Ray is an open source compute framework that simplifies the scaling of AI and Python workloads for on-premise and cloud clusters. Ray integrates with popular libraries, data stores, and tools within the machine learning (ML) ecosystem, including Scikit-learn, PyTorch, and TensorFlow. This gives developers the flexibility to scale complex AI applications without making changes to their existing workflows or AI stack.
When your apps and infrastructure rely on dozens of third-party providers for key functionality, it’s important to closely track their outages. If a service you rely on goes down, you need to move quickly to limit the outage’s impact on your users. IsDown provides a detailed status page aggregator and uptime monitoring for all your third-party dependencies.
#ownership #reliability #performance #apm #devops #devsecops #applicationsecurity #microservicesarchitecture #vulnerability #observability #cloudcomputing
Steadybit is a software reliability platform that uses chaos engineering and fault injection to help organizations improve the stability and performance of their applications. By allowing customers to simulate turbulent scenarios in a controlled environment, Steadybit enables you to identify and mitigate potential system issues to reduce downtime and improve resilience.
In a previous blog post, we explained how containers’ CPU and memory requests can affect how they are scheduled. We also introduced some of the effects CPU and memory limits can have on applications, assuming that CPU limits were enforced by the Completely Fair Scheduler (CFS) quota. In this post, we are going to dive a bit deeper into CPU and share some general recommendations for specifying CPU requests and limits.