'How to Accelerate Auto Data Tagging for Autonomous Driving in Hybrid Cloud' by Alluxio spark datatagging
In this blog, guest writer Feifei Cai , Hao Zhu , shares the practice of using Alluxio and Spark to accelerate the auto data tagging system in WeRide, an autonomous driving technology company. The original content was published on Alluxio's Blog .is a global leading company that develops L4 autonomous driving technologies.
We have both hardware and software in the loop. The biggest challenge is the size of the data. We generate many terabytes of data every day and have accumulated millions of kilometers of mileage, so the entire size is in petabytes. This requires vast amounts of storage and significant computing power.Tags are sometimes confused with labels. In autonomous driving, tags usually mean scenarios, while labels can be obstacles or boxes.
In a data-driven autonomous driving workflow, data tagging plays an essential role in data analytics. With the tagging results, we can do data selection to support the downstream pipelines, including model training, benchmark, and long-tail evaluation.An end-to-end tagging pipeline is to collect and upload the ROS bag files from the test vehicles and then convert them into Parquet format. After that, we can run Spark jobs to perform data analytics algorithms.
In both public cloud and on-prem data centers, we use Kubernetes to manage the computing resources of the CPU and the GPU clusters and to schedule the workflow and tasks. In the storage layer, we use MinIO to build S3 compatible object storage service.
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