When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Deep Learning, basic Docker fundamentals using images from NVIDIA GPU cloud, working with various frameworks, and networks to train, test and validate results.
Skills Gained
Students will be able to describe the hardware and software components necessary to use a DGX. Students will also be able to demonstrate knowledge of how to use docker and explain some of the important images in the NVIDIA GPU Cloud (NGC), which contains docker images customized by NVIDIA for use with their powerful GPUs. Students will experiment with some of the more popular frameworks hosted on NGC to train, test and validate results. Student will appraise the need for persistent storage for the current neural network operation based on the value of the data and the neural network’s need for access to that data. Finally students will be introduced to the various NVIDIA diagnostic tools and demonstrate how to locate documentation on all elements of the DGX system’s hardware, software, images and frameworks.
- Hardware Overview
- Software Overview
- User Management
- Containerization with Docker
- Using NGC
- DIGITS Example
- Data Storage
- Diagnostics
- Getting Help
Who Can Benefit
- Individuals or organizations who have been using deep learning/machine learning and have newly acquired a DGX
- Individuals or organizations who are new to deep learning/machine learning and have or are planning on getting a DGX
- Individuals or organizations who have been using a DGX in the cloud but don't know Docker or other best practices
