microsoft/FLAML
FLAML
FLAML is an automated machine learning framework, hyperparameter optimization tool, and large language model agent orchestrator. It provides a system for model selection and tuning across various learners and datasets, while also offering a toolkit for optimizing the inference parameters and fine-tuning settings of large language models.
The project features a meta-learning tuning system that analyzes historical task data to generate data-dependent default configurations, accelerating model convergence. It further enables the design of collaborative multi-agent systems through conversational workflows and event-driven orchestration.
Capabilities cover resource-efficient hyperparameter search for machine learning models and arbitrary Python functions, supporting hierarchical search spaces and lexicographic objective optimization. The framework also includes utilities for automated model selection, stacked ensemble construction, zero-shot configuration, and the enforcement of fairness constraints.
The system supports distributed tuning scaling and concurrent trial execution across compute clusters to reduce total search duration.
Features
- Automated Machine Learning - Provides a comprehensive framework for automated model selection and hyperparameter tuning to maximize performance with minimal manual effort.
- Automated ML (AutoML) - Provides an automated framework for selecting and tuning machine learning models based on dataset statistics.
- Agentic Workflow Orchestration - Provides a system for coordinating multi-agent workflows using an event-driven architecture to handle complex business processes.
- Agent Orchestration Systems - Coordinates collaborative multi-agent systems through conversational workflows and event-driven orchestration.
- Conversational Agent Construction - Offers a shared programming framework for constructing agents capable of stateful, multi-turn conversational interactions.
- AI Agent Orchestrators - Orchestrates collaborative multi-agent systems using conversational workflows and event-driven architectures.
- Hyperparameter Optimization Tools - Searches for optimal configurations for machine learning models and arbitrary Python functions under resource constraints.
- Large Language Model Fine-Tuning - Optimizes inference parameters and fine-tuning settings for pre-trained large language models to balance quality and cost.
- LLM Performance Optimization Libraries - Optimizes inference parameters and fine-tuning settings for large language models to balance performance and cost.
- Meta Learning Frameworks - Analyzes historical task data to generate data-dependent default configurations that accelerate model convergence.
- Meta-Learned Initialization - Accelerates hyperparameter convergence by using historical task data to generate data-dependent starting configurations.
- Data-Dependent Configuration Generation - Uses historical task data to generate data-dependent default hyperparameters that accelerate model training convergence.
- Hyperparameter Search Strategies - Employs cost-effective search strategies to find optimal model configurations under complex resource constraints.
- Hyperparameter Optimization - Provides automated methods for searching and selecting the best configuration parameters for models and Python functions.
- Conditional Search Space Configuration - Implements hierarchical search spaces where specific hyperparameters are only sampled based on the value of parent parameters.
- Blended Search Strategies - Combines multiple optimization algorithms to explore large heterogeneous search spaces under strict time and memory constraints.
- Event-Driven Agent Communications - Manages multi-agent coordination using event-driven communication protocols within a structured conversational workflow.
- Visual Prototyping Interfaces - Provides a web-based visual interface for designing, testing, and iterating on agent workflows without writing code.
- Event-Driven Agent Runtimes - Implements execution environments that manage asynchronous message passing and state transitions for distributed agent architectures.
- Frugality-Focused Evaluation - Prioritizes low-cost hyperparameter regions using frugality-focused algorithms to minimize total evaluation expenditure.
- Custom Pipeline Component Integrations - Allows users to integrate custom learners, metrics, and hierarchical search spaces into the automated ML pipeline.
- Distributed Hyperparameter Tuning - Parallelizes hyperparameter optimization trials across multiple compute nodes and CPU clusters for horizontal scaling.
- Stacking Ensembles - Constructs stacked ensembles where predictions from multiple high-performing base models serve as input for a meta-model.
- External Service Integrations - Connects AI agents to third-party libraries, servers, and specialized APIs through an extensible integration framework.
- Heterogeneous Space Optimizations - Implements efficient search methods for large configuration spaces containing diverse parameter types and complex dependencies.
- Inference Parameter Tuners - Adjusts selection and execution settings for large language models to improve output quality and efficiency.
- Arbitrary Function Optimization - Optimizes hyperparameters for arbitrary Python functions and non-machine learning procedures using custom metrics.
- Multi-Fidelity Schedulers - Manages trial execution with multi-fidelity evaluation and early stopping for underperforming configurations.
- Custom Metric Definitions - Supports the definition of custom scoring mechanisms and success criteria to measure accuracy and error during model search.
- Data-Dependent Configuration Generation - Uses historical tuning data across diverse tasks to generate data-dependent default hyperparameters.
- Hyperparameter Recommendations - Suggests optimal configuration values based on dataset characteristics and historical meta-learning data.
- Meta-Learned Configuration Generators - Analyzes historical tuning data across tasks to generate data-dependent configuration files for learners.
- Meta-Learned Default Wrappers - Automatically applies meta-learned hyperparameter configurations during the model training process.
- Visual Training Configurators - Ships a low-code graphical interface for configuring model training parameters and optimizing machine learning architectures.
- Multi-Fidelity Trial Scheduling - Employs early stopping and resource-limited evaluations to quickly discard poor configurations and minimize computational waste.
- Computationally Efficient Search - Implements search strategies that minimize total compute time and trial counts to find optimal configurations.
- Lexicographic Objectives - Optimizes multiple competing performance goals using a strict priority hierarchy with defined thresholds.
- Warm-Start Initializations - Resumes optimization using previously discovered best hyperparameter configurations to accelerate convergence.
- Zero-Shot Configurations - Applies high-performance, pre-determined hyperparameter configurations to tasks without requiring an active search process.
- Optimization Budget Enforcers - Limits the optimization process based on wall-clock time budgets, trial counts, and metric thresholds.
- Optimization Constraint Enforcement - Enforces mathematical and resource feasibility by restricting search spaces to minimize time, memory, or energy usage.
- Iterative Prompting Frameworks - Simulates conversations between specialized agents and assistants using a structured, iterative prompting framework to solve complex problems.
- Optimization Trials - Distributes parameter search workloads across multiple processes or machines to shorten total search duration.
- Automated Machine Learning - Fast and lightweight library for automated machine learning.
- AutoML - Efficient and economical automated model selection.
- AutoML Frameworks - Fast and lightweight library for automated machine learning.
- Automated Machine Learning - Fast library for automated machine learning and hyperparameter tuning.
