Manage Clusters offers a comprehensive interface for configuring and monitoring GPU and CPU clusters, enabling users to optimize resource allocation and performance. With advanced management tools, users can efficiently deploy workloads, scale resources, and gain real-time insights into cluster health and utilization.
To access the dashboard, log into io.net and select Manager Clusters.
To see all the details about your cluster, click on it in the Cluster tab. The screenshot below highlights what you can expect to see in a hired, active cluster.
A completed cluster provides the option to archive the cluster. Click Archive in the bottom right to archive this cluster.Run Jobs and Monitor Your ClusterReady to start working on your cluster? You can run your jobs using either Visual Studio or Jupyter Notebook. The Ray Dashboard lets you manage and monitor everything, including your cluster and running jobs.On your cluster’s details page, grab the IDE Password, and then click on Jupyter Notebook, Visual Studio, or the Ray Dashboard to get started
To access your application, enter the IDE Password.
Once your cluster’s time expires, you’ll lose access to the IDEs and the Ray Dashboard.
Allows you to filter the view by selecting different groups of workers. This option is currently set to show all workers in the cluster.
[#] Workers
Indicates that the dashboard currently shows four workers in the cluster that are active or relevant to the task being monitored. The panel below labels these workers as “IO Worker 1,” “IO Worker 2,” and so on.
[#] GPUs
Shows that there are 4 GPUs (Graphics Processing Units) in use across the cluster. Each worker seems to have one GPU assigned to it, as indicated by the details in each worker’s panel.
Search
The search bar allows you to quickly find specific workers, GPUs, or tasks by searching based on keywords, worker names, device IDs, or other identifiers.
IO Cloud uses a network of computers (called “IO workers”) to create powerful GPU clusters. This means you’re not relying on a single company for your computing power.
Self-healing
If one part of the cluster has issues, the others automatically take over, keeping your projects running smoothly.
Easy to Use
You can easily run your AI projects using Python code, just like on any other cloud platform.
Built on Industry-leading Technology
IO Cloud is powered by the same technology used by OpenAI to train its powerful AI models, such as GPT-3 and GPT-4.
Indicates the worker’s status, such as Completed, Running, Pending, or Failed. A green dot signifies successful completion.
Device ID
The unique identifier for the specific GPU device in the worker node.
GPU Information
GeForce RTX 3060 Ti (GPU type): Each worker is equipped with an NVIDIA GeForce RTX 3060 Ti GPU. x1: Number of GPUs utilized (in this case, one unit). Uptime in Cluster: Displays uptime, showing that the worker has been fully operational without downtime for the monitored period.
Activity Consistency Status Bar
A visual representation of the worker’s uptime, consisting of 10 white squares filled in to indicate 100% uptime.
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