Prometheus
Prometheus is an open-source systems monitoring and alerting toolkit originally developed at SoundCloud and later donated to the Cloud Native Computing Foundation (CNCF). It uses a time-series data model and collects metrics via an HTTP pull mechanism. Prometheus includes a powerful built-in query language, PromQL, supporting multi-dimensional data analysis. It offers flexible alerting rule configuration and integrates with visualization tools such as Grafana, and is widely adopted in cloud-native and microservices architecture monitoring.
Core Facts
A typical MLOps workflow covers stages including data version management, experiment tracking, model packaging, serving interfaces, CI/CD automation, online monitoring, and drift detection.
Docker、Kubernetes、Terraform、etcd、Prometheus等云原生核心项目几乎都用Go编写
Timeline (last 90 days)
Prometheus uses a pull model that scrapes at fixed intervals and stores timestamped samples, naturally satisfying the rate recovery requirement.
macOS has no native, always-on HTTP endpoint for GPU metrics, unlike NVIDIA's nvidia-smi, so a custom mac_gpu_exporter is needed to periodically run powermetrics and convert it into a Prometheus-scrapable format.
In the same year, the authors of Prometheus found that with a reference answer, the evaluator no longer needs to solve the problem itself.
PromQL and LogQL are the query languages of Prometheus and Loki respectively; PromQL is oriented toward time series and supports functions such as rate() and histogram_quantile(), used to compute metrics such as error rates and quantiles.
The Prometheus tool group of Grafana MCP allows executing instant and range PromQL queries, listing metric and label names, and computing p50/p90/p95/p99 from histograms.
The Muse Wonder series is the product of fourteen months of technical reconstruction by Meta AI, trained on the gigawatt-scale super data center Prometheus.
Manish advises people transitioning into DevOps/SRE to tie together knowledge points such as Linux, Shell, AWS, Terraform, Docker, Kubernetes, CI/CD, Prometheus, Grafana, and Python through one complete end-to-end project, and the project can take three to six months to complete.
The /metrics exposed by vLLM is a Prometheus-format monitoring interface, whose counters record the actual number of prompt tokens processed and tokens generated, which is more reliable than client-side timestamp estimates.
Bifrost supports Prometheus metrics, OpenTelemetry tracing, and integrations with platforms such as Datadog, Kafka, BigQuery, Pub/Sub, and Splunk.
DCGM Exporter exposes a Prometheus-format metrics scraping endpoint on port 9400 by default, deployed on each GPU node as a Kubernetes DaemonSet or a standalone process.
32 more timeline events
All Facts (20)
A typical MLOps workflow covers stages including data version management, experiment tracking, model packaging, serving interfaces, CI/CD automation, online monitoring, and drift detection.
65%UnverifiedLiteLLM的日志系统支持与Langfuse、Helicone、Datadog和Prometheus等可观测性平台集成
90%UnverifiedPrometheus最初由SoundCloud开发，2016年成为继Kubernetes之后第二个加入CNCF的毕业项目
90%UnverifiedNCCL Inspector与Prometheus监控系统深度集成，通过Prometheus Exporter暴露监控指标
90%Partially VerifiedDocker、Kubernetes、Terraform、etcd、Prometheus等云原生核心项目几乎都用Go编写
65%UnverifiedPrometheus uses a pull model that scrapes at fixed intervals and stores timestamped samples, naturally satisfying the rate recovery requirement.
50%UnverifiedmacOS has no native, always-on HTTP endpoint for GPU metrics, unlike NVIDIA's nvidia-smi, so a custom mac_gpu_exporter is needed to periodically run powermetrics and convert it into a Prometheus-scrapable format.
50%UnverifiedIn the same year, the authors of Prometheus found that with a reference answer, the evaluator no longer needs to solve the problem itself.
50%UnverifiedThe Prometheus tool group of Grafana MCP allows executing instant and range PromQL queries, listing metric and label names, and computing p50/p90/p95/p99 from histograms.
50%UnverifiedPromQL and LogQL are the query languages of Prometheus and Loki respectively; PromQL is oriented toward time series and supports functions such as rate() and histogram_quantile(), used to compute metrics such as error rates and quantiles.
50%UnverifiedThe Muse Wonder series is the product of fourteen months of technical reconstruction by Meta AI, trained on the gigawatt-scale super data center Prometheus.
50%UnverifiedManish advises people transitioning into DevOps/SRE to tie together knowledge points such as Linux, Shell, AWS, Terraform, Docker, Kubernetes, CI/CD, Prometheus, Grafana, and Python through one complete end-to-end project, and the project can take three to six months to complete.
50%UnverifiedThe /metrics exposed by vLLM is a Prometheus-format monitoring interface, whose counters record the actual number of prompt tokens processed and tokens generated, which is more reliable than client-side timestamp estimates.
50%UnverifiedBifrost supports Prometheus metrics, OpenTelemetry tracing, and integrations with platforms such as Datadog, Kafka, BigQuery, Pub/Sub, and Splunk.
50%UnverifiedDCGM Exporter exposes a Prometheus-format metrics scraping endpoint on port 9400 by default, deployed on each GPU node as a Kubernetes DaemonSet or a standalone process.
50%UnverifiedOpenSearch's metrics storage currently still relies primarily on Prometheus, with a roadmap plan to integrate via the Prometheus API so that OpenSearch can serve as the long-term metrics storage backend.
50%UnverifiedUsers can distinguish the hit counts of each cache tier by adding corresponding labels for different cache tiers (such as cache_tier="gpu" or cache_tier="cpu").
50%UnverifiedvLLM has a built-in /metrics HTTP endpoint that exposes runtime metrics such as throughput, queue length, and token generation speed in Prometheus text format.
50%UnverifiedPrometheus is the mainstream time-series metrics collection system in the cloud-native space, using a pull mode to periodically scrape metric data from service endpoints and supporting multi-dimensional aggregation queries via label dimensions.
50%UnverifiedTelemetry data generated by OTEL can be forwarded via a Collector to Jaeger, Prometheus, Datadog, or any backend compatible with the OTLP protocol
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