wehub-resource-sync
92 lines
5.2 KiB
Markdown
92 lines
5.2 KiB
Markdown
# MLflow Tracing: An Open-Source SDK for Observability and Monitoring GenAI Applications🔍
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[](https://mlflow.org/docs/latest/index.html)
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[](https://github.com/mlflow/mlflow/blob/master/LICENSE.txt)
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[](https://mlflow.org/community/#slack)
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[](https://twitter.com/MLflow)
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MLflow Tracing (`mlflow-tracing`) is an open-source, lightweight Python package that only includes the minimum set of dependencies and functionality
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to instrument your code/models/agents with [MLflow Tracing Feature](https://mlflow.org/docs/latest/tracing). It is designed to be a perfect fit for production environments where you want:
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- **⚡️ Faster Deployment**: The package size and dependencies are significantly smaller than the full MLflow package, allowing for faster deployment times in dynamic environments such as Docker containers, serverless functions, and cloud-based applications.
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- **🔧 Simplified Dependency Management**: A smaller set of dependencies means less work keeping up with dependency updates, security patches, and breaking changes from upstream libraries.
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- **📦 Portability**: With the less number of dependencies, MLflow Tracing can be easily deployed across different environments and platforms, without worrying about compatibility issues.
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- **🔒 Fewer Security Risks**: Each dependency potentially introduces security vulnerabilities. By reducing the number of dependencies, MLflow Tracing minimizes the attack surface and reduces the risk of security breaches.
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## ✨ Features
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- [Automatic Tracing](https://mlflow.org/docs/latest/tracing/integrations/) for AI libraries (OpenAI, LangChain, DSPy, Anthropic, etc...). Follow the link for the full list of supported libraries.
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- [Manual instrumentation APIs](https://mlflow.org/docs/latest/tracing/api/manual-instrumentation) such as `@trace` decorator.
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- [Production Monitoring](https://mlflow.org/docs/latest/tracing/production)
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- Other tracing APIs such as `mlflow.set_trace_tag`, `mlflow.search_traces`, etc.
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## 🌐 Choose Backend
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The MLflow Trace package is designed to work with a remote hosted MLflow server as a backend. This allows you to log your traces to a central location, making it easier to manage and analyze your traces. There are several different options for hosting your MLflow server, including:
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- [Databricks](https://docs.databricks.com/machine-learning/mlflow/managed-mlflow.html) - Databricks offers a FREE, fully managed MLflow server as a part of their platform. This is the easiest way to get started with MLflow tracing, without having to set up any infrastructure.
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- [Amazon SageMaker](https://aws.amazon.com/sagemaker-ai/experiments/) - MLflow on Amazon SageMaker is a fully managed service offered as part of the SageMaker platform by AWS, including tracing and other MLflow features such as model registry.
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- [Nebius](https://nebius.com/) - Nebius, a cutting-edge cloud platform for GenAI explorers, offers a fully managed MLflow server.
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- [Self-hosting](https://mlflow.org/docs/latest/tracking) - MLflow is a fully open-source project, allowing you to self-host your own MLflow server and keep your data private. This is a great option if you want to have full control over your data and infrastructure.
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## 🚀 Getting Started
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### Installation
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To install the MLflow Python package, run the following command:
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```bash
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pip install mlflow-tracing
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```
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To install from the source code, run the following command:
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```bash
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pip install git+https://github.com/mlflow/mlflow.git#subdirectory=libs/tracing
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```
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> **NOTE:** It is **not** recommended to co-install this package with the full MLflow package together, as it may cause version mismatches issues.
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### Connect to the MLflow Server
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To connect to your MLflow server to log your traces, set the `MLFLOW_TRACKING_URI` environment variable or use the `mlflow.set_tracking_uri` function:
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```python
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import mlflow
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mlflow.set_tracking_uri("databricks")
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# Specify the experiment to log the traces to
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mlflow.set_experiment("/Path/To/Experiment")
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```
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### Start Logging Traces
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```python
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import openai
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client = openai.OpenAI(api_key="<your-api-key>")
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# Enable auto-tracing for OpenAI
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mlflow.openai.autolog()
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# Call the OpenAI API as usual
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response = client.chat.completions.create(
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model="gpt-4.1-mini",
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messages=[{"role": "user", "content": "Hello, how are you?"}],
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)
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```
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## 📘 Documentation
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Official documentation for MLflow Tracing can be found at [here](https://mlflow.org/docs/latest/tracing).
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## 🛑 Features _Not_ Included
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The following MLflow features are not included in this package.
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- MLflow tracking server and UI.
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- MLflow's other tracking capabilities such as Runs, Model Registry, Projects, etc.
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- Evaluate models/agents and log evaluation results.
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To leverage the full feature set of MLflow, install the full package by running `pip install mlflow`.
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