Building a Deep Learning / Cryptocurrency PC (#4): AI
Among the buzzwords of this past year, two tower above the rest: deep learning and cryptocurrencies. It seems that everyone I know (in tech) wants to learn these things. And guess what — so do I! So much so that I’m building my own computer in order to facilitate that learning.
What follows are my notes-to-self as I build a computer to learn about deep learning and cryptocurrency mining. In the previous installments we discussed assembling the hardware, installing the OS, and setting up a mining operation. In this installment I’ll talk about installing machine learning software and making sure it’s working.
To recap, in case you’re just getting started with this series: my goal in purchasing and building my own PC was to have hardware on hand to run machine learning algorithms on, and bring myself up to speed on the exciting advances happening in AI. With the mining operation up and running, it’s time for some AI!
The two packages we’re going to install are TensorFlow and PyTorch. I’ve heard lots of buzz about these two frameworks and there’s tons of good resources for each. This article will walk through basic installation details on Ubuntu for both, plus instructions on setting up Jupyter notebooks. If you want to just pick one, Awni Hannun provides a great overview of the differences.
Docker
For both TensorFlow and PyTorch, we’re going to install using Docker.
Docker provides a virtualized environment that lets you isolate packages and libraries (or in our case, download pre-configured environments).
Why Docker?
The TensorFlow docs offer four options for installing TensorFlow. My first instinct was to install directly using native pip.
I installed CUDA 9, the latest version, which as of this writing Tensorflow doesn’t support, and got lost in dependency hell trying to downgrade / uninstall / reinstall CUDA. A friend recommended I leave all that nonsense alone and just install Docker.
So that’s my recommendation: install docker and avoid dependency hell.
Step-by-step Docker installation instructions
- Install
docker. Instructions for installing Docker on Ubuntu are here. - Make sure to follow the optional Step 2 instructions on adding yourself to the correct group so as to avoid needing
sudo. - Install
nvidia-docker. By default,dockerdoesn’t support leveraging the NVIDIA GPUs effectively, so to take advantage of that hardware you’ll need to installnvidia-docker.
TensorFlow
TensorFlow is software for machine learning released by the Google Brain team in 2015. It provides a set of tools for specifying training instructions, and then translating those instructions into commands that can be run quickly and take advantage of GPUs.
It’s a powerful piece of software and since it’s release it’s picked up a ton of developer mindshare.
Installation
Assuming you followed the Docker instructions above, you should have nvidia-docker available and working. The next step is to install the correct Docker image.
We want the latest GPU version, so run:
nvidia-docker run -it gcr.io/tensorflow/tensorflow:latest-gpu bash
This command will find the Docker file (remotely or locally), spin it up and put you at a bash prompt. From there, you can validate your installation.
Validating your installation
Start python:
python
Paste this program, provided by the TensorFlow docs:
# Python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
If you see “Hello, TensorFlow!” you’ll know it’s installed correctly.
Jupyter
So we’ve got TensorFlow installed and working, but inputting python commands one line at a time is a terrible way to program. A much better approach to get started is to use a Jupyter notebook.
A Jupyter notebook is a web interface for documents containing code the evaluations of that code, displayed side by side on the page.
The TensorFlow Docker image supports Jupyter notebooks out of the box. You’ll use a similar command to spin up the Docker image with a few tweaks (If the previous Docker image is still running, shut it down with ctrl-c):
nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu
What this command does is tell docker to expose port 8888 (in Docker) on port 8888 (of your local machine). (Port 8888 is the Jupyter notebook’s default port, if you were wondering.)
To make sure that things are working as expected, open up another terminal on your box and run:
curl http://localhost:8888
If your machine is like mine, you won’t see any output from this curl command, but you should see a request come in on the terminal running Docker, something like:
[I 02:05:57.912 NotebookApp] 302 GET / (xxx.xx.x.x) 0.22ms
You can access the Docker URL directly on your box. I use a laptop and prefer to access the PC remotely, so to do that, you’ll need to be on the same wifi network as your Machine Learning PC and get it’s IP.
On your Machine Learning PC, type:
ifconfig
Look for an IP address that starts with 192.168.x.x. On your laptop, you’ll take the URL provided by Docker and replace localhost with this IP. So, if the Machine Learning box’s IP is 192.168.1.1, you would type in your browser something like:
http://192.168.1.1:8888/?token=7f6b36f9d6b15272c76003b8c1cdfcdf306dc52ff310
At this URL, you should see the Jupyter notebook:
Having a Jupyter notebook handy will make it much easier to run through the TensorFlow tutorials.
Pytorch
The other machine learning tool we’re going to install is PyTorch.
PyTorch was released in 2016 by Facebook’s team. It’s still fairly new and changing quickly, but it’s already picked up a lot of steam in the community.
Installation
Let’s install the appropriate pytorch docker image. Run:
nvidia-docker run --rm -ti --ipc=host -p 8888:8888 pytorch/pytorch:latest
This puts you right at a bash prompt.
I like learning via example, and luckily the Pytorch docs provide plenty of examples to learn from. You can run through the tutorials by starting python and copy pasting code, but a Jupyter notebook is so much better. So let’s get that working.
Jupyter
Jupyter doesn’t come standard on the pytorch docker image, but it’s easy to install. In your docker container (that you started above), type:
python3 -m pip install --upgrade pip
python3 -m pip install jupyter
jupyter notebook
When I ran jupyter notebook for the first time, I got the following error:
OSError: [Errno 99] Cannot assign requested address
To fix this, I had to provide an explicit IP and allow root (hat tip to this comment):
jupyter notebook --allow-root --ip=0.0.0.0
Validating the Jupyter notebook
From another terminal, run curl:
curl http://localhost:8888
And make sure you see logs appear in Docker. If that works, try accessing in the browser at http://192.168.x.xx:8888.
Saving the Jupyter notebook
IMPORTANT: If you exit docker now, you’ll lose the installation of Jupyter you just performed. You need to commit those changes as (outlined in this article) if you want to avoid installing every time you spin up the container.
First, in another terminal, get the name of the container:
docker ps -l
This should give you the most recently created container, which should be PyTorch. Then, commit with:
docker commit <CONTAINER_NAME> pytorch
Refer to our newly named container with:
nvidia-docker run --rm -ti --ipc=host -p 8888:8888 pytorch
And start up your notebook:
jupyter notebook --allow-root --ip=0.0.0.0
If that works, great! Try running through one or two of the tutorials to make sure everything’s working.
Next steps
At this point, you should have both PyTorch and TensorFlow at your disposal.
If you’ve made it this far in the series, congratulations! You built a computer, installed an operating system, began mining cryptocurrencies, and set yourself up to begin training computers to do your bidding. You deserve a pat on the back!
Where to go from here, you may ask? First, I’d encourage you to subscribe:
I’m going to continue blogging my adventures learning this stuff, and I’d love to share it with you.
If you want an immediate next step, I’d recommend Andrew Ng’s course. It’s a deep and thorough introduction to the field.
Finally, I’ve been collecting various machine learning links and resources to work through once I have a base of knowledge. Some of these may come in handy for you! (I haven’t gone through all of these so I can’t vouch for them - feel free to recommend others in the comments).
https://www.reddit.com/r/MachineLearning/comments/7nrzhn/d_results_from_best_of_machine_learning_2017/?st=JBZ1N05J&sh=aa234160
http://www.wildml.com/2017/12/ai-and-deep-learning-in-2017-a-year-in-review/
https://explosion.ai/blog/prodigy-annotation-tool-active-learning
http://blog.kaggle.com/2017/09/11/how-can-i-find-a-dataset-on-kaggle/
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
https://www.wired.com/story/when-websites-design-themselves/
https://chatbotsmagazine.com/contextual-chat-bots-with-tensorflow-4391749d0077
https://medium.com/machine-learning-for-humans/supervised-learning-740383a2feab
https://medium.com/intuitionmachine/the-brute-force-method-of-deep-learning-innovation-58b497323ae5
https://github.com/rhdeck/bostonai/blob/master/README.md
http://nicodjimenez.github.io/2017/10/08/tensorflow.html
http://blog.kaggle.com/2017/11/27/introduction-to-neural-networks/
http://blog.kaggle.com/2017/12/06/introduction-to-neural-networks-2/