wandb/client
Client
This project is a collection of utilities designed for machine learning experiment tracking, data versioning, and the observability of large language model applications. It provides a client for recording hyperparameters and metrics during training to visualize performance trends and compare different model versions.
The tool includes a model evaluation framework that uses custom scorers and automated judges to assess the quality of generated text outputs. It also provides observability tools to monitor and debug the execution flow and runtime behavior of language model applications.
The system manages the broader machine learning lifecycle, covering the process of training, fine-tuning, and deploying models. This includes tracking dataset changes across iterations to maintain data lineage and providing the infrastructure to host experiment tracking platforms on cloud or private environments.
Features
- Experiment Tracking - Provides comprehensive tools for logging, versioning, and visualizing machine learning model training and evaluation workflows.
- Data Lineage - Tracks dataset changes and transformation history to maintain a clear record of data lineage for reproducibility.
- LLM Observability - Provides monitoring and tracing tools specifically designed to debug execution flows and runtime behavior in LLM applications.
- Model Evaluation Frameworks - Ships a framework for running model inference and validation using custom scorers and automated judges.
- Artifact Versioning - Provides artifact versioning for binary models and datasets using content hashes to ensure training reproducibility.
- LLM Evaluation - Measures the quality of generated text outputs using custom metrics and automated judges.
- Model Lifecycle Management - Manages the end-to-end lifecycle of machine learning models from initial training and fine-tuning to production deployment.
- Dynamic Schema Storage - Implements dynamic schema storage allowing users to log arbitrary key-value pairs and nested dictionaries for experiments.
- Telemetry Data Pipelines - Ships a telemetry data pipeline using REST-based HTTP requests to transmit model performance data to a centralized backend.
- Metric Streams - Captures runtime performance markers as a stream of events for real-time training visualization.
- Experiment Management - Visualizes and tracks machine learning experiment metrics and logs.
- Deep Learning Implementations - Client library for experiment tracking and visualization.
