I think fast.ai has been one of the many exciting developments in the Deep Learning / Machine Learning education space over the past 1.5 years. Fast.ai really champions the top-down approach where the goal is to make Deep Learning more accessible and get you to become productive using these tools to solve a variety of problems without spending years working on a Ph.D.
I've had some false starts with Deep Learning. I literally have stacks of books, papers and PDFs on the subject and a multitude of unfinished Coursera courses and MOOCs but I feel what's usually missing is how you distill all that knowledge to help you become productive right away.
I had the opportunity to watch several videos from the previous fast.ai MOOC and I really liked the teaching style so I decided to sign up for the second iteration of the MOOC and I must say I haven't been disappointed. Another added benefit is that the course is taught by Jeremy Howard. The next series of posts will highlight and summarize learnings from the class sessions. My goal with this is to help spread fast.ai's message of making start-of-the-art Deep Learning Research / tools more accessible.
This is really powerful stuff and what you want is for people to use these Deep Learning techniques on some of the problems they're working on which may help push the envelope in many ways.
This is really powerful stuff and what you want is for people to use these Deep Learning techniques on some of the problems they're working on which may help push the envelope in many ways.
Hardware Setup
A lot has already been written on why GPUs and specialized hardware are great for Deep Learning so I'll cut to the chase and run you through several setup options you could use for the course
A lot has already been written on why GPUs and specialized hardware are great for Deep Learning so I'll cut to the chase and run you through several setup options you could use for the course
AWS : This is a great guide to set up an AWS instance. A p2.xlarge GPU instance is probably okay for the course and it runs about 90 cents an hour. If your p2 instance limit is 0, you may have to request an instance limit increase through the AWS support center which usually takes a day or two to be approved. Jeremy also set up a fastai Community AMI in the Oregon and N.Virginia regions and a few other regions around the world (Mumbai, Sydney, Ireland) with all the needed software pre-installed.
Once you're done setting up your instance, the next steps would be to get your ssh keys and then finish the ssh config. My current setup is shown below. Notice the config file at ~/.ssh/config and its contents.
The setup allows you to ssh into your AWS instance by using the command ssh fast-ai. Notice that the Hostname is blank, you'll have to fill that in with your instance IPv4 Publib IP which you find on your EC2 dashboard. Do keep in mind that this changes whenever you stop/start your instance. You can either update this manually or write a script to replace the hostname each time.
Also, notice the last line in the config file. LocalForward establishes tunneling between your system and the AWS instance allowing you launch a jupyter instance on the AWS instance and then launch the actual notebook on your local system. Running the command jupyter notebook gives the following output. We can then paste the url in a browser on our system to launch the notebook.
Crestle : is the easiest way to get a GPU enabled notebook. It's built on AWS so you get access to an AMI via ssh. It also has the fast.at datasets loaded in by default and it was built by a former fast.ai student. The setup process is quick compared to AWS and Paperspace
Paperspace: Paperspace is a cloud platform that enables you to spin up a GPU enabled fully-fledged Ubuntu desktop in a browser. They have faster / cheaper GPUs than AWS and you're charged for compute (by the hour) and storage
They also have a fast.ai public template so it's a really quick way to set things up for the course.
Python / Anaconda : I also finally switched my python version from 2.7 to 3.6. This is trivial to do via Anaconda and you can essentially switch between version. The fast.ai pytorch library requires python 3.6.
Personal Workstation : It could make economic sense to build you own Deep Learning workstation. An NVIDIA 1080ti GPU would cost you around $700 or so and you can supercharge with multiple GPU cards. I have box with a 1060 card but I'm still running into some issues setting it up.
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