Graphsignal Logger
Project description
Graphsignal Logger
Overview
Graphsignal is an observability platform for monitoring and troubleshooting production machine learning applications. It helps ML engineers, MLOps teams and data scientists to quickly address issues with data and models as well as proactively analyze model performance and availability. Learn more at graphsignal.ai.
AI Observability
- Model monitoring. Monitor offline and online predictions for data validity and anomalies, data drift and concept drift, prediction latency, exceptions, system metrics and more.
- Automatic issue detection. Graphsignal automatically detects and notifies on issues in data and models, no need to manually setup and maintain complex rules.
- Root cause analysis. Analyse prediction outliers and issue-related samples for faster problem root cause identification.
- Model framework and deployment agnostic. Monitor models serving online, in streaming apps, accessed via APIs or offline, running batch predictions.
- Any scale and data size. Graphsignal logger only sends data statistics and samples allowing it to scale with your application and data.
- Team access. Easily add team members to your account, as many as you need.
Documentation
See full documentation at graphsignal.ai/docs.
Getting Started
Installation
Install the Python logger by running
pip install graphsignal
Or clone and install the GitHub repository.
git clone https://github.com/graphsignal/graphsignal.git
python setup.py install
Import the package in your application
import graphsignal
Configuration
Configure the logger by specifying your API key.
graphsignal.configure(api_key='my_api_key')
To get an API key, sign up for a free account at graphsignal.ai. The key can then be found in your account's Settings / API Keys page.
Logging session
Get logging session for a deployed model identified by deployment name. Multiple sessions can be used in parallel in case of multi-model scrips or servers.
sess = graphsignal.session(deployment_name='model1_prod')
If a model is versioned you can set the version as a model attribute.
Set model attributes.
sess.set_attribute('my attribute', 'value123')
Some system attributes, such as Python version and OS are added automatically.
Prediction Logging
Log single or batch model prediction/inference data. Pass prediction data according to supported data formats using list, dict, pandas.DataFrame or numpy.ndarray.
Computed data statistics such as feature and class distributions are uploaded at certain intervals and on process exit. Additionally, random and outlier prediction instances may be uploaded.
# Examples of input features and output classes.
x = pandas.DataFrame(data=[[0.1, 'A'], [0.2, 'B']], columns=['feature1', 'feature2'])
y = numpy.asarray([[0.2, 0.8], [0.1, 0.9]])
sess.log_prediction(input_data=x, output_data=y)
Track metrics. The last set value is used when metric is aggregated.
sess.log_metric('my_metric', 1.0)
Log any prediction-related event or exception.
sess.log_event(description='My event', attributes={'my_attr': '123'})
Measure prediction latency and record any exceptions.
with sess.measure_latency()
my_model.predict(X)
See prediction logging API reference for full documentation.
Example
import numpy as np
from tensorflow import keras
import graphsignal
# Configure Graphsignal logger
graphsignal.configure(api_key='my_api_key')
# Get logging session for the model
sess = graphsignal.session(deployment_name='mnist_prod')
model = keras.models.load_model('mnist_model.h5')
(_, _), (x_test, _) = keras.datasets.mnist.load_data()
x_test = x_test.astype("float32") / 255
x_test = np.expand_dims(x_test, -1)
# Measure predict call latency
with sess.measure_latency()
output = model.predict(x_test)
# See supported data formats description at
# https://graphsignal.ai/docs/python-logger/supported-data-formats
sess.log_prediction(output_data=output)
# Report a metric
sess.log_metric('my_metric', 1.2)
See more examples.
Performance
When logging predictions, the data is windowed and only when certain time interval or window size conditions are met, data statistics are computed and sent along with a few sample and outlier data instances by the background thread.
Since only data statistics are sent to our servers, there is no limitation on logged data size and it doesn't have a direct effect on logging performance.
Security and Privacy
Graphsignal logger can only open outbound connections to log-api.graphsignal.ai and send data, no inbound connections or commands are possible.
Please make sure to exclude or anonymize any personally identifiable information (PII) when logging model data and events.
Troubleshooting
To enable debug logging, add debug_mode=True to configure(). If the debug log doesn't give you any hints on how to fix a problem, please report it to our support team via your account.
In case of connection issues, please make sure outgoing connections to https://log-api.graphsignal.ai are allowed.
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