Quick Start
Install
Install the Graphsignal library by running:
Python
pip install -U graphsignal
GPU profiling (Linux): For CUPTI-based GPU profiling, install the extra matching your CUDA version: pip install graphsignal[cu12] (CUDA 12.x) or pip install graphsignal[cu13] (CUDA 13.x).
Configure
Configure the Graphsignal SDK by specifying your API key directly or via the GRAPHSIGNAL_API_KEY environment variable. To get an API key, sign up for a free account at graphsignal.com. The key can then be found in your account's Settings / API Keys page.
Python
import graphsignal
graphsignal.configure(api_key='my-api-key')
# or pass the API key via the GRAPHSIGNAL_API_KEY environment variable
See the configure() API docs for all configuration parameters.
CLI
Alternatively, you can use the Graphsignal runner when running your application. Pass the API key via the GRAPHSIGNAL_API_KEY environment variable.
graphsignal-run <my-app>
Integrate
Graphsignal works automatically and also provides an API for manual tracing and profiling. It captures performance profiles, traces, metrics, and errors. All insights are available for analysis at app.graphsignal.com.
Refer to the guides below for detailed information on:
See integration documentation for libraries and inference engines:
See the API reference for complete documentation.
Analyze
Log in to Graphsignal to monitor and analyze your application.
Overhead
The SDK has no impact on production performance that needs to be considered. Profiling uses low-overhead profiling APIs, so it runs with minimal impact on your code. Tracing is lightweight—overhead per trace is typically under 100 microseconds.
Troubleshooting
To enable debug logging, add debug_mode=True to configure(). If the debug log doesn’t provide hints for resolving the issue, report it to our support team via your account.
In case of connection issues, please make sure outgoing connections to https://api.graphsignal.com are allowed.