Powered by TensorFlow: utilizing deep learning to better predict extreme weather November 18, 2019 9 By Stanley Isaacs CategoryArticles BlogTags.gov ML building ml models datasets google ML lawrence berkeley lawrence berkeley national laboratory machine learning machine learning government ML ML datasets ML models ML problems ML researchers national energy research national energy research scientific computing center NERSC NERSC gov Powered by TensorFlow super computing TensorFlow tensorflow apps tensorflow business TensorFlow dev summit tensorflow weather app tensorflow weather patterns 9 Comments username says: March 7, 2019 at 9:57 pm it is still unpredictiable, we can built space craft and send rockets to moon even mars but if there is an unpredicted wind blown, then all those sophisticated vechiles cant launch 😀 Reply Hector Seguro says: March 7, 2019 at 10:02 pm 0:40 When people tell you that spaces are better than tabs, show them this frame. Now I will have nightmares Reply Trade! says: March 7, 2019 at 10:10 pm Science For The Win! Reply Wallace Rose says: March 7, 2019 at 11:31 pm Who predicts better? IBM Watson (weather channel) or tensor flow?!?!?! Or are they different but with the same statistical out come? Reply Ganesh N says: March 8, 2019 at 2:04 am Harnessing the exceptional performance of the TensorFlow back end as well as the improved data feeding mechanisms through the tf data API, we achieve up to 40 TeraFLOP/s performance on a single nvidia v100 GPU. By applying optimizations to the distributed training enabler framework Horovod, the training algorithm as well as to the data feeding pipeline asynchronously executed on the IBM Power9 host processors, we could scale the distributed training efficiently to the full Summit AI system. Code delivered a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision on 4560 Summit nodes, i.e. 27360 V100 GPUs. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively. Getting Access to Summit AI super computer : https://www.linkedin.com/pulse/president-trump-signs-development-ai-ganesan-narayanasamy Reply victory for me says: March 8, 2019 at 3:30 am 🤓🤓🤓🤓 Reply Ojasvi Singh 786 says: March 10, 2019 at 6:59 am 👏👏👏 Reply Danlan says: March 12, 2019 at 9:16 pm I was working on predicting typhoon intensity in Japan using deep learning as well! Reply pi money says: September 18, 2019 at 6:43 am Can the system predict earthquake? Reply Leave a Reply Cancel reply Your email address will not be published. Required fields are marked *Comment Name * Email * Save my name, email, and website in this browser for the next time I comment.