slurm
Grafana
slurm | Grafana | |
---|---|---|
6 | 380 | |
2,350 | 60,503 | |
2.8% | 0.8% | |
10.0 | 10.0 | |
2 days ago | 4 days ago | |
C | TypeScript | |
GNU General Public License v3.0 or later | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
slurm
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ntasks and submit.lua in Slurm
I'm trying to have Slurm automatically switch partitions to a specific one via the job_sutmit.lua plugin whenever our users request strictly more than 8 cpus. But trying to extract or calculate ahead of time how many cpus will be allocated or requested isn't trivial (to me). Are there attributes in job_submit that could help out with this task? For example, I don't see any job->desc.ntasks attribute in https://github.com/SchedMD/slurm/blob/master/src/plugins/job_submit/lua/job_submit_lua.c. Any information or documentation on how to leverage job_submit.lua would be appreciated.
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job scheduling for scientific computing on k8s?
Do you have a reason to use kubernetes besides it’s the $CURRENT tech? Why not stick with what you’re already familiar with (batch job managers) and use SLURM, a workload and resource manager, like many others in HPC? Do the researchers need to schedule against Nvidia GPU resources now or in the future? Nvidia themselves recommend SLURM.
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What’s the path to working on supercomputers or quantum computing?
Quantum computing and supercomputers are two different things. Quantum computers are currently an area of research, there isn't a version ready for use apart from some prototypes, and it will probably stay that way for while. Also, quantum computing will most likely not be a completly new architecture, that all of the chips we use will adopt, but an addition to current chipsets for some important but special tasks. Supercomputers, or HPC (High performance clusters) are classic computers, just that they are huge. They use derivatives of "off-the-shelf", but high end, hardware. There is a lot of interesting work in designing such systems, a lot of challenging problems in distributed systems theory, but they aren't a complete detached industry. Using them for work, not designing them, doesn't require a EECS type degree, they guy who sit's next to me in the office, uses a supercomputer to predict protein folding, he is by training a doctor and now does computational microbiology. The applications for massive compute power (often times "just brute force the solution instead of spending years in the lab") are almost endless, but to use them it's not that important to understand the full details of how they are constructed, domain knowledge in the application domain is much more important. If you know how your cluster is structured, and knowledge of slurm etc. will enable you to use the supercomputer just fine, again, they aren't that different from regular computers, just that you workstation might have 1 CPU and your supercomputer has 500. Hiding this complexity is done by slurm or any other resource manager. It's open source as well :) https://github.com/SchedMD/slurm
- Open source / part time research in the world of HPC?
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Brand New HPC Sysadmin at a Major University, Where to Start?
SLURM (distributed by OpenHPC) If you have shared storage then this is the industry standard solution that is both open source and free (extremely popular in the top 500 list). You can pair this with a high speed network or not depending on your research workloads.
- Is it possible to let slurmdbd connect to mysql over unix sockets?
Grafana
- Grafana: From Dashboards to Centralized Observability
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.
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Golang: out-of-box backpressure handling with gRPC, proven by a Grafana dashboard
To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.
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Monitoring, Observability, and Telemetry Explained
Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.
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4 facets of API monitoring you should implement
Prometheus: Open-source monitoring system. Often used together with Grafana.
- Grafana: Open and composable observability and data visualization platform
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The Mechanics of Silicon Valley Pump and Dump Schemes
Grafana
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Reverse engineering the Grafana API to get the data from a dashboard
Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.
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Building an Observability Stack with Docker
So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:
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How to collect metrics from node.js applications in PM2 with exporting to Prometheus
In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:
What are some alternatives?
Ansible - Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.
Thingsboard - Open-source IoT Platform - Device management, data collection, processing and visualization.
ohpc - OpenHPC Integration, Packaging, and Test Repo
Apache Superset - Apache Superset is a Data Visualization and Data Exploration Platform [Moved to: https://github.com/apache/superset]
mfem - Lightweight, general, scalable C++ library for finite element methods
Heimdall - An Application dashboard and launcher
spack - A flexible package manager that supports multiple versions, configurations, platforms, and compilers.
Wazuh - Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
flux-operator - Deploy a Flux MiniCluster to Kubernetes with the operator
Thingspeak - ThingSpeak is an open source “Internet of Things” application and API to store and retrieve data from things using HTTP over the Internet or via a Local Area Network. With ThingSpeak, you can create sensor logging applications, location tracking applications, and a social network of things with status updates.
prometheus - The Prometheus monitoring system and time series database.
uptime-kuma - A fancy self-hosted monitoring tool