openvino_notebooks
vector
openvino_notebooks | vector | |
---|---|---|
80 | 97 | |
2,003 | 16,672 | |
5.7% | 2.5% | |
9.9 | 9.9 | |
5 days ago | 3 days ago | |
Jupyter Notebook | Rust | |
Apache License 2.0 | Mozilla Public License 2.0 |
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.
openvino_notebooks
- FLaNK-AIM Weekly 06 May 2024
- FLaNK AI Weekly 18 March 2024
- FLaNK Stack Weekly 19 Feb 2024
- FLaNK Stack Weekly 12 February 2024
- FLaNK Stack 05 Feb 2024
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Optimum Intel OpenVino Performance
Also, credits for using zram in your VM setup; that's a smart hack for memory management. Have you tried tweaking other models like the ones in this OpenVINO notebook?
- FLaNK Stack Weekly 06 Nov 2023
- Trouvez-la plus vite
- Change your voice. FreeVC offers one-shot voice conversion, no text transcript required. Explore how OpenVINO powers AI solutions, see the code on GitHub.
- Vous aurez la banane
vector
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What is a low/reasonable cost solution for service log storage and querying?
I am thinking about using https://vector.dev/ but would also love opinions on the best deal for lower or reasonable cost storage/querying of logs. Thanks!
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Docker Log Observability: Analyzing Container Logs in HashiCorp Nomad with Vector, Loki, and Grafana
job "vector" { datacenters = ["dc1"] # system job, runs on all nodes type = "system" group "vector" { count = 1 network { port "api" { to = 8686 } } ephemeral_disk { size = 500 sticky = true } task "vector" { driver = "docker" config { image = "timberio/vector:0.30.0-debian" ports = ["api"] volumes = ["/var/run/docker.sock:/var/run/docker.sock"] } env { VECTOR_CONFIG = "local/vector.toml" VECTOR_REQUIRE_HEALTHY = "false" } resources { cpu = 100 # 100 MHz memory = 100 # 100MB } # template with Vector's configuration template { destination = "local/vector.toml" change_mode = "signal" change_signal = "SIGHUP" # overriding the delimiters to [[ ]] to avoid conflicts with Vector's native templating, which also uses {{ }} left_delimiter = "[[" right_delimiter = "]]" data=<
- FLaNK AI Weekly 18 March 2024
- Vector: A high-performance observability data pipeline
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Hacks to reduce cloud spend
we are doing something similar with OTEL but we are looking at using https://vector.dev/
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About reading logs
We don't pull logs, we forward logs to a centralized logging service.
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Self hosted log paraer
opensearch - amazon fork of Elasticsearch https://opensearch.org/docs/latestif you do this an have distributed log sources you'd use logstash for, bin off logstash and use vector (https://vector.dev/) its better out of the box for SaaS stuff.
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creating a centralize syslog server with elastic search
I have done something similar in the past: you can send the logs through a centralized syslog servers (I suggest syslog-ng) and from there ingest into ELK. For parsing I am advice to use something like Vector, is a lot more faster than logstash. When you have your logs ingested correctly, you can create your own dashboard in Kibana. If this fit your requirements, no need to install nginx (unless you want to use as reverse proxy for Kibana), php and mysql.
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Show HN: Homelab Monitoring Setup with Grafana
I think there's nothing currently that combines both logging and metrics into one easy package and visualizes it, but it's also something I would love to have.
Vector[1] would work as the agent, being able to collect both logs and metrics. But the issue would then be storing it. I'm assuming the Elastic Stack might now be able to do both, but it's just to heavy to deal with in a small setup.
A couple of months ago I took a brief look at that when setting up logging for my own homelab (https://pv.wtf/posts/logging-and-the-homelab). Mostly looking at the memory usage to fit it on my synology. Quickwit[2] and Log-Store[3] both come with built in web interfaces that reduce the need for grafana, but neither of them do metrics.
- [1] https://vector.dev
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Retaining Logs generated by service running in pod.
Log to stdout/stderr and collect your logs with a tool like vector (vector.dev) and send it to something like Grafana Loki.
What are some alternatives?
chdb - chDB is an embedded OLAP SQL Engine 🚀 powered by ClickHouse
graylog - Free and open log management
deepeval - The LLM Evaluation Framework
Fluentd - Fluentd: Unified Logging Layer (project under CNCF)
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
agent - Vendor-neutral programmable observability pipelines.
starcoder - Home of StarCoder: fine-tuning & inference!
syslog-ng - syslog-ng is an enhanced log daemon, supporting a wide range of input and output methods: syslog, unstructured text, queueing, SQL & NoSQL.
open_model_zoo - Pre-trained Deep Learning models and demos (high quality and extremely fast)
OpenSearch - 🔎 Open source distributed and RESTful search engine.
netron - Visualizer for neural network, deep learning and machine learning models
tracing - Application level tracing for Rust.