If you are a professor teaching or a student enrolled in machine learning program or non-technical program with a machine learning hands-on lab becoming a member of the H2O.ai Academic Program will get you free access to non-commercial use of software license for education and research purposes. Since November 2018 H2O.ai (my employer) made its ground-breaking automated machine learning (AutoML) platform Driverless AI available to academia for free.
What Does Driverless AI Do?
H2O.ai defines Driverless AI as
To find out how Driverless AI automates machine learning activities into integral and repeatable workflow seamlessly encompassing feature engineering, model validation and parameter tuning, model selection and ensembling, custom recipes for transformers, models and scorers, and finally model deployment click on the link. Not to forget MLI (Machine Learning Interpretability) module that offers tools for both white and black box model interpretability, model debugging, disparate impact analysis, and what-if (sensetivity) analysis.
H2O.ai Academic Program
- intended use is non-commercial for education and research purposes only and
- person belongs to higher education institution or is a student currently enrolled in a higher education degree program and
- if a student then academic status can be verified by sending a photo of your current student ID to [email protected] (required).
Upon approval H2O.ai will issue a free license for Driverless AI for non-commercial use only. While waiting to be approved apply for access to H2O.ai Community Slack channel here and don’t forget to join #academic).
Driverless AI Installation Options
After receiving a license key follow installation instructions for Mac OS X or Windows 10 Pro (via WSL Ubuntu option is highly preferred) to run Driverless AI on your workstation or laptop. While such approach suffices for small datasets serious problems demand installing and running Driverless AI on modern data center hardware with multiple CPUs and one or several GPUs for best results.
There are several economical cloud providers for such solution with one of them utilized below. For general guidelines and instructions for native DEB installation on Linux Ubuntu see here. Steps below can be tracked back to this documentation.
Paperspace offers robust choice of configurations to provision and run Linux Ubuntu VMs with single GPU (no multi GPU systems available). The pricing appears competitive to suit thrifty academic budget by starting at around $0.50/hour for GPU systems with 30G of memory that should comfortably host Driverless AI. It also features simple streamlined interface to deploy and manage VMs.
Spinning up Linux VM
1. Create Paperspace Account
Start with creating account at paperspace.com:
2. Create a Cloud VM
After successfully creating account proceed to create a cloud VM:
3. Start Adding New Machine
Under Core -> Compute -> Machines on the left select (+) to add new machine:
4. Machine Location
Choose region closer to your location – in my case it was “East Coast (NY2)”:
5. Choose Type Operating System
Scroll down to “Choose OS” and click on “Linux Templates”:
6. Choose OS Version
Keep default Ubuntu 16.04 server image:
7. Pick Machine Type (How Much to Pay)
Scroll down to choose machine profile (keep hourly rate): for VM pick type “P4000” or more expensive machine type with GPU, while for CPU only system pick “C6” or higher (in case this instance type is not enabled instructions to enable it should pop up):
8. Enable Public IP
Scroll down to “Public IP” to enable it while keeping other settings unchanged except maybe for “Storage” and “Auto-Shutdown”. While 50G of storage suffices for many applications if you plan on using larger datasets or create massive number of models increase your storage accordingly: allocate at least 20 times storage as the largest dataset you plan to use. Lastly change auto-shutdown timeout according to your needs:
9. Apply $10.00 Promo Code and Payment
Scroll down to payment to enter credit card information, enter promotion code 5NXWB5R to apply (Paperspace should credit your account $10.00) before finally creating VM with “Create Your Paperspace” button:
10. Creating VM
See system is in “Provisioning” state:
11. Wait for System to Start
Wait until after a minute or two system state changes to “On/Ready” and click on small gear:
12. System Console
System details view with information about VM opens including public IP address assigned to your VM:
13. Notificaiton from Paperspace
Next find email from Paperspace with system password:
With public IP address and password you can ssh (on Mac OS X or Linux) or connect using putty (on Windows) to paperspace VM and install Driverless AI software following steps for vanilla Ubuntu system. This example continues with this install to show all steps in detail.
14. Terminal Access to VM
ssh to Paperspace VM with from Mac OS terminal using Public IP and password as shown in steps 12 and 13 (ssh below is used on Mac OS X – for other OSes adjust accordingly):
15. Change paperspace assigned password (optional):
16. Install core packages (optional):
17. Add support for NVIDIA GPU libraries (CUDA 10):
18. Install other prerequisites and open port Driverless AI listens to:
Installing Driverless AI
19. H2O Download Page
Leave (do not close) ssh terminal for a browser and locate H2O.ai download page. Choose latest version of Driverless AI product:
17. Download Link
Go to Linux (X86) tab and then right-click on the “Download” link for DEB package to copy link location:
18. Back to Terminal Access
Return to ssh terminal session connected to paperspace VM. If session timed out or became inactive repeat step 14.
19. Download and install Driverless AI DEB package:
20. Install Completed
After installer successfully finishes it displays following helpful information:
21. Start Driverless AI
Check that Driverless AI is installed but inactive and then start it and check yet again its status and logs:
22. Web Access
Open browser and enter URL with public IP address like this: http://220.127.116.11:12345 (ignore 127.0.0.1 in screenshot as I was using port forwarding when taking them):
23. License Agreement
Scroll down to accept license agreement:
24. Login to Driverless AI
Driverless AI display login screen – enter credentials h2oai/h2oai:
25. Activate License
Driverless AI prompts to Enter License to activate software license:
26. License Key
Enter Driverless AI license key received by enrolling to H2O.ai Academic Program and press Save:
27. All Done
Now Driverless AI platform is fully enabled to help in your research or studies or both: