Why building your own deep learning PC is 10x cheaper than Amazon

If you’ve used, or are considering, AWS/Azure/GCloud for Machine Learning, you know how crazy expensive GPU time is. And turning machines on and off is a major disruption to your workflow. There’s a better way. Just build your own Deep Learning Computer. It’s 10x cheaper and also easier to use.

Building an expandable Deep Learning Computer w/ 1 top-end GPU only costs $3K of computer parts before tax. You’ll be able to drop the price to about $2k by using cheaper components, which is covered in the next post.
Building is 10x cheaper than renting on AWS / EC2 and is just as performant

Assuming your 1 GPU machine depreciates to $0 in 3 years (very conservative), the chart below shows that if you use it for up to 1 year, it’ll be 10x cheaper, including costs for electricity. Amazon discounts pricing if you have a multi-year contract, so the advantage is 4–6x for multi-year contracts. If you are shelling out tens of thousands of dollars for a multi-year contract, you should seriously consider building at 4–6x less money. The math gets more favorable for the 4 GPU version at 21x cheaper within 1 year!
Cost comparisons for building your own computer versus renting from AWS. 1 GPU builds are 4–10x cheaper and 4 GPU builds are 9–21x cheaper, depending on how long you use the computer. AWS pricing includes discounts for full year and 3 year leases (35%, 60%). Power consumption assumed at $0.20 / kWh, and 1 GPU machine consumes 1 kW / h and 4 GPU machine consumes 2 kW / h. Depreciation is conservatively estimated at linear w/ full depletion in 3 years. Additional GPUs at $700 each, before tax.

There are some draw backs, such as slower download speed to your machine because it’s not on the backbone, static IP is required to access it away from your house, you may want to refresh the GPUs in a couple of years, but the cost savings is so ridiculous it’s still worth it.

If you’re thinking of using the 2080 Ti for your Deep Learning Computer, it’s $500 more and still 4-9x cheaper for a 1 GPU machine.
Cloud GPU machines are expensive at $3 / hour and you have to pay even when you’re not using the machine.

The reason for this dramatic cost discrepancy is that Amazon Web Services EC2 (or Google Cloud or Microsoft Azure) is expensive for GPUs at $3 / hour or about $2100 / month. At Stanford, I used it for my Semantic Segmentation project and my bill was $1,000. I’ve also tried Google Cloud for a project and my bill was $1,800. This is with me carefully monitoring usage and turning off machines when not in use — major pain in the butt!

Even when you shut your machine down, you still have to pay storage for the machine at $0.10 per GB per month, so I got charged a hundred dollars / month just to keep my data around.

The machinene I built costs $3k and has the parts shown below. There’s one 1080 Ti GPU to start (you can just as easily use the new 2080 Ti for Machine Learning at $500 more — just be careful to get one with a blower fan design), a 12 Core CPU, 64GB RAM, and 1TB M.2 SSD. You can add three more GPUs easily for a total of four.

Read More at:   https://medium.com/the-mission/why-building-your-own-deep-learning-computer-is-10x-cheaper-than-aws-b1c91b55ce8c

Machine Learning with Decision Trees

Machine Learning with Decision Trees

Introduction

This paper shows you how to get started with machine learning by applying decision trees using Python on an established dataset. The code used in this article is available on Github. A popular library for creating decision trees is the standard scikit — learn and with this library you can get your first machine learning model running with just a few lines of computer code. In subsequent articles you will apply the SparkML library for machine learning.

Decision trees have influenced the development of machine learning algorithms, including Classification and Regression Tree (CART) models. The divide and conquer approach has attracted many to use them successfully.
A tree-like model of decisions is drawn that can be visually presented and saved to file, both in image form or in pseudo-code form.
A decision tree is drawn like an upside-down tree. We start from the root node, then split the nodes at each level until we reach leaf nodes which represent outcomes or decisions. At each of the internal nodes a decision is taken which then leads to further nodes.

Model From Iris Data

Figure 1 shows a decision tree for the famous Iris dataset. This dataset is available for download from the UCI website which has a list of hundreds of datasets for machine learning applications.

read more at:   https://dzone.com/articles/machine-learning-with-decision-trees-1

Posted by Alan, Tendron Systems Ltd