DASK

As we move towards a more dense computing infrastructure, with more compute, more GPUs, accelerated networking, and so forth—multi-gpu training and analysis...

In the machine learning and MLOps world, GPUs are widely used to speed up model training and inference, but what about the other stages of the workflow like ETL...

As datasets continue to grow in size, the adoption of cloud-storage platforms like Amazon S3 and Google Cloud Storage (GCS) are becoming more popular. Although...

Python is no stranger to data scientists. It ranks as the most popular computer language and is widely used for all kinds of tasks. Though Python is notoriously...

GPU-accelerated computing is a game-changer for data practitioners and enterprises, but leveraging GPUs can be challenging for data professionals. RAPIDS...

In XGBoost 1.0, we introduced a new official Dask interface to support efficient distributed training.  Fast-forwarding to XGBoost 1.4, the interface is...

This is the third installment of the series of introductions to the RAPIDS ecosystem. The series explores and discusses various aspects of RAPIDS that allow its...

At NVIDIA, we are driving change in data science, machine learning, and artificial intelligence. Some of the key trends that drive us are as follows: The rise...

In finance, computation efficiency can be directly converted to trading profits sometimes. Quants are facing the challenges of trading off research efficiency...