Booking required!
Please register on Eventbrite.
Today the computational requirements of deep neural networks that enable complex AI applications like autonomous driving, language translation, and speech synthesis are enormous. A single training cycle can take weeks on a single GPU or even years for the larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.
At the University of Luxembourg, we have access to state-of-the-art HPC systems perfectly suitable for the largest Deep Learning Training sessions. This course will give you all the necessary tools to utilize that power using just Python and the framework of choice between TensorFlow, PyTorch, Caffe2, MXNET, CNTK.
This course will teach you how to use multiple GPUs to training neural networks. You’ll learn:
- Approaches to multi-GPU training
- Algorithmic and engineering challenges to large-scale training
- Key techniques used to overcome the challenges mentioned above
- Upon completion, you’ll be able to effectively parallelize training of deep neural networks using TensorFlow.
This workshop is hands-on with multiple Python coding exercises, thus beginner’s level Python experience will be beneficial. Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.
This course is the premier course on deep learning deployment offered by the NVIDIA Deep Learning Institute. You can find more detailed information here.
Note that this course requires a minimum participation of 20. It is prioritized to SnT but soon registrations will open to all university members.