Automated telemetry and monitoring for ML & LLM Frameworks
Project description
Welcome to xpuls.ai 👋
MLMonitor - Automatic Instrumentation for ML Frameworks
Roadmap 🚀
| Framework | Status |
|---|---|
| Langchain | ✅ |
| LLamaIndex | Planned |
| PyTorch | Planned |
| SKLearn | Planned |
| Transformers | Planned |
| Stable Diffusion | Next |
💡 If support of any framework/feature is useful for you, please feel free to reach out to us via Discord or Github Discussions
🔗 Installation
- Install from PyPI
pip install xpuls-mlmonitor
🧩 Usage Example
from xpuls.mlmonitor.langchain.instrument import LangchainTelemetry
import os
# Enable this for advance tracking with our xpuls-ml platform
os.environ["XPULSAI_TRACING_ENABLED"] = "true"
# Add default labels that will be added to all captured metrics
default_labels = {"service": "ml-project-service", "k8s_cluster": "app0", "namespace": "dev", "agent_name": "fallback_value"}
# Enable the auto-telemetry
LangchainTelemetry(
default_labels=default_labels,
xpuls_host_url="http://app.xpuls.ai" # Optional param, required when XPULSAI_TRACING is enabled
).auto_instrument()
## [Optional] Override labels for scope of decorator [Useful if you have multiple scopes where you need to override the default label values]
@TelemetryOverrideLabels(agent_name="chat_agent_alpha")
def get_response_using_agent_alpha(prompt, query):
agent = initialize_agent(llm=chat_model,
verbose=True,
agent=CONVERSATIONAL_REACT_DESCRIPTION,
memory=memory)
res = agent.run(f"{prompt}. \n Query: {query}")
ℹ️ Complete Usage Guides
🧾 License
This project is licensed under the Apache License 2.0. See the LICENSE file for more details.
📢 Contributing
We welcome contributions to MLMonitor! If you're interested in contributing.
If you encounter any issues or have feature requests, please file an issue on our GitHub repository.
💬 Get in touch
🐦 Follow the latest from xpuls.ai team on Twitter @xpulsai
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