xpuls-mlmonitor 0.1.0

Automated telemetry and monitoring for ML & LLM Frameworks

MLMonitor - Automatic Instrumentation for ML Frameworks

Roadmap

• Prometheus Support for major ML & LLM frameworks
  • Langchain - Done
  • LLamaIndex - Coming Soon
  • SKLearn - Coming Soon
  • transformers - Coming Soon
  • pytorch - Coming Soon

Installation

1. 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}")

Complete Usage Guides

• Langchain Framework + Grafana Template

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.