In this post I present some Multi-GPU scaling tests running TensorFlow on a very nice system with 8 1080Ti GPU’s. I use the Docker Workstation setup that I have recently written about. The job I ran for this testing was the “Billion Words Benchmark” using an LSTM model. Results were very good and better than expected.
The Intel CPU flaw and the Meltdown and Spectre security exploits are causing a lot of concern. There is a possibility of application slowdown from the kernel patches to mitigate the exploits. This slowdown concern is a concern for GPU accelerated application because of the systems calls they require for moving data between CPU and GPU memory space. I did some testing on a couple of large Tensorflow and Caffe machine learning jobs along with the creation of a LMDA database from 1.3 million images.
In this post I’ll be working up, analyzing, visualizing, and doing Gradient Descent for Linear Regression. It’s a Jupyter notebook with all the code for plots and functions in Python available on my github account.
In Part 3 of this series on Linear Regression I will go into more detail about the Model and Cost function. Including several graphs that will hopefully give insight into the their nature and serve as a reference for developing algorithms in the next post.
In Part 2 of this series on Linear Regression I will pull a data-set of house sale prices and “features” from Kaggle and explore the data in a Jupyter notebook with pandas and seaborn. We will extract a good subset of data to use for our example analysis of the linear regression algorithms.
Linear regression could possibly be considered the “Hello World” problem of Machine Learning. It’s implementation touches on many of the fundamental ideas and problems in this field. I’ll give you some guidance for understanding and implementation of this fundamental idea.
This is the start of a series of posts on Machine Learning and Data Science. I’ll be exploring the algorithms and tools of Machine Learning and Data Science. It will be tutorials, guides, how-to, reviews and “real world” application. The post will be done using Juypter notebooks and the notebooks will be available on GitHub.
I’ve been doing this series of posts about setting up Docker for your desktop system, so why not literally add containers to your desktop! The way we have Docker configured, containers are the same as other applications you run. In this post I’ll show you how to add icons and menu items to launch containers.
Docker can be complex but for use on single-user-workstation you can get a lot done with just a few commands. This post will go through some commands to manage your images and containers. We will also go through the process of building a docker image for CUDA development that includes OpenGl support.
A few weeks ago I wrote a blog post titled Should You Learn to Program with Python. If you read that and decided the answer is yes then this post is for you.
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