Главная
Study mode:
on
1
Intro
2
The problem of selfdriving cars
3
NVIDIAs principles
4
Development workflow
5
Platform overview
6
Data lake overview
7
Collection
8
Active Learning
9
Targeted Learning
10
Ground Truth
11
Labelling
12
Examples
13
Labeling
14
Traceability
15
Export
16
Data Preparation
Description:
Explore NVIDIA's Project MagLev, an end-to-end AI platform for developing self-driving car software, in this 26-minute USENIX conference talk. Dive into the infrastructure supporting continuous data ingest from multiple vehicles producing terabytes of data hourly. Learn how autonomous AI designers iterate training of new neural network designs across thousands of GPU systems and validate their behavior over multi-petabyte-scale datasets. Discover the overall architecture for data center deployment, AI pipeline automation, large-scale AI dataset management, training, and testing. Gain insights into the development workflow, data lake overview, collection methods, active and targeted learning, ground truth labeling, traceability, and data preparation techniques used in NVIDIA's DRIVE software ecosystem.

Inside NVIDIA’s AI Infrastructure for Self-Driving Cars

USENIX
Add to list
0:00 / 0:00