gpt4all/gpt4all-bindings/python/docs/gpt4all_python/monitoring.md
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V3 docs max (#2488)
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Signed-off-by: Max Cembalest <max@nomic.ai>

* v3 docs

Signed-off-by: Max Cembalest <max@nomic.ai>

---------

Signed-off-by: Max Cembalest <max@nomic.ai>
2024-07-01 13:00:14 -04:00

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# GPT4All Monitoring
GPT4All integrates with [OpenLIT](https://github.com/openlit/openlit) open telemetry instrumentation to perform real-time monitoring of your LLM application and hardware.
Monitoring can enhance your GPT4All deployment with auto-generated traces for
- performance metrics
- user interactions
- GPU metrics like utilization, memory, temperature, power usage
## Setup Monitoring
!!! note "Setup Monitoring"
With [OpenLIT](https://github.com/openlit/openlit), you can automatically monitor metrics for your LLM deployment:
```shell
pip install openlit
```
```python
from gpt4all import GPT4All
import openlit
openlit.init() # start
# openlit.init(collect_gpu_stats=True) # or, start with optional GPU monitoring
model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf')
# Start a chat session and send queries
with model.chat_session():
response1 = model.generate(prompt='hello', temp=0)
response2 = model.generate(prompt='write me a short poem', temp=0)
response3 = model.generate(prompt='thank you', temp=0)
print(model.current_chat_session)
```
## OpenLIT UI
Connect to OpenLIT's UI to start exploring performance metrics. Visit the OpenLIT [Quickstart Guide](https://docs.openlit.io/latest/quickstart) for step-by-step details.
## Grafana, DataDog, & Other Integrations
If you use tools like , you can integrate the data collected by OpenLIT. For instructions on setting up these connections, check the OpenLIT [Connections Guide](https://docs.openlit.io/latest/connections/intro).