At NVIDIA GTC 2024, the RAPIDS team demonstrated new features on NVDashboard v0.10 a dashboard that runs on JupyterLab, for monitoring GPU usage to help maximize the efficiency of GPU resources. We are excited to follow that up by announcing that NVDashboard v0.10 is now available to use. This update introduces a host of improvements, including data streaming through WebSockets for…
]]>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 pipelines or hyperparameter optimization? Within the RAPIDS data science framework, ETL tools are designed to have a familiar look and feel to data scientists working in Python. Do you currently use Pandas, NumPy, Scikit-learn…
]]>Bring-your-own-container models are widely supported on today’s modern compute platforms. In other words, you can provide your own container images within your custom software environment. However, user-provided containers must satisfy each platform’s unique requirements, which can vary from platform to platform. For example, you may need to: Keeping your container images conformant…
]]>This post was originally published on the RAPIDS AI blog here. NVDashboard is an open-source package for the real-time visualization of NVIDIA GPU metrics in interactive Jupyter Lab environments. NVDashboard is a great way for all GPU users to monitor system resources. However, it is especially valuable for users of RAPIDS, NVIDIA’s open-source suite of GPU-accelerated data-science…
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