Publications (6)
Using Python for Model Inference in Deep Learning
Zachary DeVito, Jason Ansel, Will Constable +3
Python has become the de-facto language for training deep neural networks, coupling a large suite of scientific computing libraries with efficient libraries for tensor computation…
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
Chris Cummins, Bram Wasti, Jiadong Guo +9
Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domain…
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework
Menglu Yu, Jiaqi Xu, Yuzhen Huang +19
In large-scale industrial applications, deep learning models that power recommendation and ranking have complex and diverse model architectures. These models are continuously devel…
Tight Prediction Intervals Using Expanded Interval Minimization
Dongqi Su, Ying Yin Ting, Jason Ansel
Prediction intervals are a valuable way of quantifying uncertainty in regression problems. Good prediction intervals should be both correct, containing the actual value between the…
DMTCP: Transparent Checkpointing for Cluster Computations and the Desktop
Jason Ansel, Kapil Arya, Gene Cooperman
DMTCP (Distributed MultiThreaded CheckPointing) is a transparent user-level checkpointing package for distributed applications. Checkpointing and restart is demonstrated for a wide…
Torch.fx: Practical Program Capture and Transformation for Deep Learning in Python
James K. Reed, Zachary DeVito, Horace He +2
Modern deep learning frameworks provide imperative, eager execution programming interfaces embedded in Python to provide a productive development experience. However, deep learning…