Skip to main content

No project description provided

Project description

RoboMetrics

RoboMetrics is a Python library for measuring system resources. Specially focused on CPU, Memory and GPU usage. It is designed to be used in a wide range of applications, from monitoring system resources to testing and benchmarking.

Goals of RoboMetrics:

  • Provide a simple way to measure resources without losing data in the process.
  • Be able to measure resources in real-time.
  • Interconnect workers in a network.

Installation

To install the client install it via pip:

pip install roborregos-metrics-client

To install the server:

pip install roborregos-metrics-server

Architecture

RoboMetrics is designed to be modular, extensible and distributed. It is composed of four main components:

  • Process: This represent your normal Python/C++ process/application. Inn order to measure its resources, you only need to import the robometrics.lib.register module and call register.
from robometrics.lib.register import Register

Register.async_auto_register() # Recomended for non-blocking calls

Register.auto_unregister()
  • Workers: The worker is a module made to run in the background in a host machine. The worker is responsible for collecting data from the registered processes and sending it to the intake server (or in its defect, save the data locally).

To start a worker, you only need to run the entry point robometrics.worker.worker.

robometrics-worker
  • Intake Server: The intake server is a server that receives data from the workers and stores it in a database. The server is responsible for managing the data and providing an API to access it.

The current implementation relies on a mongo database, but can be worked towards a more general solution. There is a docker-compose file that can be used to start the db.

To start the server, you only need to run the entry point robometrics.server.server.

robometrics-server
  • RoboMetrics-Viz: The visualization module is a web application that connects to the intake server and displays the data in a user-friendly way. The web application is built using Next.js and Vega-Lite.

Refer to the viz repo: RoboMetrics-Viz

Architecture

Recomendations

The modules are pretend to used in a robotics enviroment or a distributed system. This way each host sould be responsible to run a worker and a process. The intake server can be run in a central server or in a server that is always on. (Other alternative is to run the worker and process without a central server and run robometrics-sync to sync the data to a central server)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

roborregos-metrics-server-0.1.0.3.tar.gz (8.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

roborregos_metrics_server-0.1.0.3-py3-none-any.whl (14.9 kB view details)

Uploaded Python 3

File details

Details for the file roborregos-metrics-server-0.1.0.3.tar.gz.

File metadata

File hashes

Hashes for roborregos-metrics-server-0.1.0.3.tar.gz
Algorithm Hash digest
SHA256 f13285442925fecffa4e6d8bebe14700ed51eb472986ab9d8e173fb71954a517
MD5 8855cb1e7300d9f2ea8f305402ed036a
BLAKE2b-256 7b2d0cd746fcc0a2c69d6397855973d0f464d5201940d735176170700b630d55

See more details on using hashes here.

File details

Details for the file roborregos_metrics_server-0.1.0.3-py3-none-any.whl.

File metadata

File hashes

Hashes for roborregos_metrics_server-0.1.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 fdd93a82d028c243516b4825d77984af78b9df8fd24d1b752bce7977f840aa5c
MD5 e28f98484e39589557fa8ad6cd2442cf
BLAKE2b-256 b269382ab3ff69749ebcc76f6d4eb572a2a742fc13aafc488b198959189c69d5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page