WandMon is a lightweight Python library designed to help you seamlessly monitor the inputs and outputs of your machine learning model in a production environment. Developed by Weights and Biases, it offers the capability to stream the monitoring data directly to your Weights and Biases dashboard.
Wrap any prediction function with a @monitor decorator and WandMon provides real-time tracking of your function calls with minimal interference to your existing pipeline.
- Real-time monitoring: WandMon captures real-time system utilization, call time, and number of calls.
- Flexibility: Easily specify an input preprocessor or output postprocessor to have more control over what gets logged.
- Direct logging to Weights and Biases: By default, WandMon logs all keyword arguments.
- Dashboard integration: WandMon is fully compatible with Weights and Biases' workspaces and integrates with Weave enabling easy analysis and customization.
You can install WandMon via pip:
pip install -e .
To get started with WandMon, simply import the library and use the @monitor decorator for your function. WandMon will automatically log the inputs and outputs of your function to your Weights and Biases account.
See examples for some getting started examples.
from wandmon import monitor
@monitor
def my_function(a, b, c):
...Once you've set up WandMon, calling my_function will automatically stream its inputs and outputs to Weights and Biases:
my_function(1, 2, 3)For more control over what data is logged, you can provide an input_preprocessor or output_postprocessor:
def my_input_preprocessor(kwargs):
return {key: value for key, value in kwargs.items() if key in ["a", "b"]}
def my_output_postprocessor(result):
return {"result": result}
@monitor(input_preprocessor=my_input_preprocessor, output_postprocessor=my_output_postprocessor)
def my_function(a, b, c):
...To view the logged data, visit your Weights and Biases dashboard. The dashboard displays real-time system utilization, call time, and inputs and outputs.
WandMon makes it easier to monitor your production ML models and visualize data to get insights.
We would love to hear your feedback and are open to contributions. Please feel free to open an issue or send a pull request.
pip install -r requirements.test.txtWandMon is under the Apache 2.0.
Whether you're tracking your models' performance over time, comparing different models, or trying to diagnose the cause of a recent crash, WandMon and Weights and Biases have you covered. Let's get your models monitored, understood, and improved!