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Training deep neural network models requires a highly tuned system with the right combination of software, drivers, compute, memory, network, and storage resources.
Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes.
In this talk we are going to introduce an open source project called Fabric for Deep Learning (FfDL). It uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently.
Bio:
Animesh Singh is an STSM and Program Director for IBM Watson and Cloud Platform, currently leading Machine Learning and Deep Learning initiatives on IBM Cloud. He has been with IBM for more than a decade and is currently working with communities and customers to design and implement Deep Learning, Machine Learning and Cloud Computing frameworks. He has been leading cutting edge projects for IBM enterprise customers in Telco, Banking, and Healthcare Industries, around cloud and virtualization technologies. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. He also led the design and development first IBM public cloud offering, and was the lead architect for Bluemix Local. Find Animesh on Twitter @AnimeshSingh.
Animesh Singh
Venue:
ITU Auditorium, International Technological University 2711 N 1st St., San Jose, CA 95134
§Theater style auditorium, capacity 250 people
§Free parking at the ITU, first-come, first-serve basis
§Attendees can only enter the building via the main entrance.
§All attendees are required to check in at the front desk.
§Food and drinks will be provided
Open to all to attend
(Online registration is needed. If you did not register, seating is not guaranteed.)
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