resource-tracker 0.2.0

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 server level. Designed with batch jobs in mind (like 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.

Installation

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

pip install resource-tracker

Development version can be installed directly from the 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).

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

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 server-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).

Standalone Usage

The package comes with helper functions and classes for tracking resource usage, such as PidTracker and SystemTracker, both using either procfs or psutil under the hood, depending on which is available, with a preference for psutil when both are present.

from resource_tracker import SystemTracker
tracker = SystemTracker()

SystemTracker tracks system-wide resource usage, including CPU, memory, GPU, network traffic, disk I/O and space usage every 1 second, and write CSV to the standard output stream by default. Example output:

"timestamp","processes","utime","stime","cpu_usage","memory_free","memory_used","memory_buffers","memory_cached","memory_active","memory_inactive","disk_read_bytes","disk_write_bytes","disk_space_total_gb","disk_space_used_gb","disk_space_free_gb","net_recv_bytes","net_sent_bytes","gpu_usage","gpu_vram","gpu_utilized"
1741785685.6762981,1147955,40,31,0.7098,37828072,26322980,16,1400724,13080320,1009284,86016,401408,5635.25,3405.81,2229.44,10382,13140,0.24,1034.0,1
1741785686.676473,1147984,23,49,0.7199,37836696,26316404,16,1398676,13071060,1009284,86016,7000064,5635.25,3405.81,2229.44,1369,1824,0.15,1033.0,1
1741785687.6766264,1148012,38,34,0.7199,37850036,26301016,16,1400724,13043036,1009284,40960,49152,5635.25,3405.81,2229.44,10602,9682,0.26,1029.0,1

The default stream can be redirected to a file by passing a path to the csv_file_path argument, and can use different intervals for sampling via the interval argument.

The PidTracker class tracks resource usage of a running process and optionally all its children (recursively), in a similar manner, although somewhat limited in functionality, as e.g. nvidia-smi pmon can only track up-to 4 GPUs, and network traffic monitoring is not available.

Helper functions are also provided, e.g. get_pid_stats and get_system_stats from both the tracker_procfs and tracker_psutil modules, which are used internally by the above classes after diffing values between subsequent calls.

See more details in the API References.

Discovery Helpers

The packages also comes with helpers for discovering the cloud environment and basic server hardware specs. Quick example on an AWS EC2 instance:

from resource_tracker import get_cloud_info, get_server_info
get_cloud_info()
# {'vendor': 'aws', 'instance_type': 'g4dn.xlarge', 'region': 'us-west-2', 'discovery_time': 0.1330404281616211}
get_server_info()
# {'vcpus': 4, 'memory_mb': 15788.21, 'gpu_count': 1, 'gpu_names': ['Tesla T4'], 'gpu_memory_mb': 15360.0}

Spare Cores integration can do further lookups for the current server type, e.g. to calculate the cost of running the current job and recommend cheaper cloud server types for future runs.

Metaflow Integration

The package also comes with a Metaflow extension for tracking resource usage of Metaflow steps, including the visualization of the collected data in a card with recommended @resources and cheapest cloud server type for future runs, along with basic cost estimates.

To get started, import the track_resources decorator from metaflow and use it to decorate your Metaflow steps:

from metaflow import Flow, FlowSpec, step, track_resources

class ResourceTrackingFlow(FlowSpec):
    @step
    def start(self):
        print("Starting step")
        self.next(self.my_sleeping_data)

    @track_resources
    @step
    def my_sleeping_data(self):
        data = bytearray(500 * 1024 * 1024)  # 500MB
        sleep(3)
        self.next(self.end)

    @step
    def end(self):
        print("Step finished")
        pass

if __name__ == "__main__":
    ResourceTrackingFlow()

Note that if you are using --environment=pypi or other means of custom package management on a non-Linux system, you might need to install psutil there.

Example output of an auto-generated Metaflow card:

Example data collected and then stored as an artifact of the step:

from metaflow import Flow
from rich import print as pp

artifact = Flow("ResourceTrackingFlow").latest_run.data.resource_tracker_data
pp(artifact)
# {
#     'pid_tracker': TinyDataFrame with 9 rows and 12 columns. First row as a dict: {'timestamp': 1741732803.3076203, 'pid': 
# 777691.0, 'children': 3.0, 'utime': 95.0, 'stime': 13.0, 'cpu_usage': 1.0796, 'pss': 563273.0, 'read_bytes': 52260.0, 
# 'write_bytes': 0.0, 'gpu_usage': 0.0, 'gpu_vram': 0.0, 'gpu_utilized': 0.0},
#     'system_tracker': TinyDataFrame with 9 rows and 21 columns. First row as a dict: {'timestamp': 1741732803.2471318, 
# 'processes': 777773.0, 'utime': 225.0, 'stime': 53.0, 'cpu_usage': 2.7797, 'memory_free': 38480700.0, 'memory_used': 
# 24338580.0, 'memory_buffers': 4792.0, 'memory_cached': 2727720.0, 'memory_active': 15931396.0, 'memory_inactive': 
# 0.0, 'disk_read_bytes': 380928.0, 'disk_write_bytes': 10088448.0, 'disk_space_total_gb': 5635.25, 'disk_space_used_gb': 
# 3405.11, 'disk_space_free_gb': 2230.14, 'net_recv_bytes': 8066.0, 'net_sent_bytes': 8593.0, 'gpu_usage': 0.29, 'gpu_vram': 
# 998.0, 'gpu_utilized': 1.0},
#     'cloud_info': {
#         'vendor': 'unknown',
#         'instance_type': 'unknown',
#         'region': 'unknown',
#         'discovery_time': 1.0617177486419678
#     },
#     'server_info': {
#         'vcpus': 12,
#         'memory_mb': 64015.42,
#         'gpu_count': 1,
#         'gpu_names': ['Quadro T1000'],
#         'gpu_memory_mb': 4096.0
#     },
#     'stats': {
#         'cpu_usage': {'mean': 1.42, 'max': 6.11},
#         'memory_usage': {'mean': 342509.0, 'max': 591621.0},
#         'gpu_usage': {'mean': 0.0, 'max': 0.0},
#         'gpu_vram': {'mean': 0.0, 'max': 0.0},
#         'gpu_utilized': {'mean': 0.0, 'max': 0.0},
#         'disk_usage': {'max': 3405.11},
#         'traffic': {'inbound': 77383.0, 'outbound': 58481.0},
#         'duration': 9.89
#     },
#     'historical_stats': {
#         'available': True,
#         'runs_analyzed': 5,
#         'avg_cpu_mean': 1.52,
#         'max_memory_max': 597372.0,
#         'avg_gpu_mean': 0.0,
#         'max_vram_max': 0.0,
#         'max_gpu_count': 0.0,
#         'avg_duration': 10.2
#     }
# }

Find more examples in the examples directory, including multiple Metaflow flows with different resource usage patterns, e.g. GPU jobs as well.

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