resource-tracker 0.5.0

Lightweight Python package for monitoring process and system resource usage with optional Metaflow integration for cloud cost optimization.

resource-tracker

A lightweight, zero-dependency Python package for monitoring resource usage across processes and at the system level. Designed with batch jobs in mind (like Python or R scripts, or Metaflow steps), it provides simple tools to track CPU, memory, GPU, network, and disk utilization with minimal setup -- e.g. using a step decorator in Metaflow to automatically track resource usage and generate a card with data visualizations on historical resource usage and cloud server recommendations for future runs.

For an alternative implementation of the same concept, but less tied to the Python ecosystem, see resource-tracker-rs.

Installation

You can install the stable version of the package from PyPI:

pip install resource-tracker

Development version can be installed directly from the git repository:

pip install git+https://github.com/sparecores/resource-tracker.git

Note that depending on your operating system, you might need to also install psutil (e.g. on MacOS and Windows). For more details, see the OS support section.

Integrations

The resource-tracker Python package is designed to be used in a variety of ways, even outside of Python. Find more details about how to use it directly from Python, R, or via our framework integrations, such as Metaflow, in the integrations section of the documentation.

It also supports streaming resource usage data to a remote location for central analysis, visualization, and recommendations. To get started, visit the sentinel.sparecores.com website to register a free account, generate an API key, and use it to configure the resource-tracker package via the SENTINEL_API_KEY environment variable. Find more details in the the streaming section of the documentation.

Operating System Support

The package was originally created to work on Linux systems (as the most commonly used operating system on cloud servers) using procfs directly and without requiring any further Python dependencies, but to support other operating systems as well, now it can also use psutil when available.

To make sure the resource tracker works on non-Linux systems, install via:

pip install resource-tracker[psutil]

Minor inconsistencies between operating systems are expected, e.g. using PSS (Proportional Set Size) instead of RSS (Resident Set Size) as the process-level memory usage metric on Linux, as it is evenly divides the shared memory usage between the processes using it, making it more representative of the memory usage of the monitored applications. Mac OS X and Windows use USS (Unique Set Size) instead.

CI/CD is set up to run tests on the below operating systems:

• Ubuntu latest LTS (24.04)
• MacOS latest (13)
• Windows latest (Windows Server 2022)

Python Version Support

The package supports Python 3.9 and above.

CI/CD is set up to run tests on the below Python versions on Ubuntu latest LTS, Windows Server 2022 and MacOS latest:

• 3.9
• 3.10
• 3.11
• 3.12
• 3.13
• 3.14

Performance

The performance of the procfs and the psutil implementations is similar, see e.g. benchmark.py for a comparison of the two implementations when looking at process-level stats:

PSUtil implementation: 0.082130s avg (min: 0.067612s, max: 0.114606s)
ProcFS implementation: 0.084533s avg (min: 0.081533s, max: 0.111782s)
Speedup factor: 0.97x (psutil faster)

On a heavy application with many descendants (such as Google Chrome with hundreds of processes and open tabs):

PSUtil implementation: 0.201849s avg (min: 0.193392s, max: 0.214061s)
ProcFS implementation: 0.182557s avg (min: 0.174610s, max: 0.192760s)
Speedup factor: 1.11x (procfs faster)

The system-level stats are much cheaper to collect, and there is no effective difference in performance between the two implementations.

Why have both implementations then? The psutil implementation works on all operating systems at the cost of the extra dependency, while the procfs implementation works without any additional dependencies, but only on Linux. This latter can be useful when deploying cloud applications in limited environments without easy control over the dependencies (e.g. Metaflow step decorator without explicit @pypi config).

References

• PyPI: https://pypi.org/project/resource-tracker
• Documentation: https://sparecores.github.io/resource-tracker
• Source code: https://github.com/SpareCores/resource-tracker
• Project roadmap and feedback form: https://sparecores.com/feedback/metaflow-resource-tracker