Today, we hit 2,500 stars on our Github repository! To thank our users for getting us here, we're doing a PlaidML swag giveaway. Enter our giveaway by telling us how you use PlaidML (be sure to include #PlaidMLUserStories in your entry). We'll select a winner on 10/4.
@pillarofautuman 2/2 If you'd like us to look at your specific use case, please file an issue on our Github repository with some code samples and your hardware specs, and we'll take a look!
https://t.co/yTH73ZSSX5
@pillarofautuman 1/2 Hi @pillarofautuman thanks for using PlaidML! Our team loves the cats/dogs dataset (we're even rolling out a demo featuring resnet50 training on it).
Regarding performance, PlaidML works best with larger networks/batch sizes since CPU->GPU data transfers are bottlenecked.
ICYMI, the PlaidML team hosted an introductory webinar a couple of weeks ago! If you'd like to learn more about how PlaidML can accelerate your deep learning workflows, catch the recording of our webinar here: https://t.co/KxcNBeoos2
#plaidml#TensorCompiler#DeepLearning
Great news - Tim and Denise from our team will be presenting at our very own webinar on 7/17 at 9AM PDT! Follow the link and sign up for "The PlaidML Tensor Compiler" to stay in the loop.
https://t.co/m8B01sxccK
Hey everyone - #plaidml v0.6.0 has been released to PyPI.
https://t.co/6DQaFUQpDg
We've spent a lot of time working on our polyhedral IR, Stripe. We're so excited for you to try out the optimizations we've made in this release!
Good news! #plaidml v0.5.0 has been released to PyPI. https://t.co/3Kse1VEVmb
Release notes and a proper github release inbound. This version supports the latest #keras, has a ton of bug fixes, and contains an early release of our new polyhedral IR: Stripe.
More soon!
@CDerinbogaz Good question! Our new Stripe based backends will make supporting multi-gpu configs quite a bit easier. Not sure exactly where that is on our roadmap – we'll be creating and publishing a roadmap in the next month or so.
@SShanabrook @denfromufa Yes it's designed to have very minimal dependencies and you could use it anywhere the computational model makes sense.
Adding a backend right now is fairly easily but a bit ad-hoc. We'll have a GPU / OpenCL example and more docs out soon.
Do you love tensor compilers like #plaidml and @TVMProject as much as I do?
If so, Intel is hiring people to contribute to #plaidml and develop groundbreaking new HW/SW co-design tools: https://t.co/42kJ2tmpLd
#CVPR18 attendees: Join us tomorrow 6/18 at 9am in ballroom BD at the Autonomous Driving Workshop to hear @DeepScale_ co-founder and @berkeley_ai Prof. Kurt Keutzer cover how automotive challenges computer vision research.