- RT @johnpmayer: #aals2018 Here are my slides and link to audio/video for my 10 min talk at the AALS Section on Law, Tech and Teaching on 1/… 09:29:59, 2018-01-08
- RT @johnpmayer: Are you a law students/law faculty on Reddit? Asking for your help to upvote a post I made about CALI – https://t.co/9FS8H… 09:30:34, 2018-01-08
- RT @kevinokeefe: Legal Aid bracing for possible cut to federal funding https://t.co/UU2T0aMnAy 15:06:52, 2018-01-08
- RT @johnpmayer: "..It’s a sure sign that the speaker has no real background in learning theory and is basically winging it.."
- RT @BoingBoing: Disney's 1998 copyright term extension expires this year and Big Content's lobbyists say they're not going to try for anoth… 15:15:24, 2018-01-08
- RT @ABAJournal: .@NCCULAW and Arizona Summit School of Law found out of compliance with ABA accreditation standards; Appalachian School of… 15:20:13, 2018-01-08
- RT @EJWalters: ?Launch! The Journal of Robotics, Artificial Intelligence & Law (The RAIL) is live on @Fastcase! Press release here: https:… 15:25:36, 2018-01-08
AWS Launches New Deep Learning AMIs for Machine Learning Practitioners
The Conda-based AMI comes pre-installed with Python environments for deep learning created using Conda. Each Conda-based Python environment is configured to include the official pip package of a popular deep learning framework, and its dependencies. Think of it as a fully baked virtual environment ready to run your deep learning code, for example, to train a neural network model. Our step-by-step guide provides instructions on how to activate an environment with the deep learning framework of your choice or swap between environments using simple one-line commands.
But the benefits of the AMI don’t stop there. The environments on the AMI operate as mutually-isolated, self-contained sandboxes. This means when you run your deep learning code inside the sandbox, you get full visibility and control of its run-time environment. You can install a new software package, upgrade an existing package or change an environment variable—all without worrying about interrupting other deep learning environments on the AMI. This level of flexibility and fine-grained control over your execution environment also means you can now run tests, and benchmark the performance of your deep learning models in a manner that is consistent and reproducible over time.
Finally, the AMI provides a visual interface that plugs straight into your Jupyter notebooks so you can switch in and out of environments, launch a notebook in an environment of your choice, and even reconfigure your environment—all with a single click, right from your Jupyter notebook browser. Our step-by-step guide walks you through these integrations and other Jupyter notebooks and tutorials.
— New AWS Deep Learning AMIs for Machine Learning Practitioners | AWS AI Blog