Modified items

All recently modified items, latest first.

nagios-4.4.14-5.lbn36.x86_64: Nagios is a program that will monitor hosts and services on your network. It has the ability to send email or page alerts when a problem arises and when a problem is resolved. Nagios is written in C and is designed to run under Linux (and some other *NIX variants) as a background process, intermittently running checks on various services that you specify. The actual service checks are performed by separate "plugin" programs which return the status of the checks to Nagios. The plugins are available at https://github.com/nagios-plugins/nagios-plugins This package provides the core program, web interface, and documentation files for Nagios. Development files are built as a separate package.

python3-schedule-1.2.2-1.lbn36.noarch: Python job scheduling for humans. Run Python functions (or any other callable) periodically using a friendly syntax. A simple to use API for scheduling jobs, made for humans. In-process scheduler for periodic jobs. No extra processes needed! Very lightweight and no external dependencies. Excellent test coverage. Tested on Python and 3.7, 3.8, 3.9, 3.10, 3.11, 3.12 Usage $ pip install schedule import schedule import time def job(): print("I'm working...") schedule.every(10).seconds.do(job) schedule.every(10).minutes.do(job) schedule.every().hour.do(job) schedule.every().day.at("10:30").do(job) schedule.every(5).to(10).minutes.do(job) schedule.every().monday.do(job) schedule.every().wednesday.at("13:15").do(job) schedule.every().day.at("12:42", "Europe/Amsterdam").do(job) schedule.every().minute.at(":17").do(job) def job_with_argument(name): print(f"I am {name}") schedule.every(10).seconds.do(job_with_argument, name="Peter") while True: schedule.run_pending() time

python3-anthropic-0.49.0-1.lbn36.noarch: Anthropic Python API library The Anthropic Python library provides convenient access to the Anthropic REST API from any Python 3.8+ application. It includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx. Documentation The REST API documentation can be found on docs.anthropic.com. The full API of this library can be found in api.md. Installation pip install anthropic Usage The full API of this library can be found in api.md. import os from anthropic import Anthropic client = Anthropic( api_key=os.environ.get("ANTHROPIC_API_KEY"), ) message = client.messages.create( max_tokens=1024, messages=[ { "role": "user", "content": "Hello, Claude", } ], model="claude-3-opus-20240229", ) print(message.content) While you can provide an api_key keyword argument, we recommend using python-d

python3-openlit-1.33.23-1.lbn36.noarch: OpenTelemetry-native AI Observability, Evaluation and Guardrails Framework Documentation | Quickstart | Roadmap | Feature Request | Report a Bug OpenLIT SDK is a monitoring framework built on top of OpenTelemetry that gives your complete Observability for your AI stack, from LLMs to vector databases and GPUs, with just one line of code with tracing and metrics. It also allows you to send the generated traces and metrics to your existing monitoring tools like Grafana, New Relic, and more. This project proudly follows and maintains the Semantic Conventions with the OpenTelemetry community, consistently updating to align with the latest standards in Observability. ⚡ Features 🔎 Auto Instrumentation: Works with 50+ LLM providers, Agents, Vector databases, and GPUs with just one line of code. 🔭 OpenTelemetry-Native Observability SDKs: Vendor-neutral SDKs that can send traces and metrics to your existing observability tool like Prometheus and Jaeger. 💲 Cost Tracking for Custom and F

grafana-pyroscope-0.37.2-0.1.git5dc1ccf.lbn36.x86_64: Grafana Pyroscope is an open source continuous profiling platform. It will help you: Find performance issues and bottlenecks in your code Use high-cardinality tags/labels to analyze your application Resolve issues with high CPU utilization Track down memory leaks Understand the call tree of your application Auto-instrument your code to link profiling data to traces

grafana-image-renderer-3.12.0-1.lbn36.x86_64: Rendering images requires a lot of memory, mainly because Grafana creates browser instances in the background for the actual rendering. We recommend a minimum of 16GB of free memory on the system rendering images. Rendering multiple images in parallel requires an even bigger memory footprint. You can use the remote rendering service in order to render images on a remote system, so your local system resources are not affected. Configuration ------------- Install this package; and edit the rendering section in your grafana config: [rendering] server_url = http://localhost:8081/render callback_url = http://localhost:3000/

grafana-alloy-devel-1.9.1-0.1.gitd3d7931.lbn36.x86_64: Development tools for Alloy; linting and component listing

grafana-alloy-1.9.1-0.1.gitd3d7931.lbn36.x86_64: Grafana Alloy is an open source OpenTelemetry Collector distribution with built-in Prometheus pipelines and support for metrics, logs, traces, and profiles. What can Alloy do? Programmable pipelines: Use a rich expression-based syntax for configuring powerful observability pipelines. OpenTelemetry Collector Distribution: Alloy is a distribution of OpenTelemetry Collector and supports dozens of its components, alongside new components that make use of Alloy's programmable pipelines. Big tent: Alloy embraces Grafana's "big tent" philosophy, where Alloy can be used with other vendors or open source databases. It has components to perfectly integrate with multiple telemetry ecosystems: * OpenTelemetry Collector * Prometheus * Grafana Loki * Grafana Pyroscope Shareable pipelines: Use modules to share your pipelines with the world. Automatic workload distribution: Configure Alloy instances to form a cluster for automatic workload distribution. Centralized configuration support: Alloy supports retrieving its configuration from a server for centralized configuration management. Debugging utilities: Use the built-in UI for visualizing and debugging pipelines.

prometheus-kannel-exporter-0.8.0-1.lbn36.noarch: Kannel exporter for Prometheus. Exposes metrics collected from the kannel status page.

prometheus-graphite-exporter-0.15.0-1.lbn36.x86_64: Server that accepts metrics via the Graphite protocol and exports them as Prometheus metrics.

prometheus-apache-exporter-0.13.1-0.1.git11dc46e.lbn36.x86_64: Exports apache mod_status statistics via HTTP for Prometheus consumption.

prometheus-alerts-rabbitmq-exporter-0.0.1-1.lbn36.x86_64: Alerting rules for RabbitMQ exporter

prometheus-alerts-prometheus-0.0.1-1.lbn36.x86_64: Alerting rules for Prometheus

prometheus-alerts-postgresql-exporter-0.0.1-1.lbn36.x86_64: Alerting rules for PostgreSQL exporter

prometheus-alerts-node-exporter-0.0.1-1.lbn36.x86_64: Alerting rules for node-exporter

prometheus-alerts-mongodb-exporter-0.0.1-1.lbn36.x86_64: Alerting rules for Mongodb exporter

prometheus-alerts-es-exporter-0.0.1-1.lbn36.x86_64: Alerting rules for Elastic Search exporter

prometheus-alerts-consul-exporter-0.0.1-1.lbn36.x86_64: Alerting rules for Consul exporter

prometheus-alerts-0.0.1-1.lbn36.x86_64: Popular prometheus alerts as inspired by https://awesome-prometheus-alerts.grep.to/rules.html


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