to run the containers. The basic operating system (OS) configurations

Edge computing – AI on IoT at fingertips

Putting artificial intelligence (AI) on internet of things (IoT) devices puts AI at users’ fingertips. This processes data close to where it’s produced reducing latency (lag), doesn’t require continuous internet connection, reduces energy use, and keeps data private since it doesn’t require transferring data off the device. An example device is a smartphone and an example

Pull and track source code

A first step in learning to program AI on IoT is pulling down and tracking source code changes. Setup Github for pulling down source code examples and track your code own changes. Create a Github account Go to https://github.com/ . and click sign up at the top of the page. Then fill in the form

LearnIoTAI (Learn Internet of Things and Artificial Intelligence)

LearnIoTAI is about getting you going on Artificial Intelligence (AI) of the Internet of Things (IoT). AI and IoT are both fast growing fields. The AI market has a 33.1% Compound annual growth rate  (CAGR). The IoT market has a 24.9% CAGR. The two fields are converging to put AI on the computing edge of