Welcome to the trial of NVIDIA cuOpt on NVIDIA LaunchPad!
NVIDIA cuOpt is NVIDIA’s GPU-accelerated platform for operations and logistics that enables near real-time routing optimizations, empowering developers to leverage larger data sets and faster processing, delivering new capabilities like dynamic-rerouting, new algorithms, and sub-second solver response time. NVIDIA cuOpt would easily integrate with most applications and accelerate their processing time and accuracy.
Operations Research (OR) and logistics are incredibly compute-intensive with massive operational costs. Most problems are NP-hard, and the GPUs bring the throughput capabilities needed to fuel the most ambitious heuristics (tabu search, guided local search, ant colony) while supporting the most challenging constraints (multi-capacities, time windows, heterogeneous fleet)
NVIDIA cuOpt is exposed as a fast combinatorial optimization solver available as:
The solver has been tested in Last-Mile Delivery applications spanning retail industries and food services. The team is currently working on expanding to factory and warehouse use cases focusing on autonomous moving robots.
Documentation for cuOpt, including details of the Python and server APIs, can be found at https://docs.nvidia.com/cuopt. Ater the lab, visit cuOpt-Resources on github to learn how to run cuOpt locally or in the cloud.
This lab includes multiple Jupyter notebooks that demonstrate how to use cuOpt via the Python APIs or via the cuOpt server.
Within Jupyter, you can also create new notebooks to explore the Python or server APIs on your own, using the examples as a guide.
The version of NVIDIA cuOpt used in this lab is 22.12