When a user starts a new kernel, different options determine how much time Jupyter will wait for it to be successfully spawned before considering the operation as failed. A common reason for timeouts to be reached is because there are no available resources to meet the kernel requirements. For example,…
Exploring Kubernetes clusters without having to learn kubectl commands is great both for developers just getting started as well as for administrators looking for a user-friendly way to navigate across resources. Lens is an integrated development environment (IDE) designed make managing Kubernetes clusters easier. It is built on open-source, is…
Bright Cluster Manager’s data science add-on provides many ML-related packages that can be used to run AI workloads on a Bright cluster without having to use container images. In addition, it is also possible to run AI workloads by using container images from e.g. the NVIDIA GPU Cloud. With Bright’s…
The steps described in this page can be followed to run a distributed Spark application using Kubernetes on Bright 9.1. 1. Software versions The Docker image that is going to be used for Spark will provide software with the following main versions: Operating System: Debian GNU/Linux 10 Apache Spark: 3.1.1…
The steps described in this page can be followed to build a Docker image that is suitable for running distributed Spark applications using XGBoost and leveraging RAPIDS to take advantage of NVIDIA GPUs. A Python application requiring this Docker image is provided by Bright as a Jupyter notebook. It is…