Crate aprender 

Aprender — Next-generation ML framework in pure Rust.

This facade crate re-exports aprender-core so that use aprender::* works whether you depend on aprender or aprender-core directly.

Install the CLI: cargo install aprender

Modules

active_learning

Active Learning strategies for label-efficient training.

autograd

Reverse-mode automatic differentiation engine for neural network training.

automl

Automated Machine Learning (AutoML) module.

bayesian

Bayesian inference and probability methods.

bench

Model evaluation and benchmarking framework (spec §7.10) Model Evaluation and Benchmarking Framework (aprender::bench)

bench_viz

Benchmark visualization with rich colors and scientific statistics (PAR-040)

bundle

Model Bundling and Memory Paging

cache

Model Cache and Registry

calibration

Model calibration for confidence estimation.

chaos

Chaos Engineering Configuration

citl

Compiler-in-the-Loop Learning (CITL) for transpiler support. Compiler-in-the-Loop Learning (CITL) module.

classification

Classification algorithms.

cluster

Clustering algorithms.

code

Code Analysis and Code2Vec Embeddings

compute

Compute infrastructure integration (trueno 0.8.7+) Compute Infrastructure Integration (trueno 0.8.8+)

data

DataFrame module for named column containers.

decomposition

Dimensionality reduction and matrix decomposition algorithms.

demo

End-to-end demo infrastructure for Qwen2-0.5B WASM demo (spec §J) End-to-End Demo Module

embed

Data embedding with test data and tiny model representations (spec §4) Data Embedding Module (spec §4)

ensemble

Mixture of Experts (MoE) ensemble learning (GH-101)

error

Error types for Aprender operations.

explainable

Explainability wrappers for inference monitoring Explainable AI Integration for Inference Monitoring

format

Aprender Model Format (.apr)

glm

Generalized Linear Models (GLM)

gnn

Graph Neural Network layers for learning on graph-structured data.

graph

Graph construction and analysis with cache-optimized CSR representation.

hf_hub

Hugging Face Hub integration (GH-100) Hugging Face Hub Integration (GH-100, APR-PUB-001)

index

Indexing data structures for efficient nearest neighbor search.

inspect

Model inspection tooling (spec §7.2) Model Inspection Tooling

interpret

Model Interpretability and Explainability.

linear_model

Linear models for regression.

loading

Model loading subsystem with WCET and cryptographic agility (spec §7.1) APR Loading Subsystem

logic

TensorLogic: Neuro-symbolic reasoning via tensor operations (Domingos, 2025) TensorLogic: Neuro-Symbolic Reasoning via Tensor Operations

loss

Loss functions for training machine learning models.

metaheuristics

Derivative-free global optimization (metaheuristics).

metrics

Evaluation metrics for ML models.

mining

Pattern mining algorithms for association rule discovery.

model_selection

Model selection utilities for cross-validation and train/test splitting.

models

Pre-trained model architectures (Qwen2, etc.) Pre-trained model architectures for inference.

monte_carlo

Monte Carlo Simulation Framework

native

SIMD-native model format for zero-copy Trueno inference (spec §5) SIMD-Native Model Format (spec §5)

nn

Neural network modules for deep learning.

online

Online learning and dynamic retraining infrastructure Online Learning Infrastructure for Dynamic Model Retraining

optim

Optimization algorithms for gradient-based learning.

prelude

Convenience re-exports for common usage.

preprocessing

Preprocessing transformers for data standardization and normalization.

primitives

Core compute primitives (Vector, Matrix).

pruning

Neural network pruning: importance scoring, sparsity masks, and compression. Neural network pruning module.

qa

Model Quality Assurance module (spec §7.9) Model Quality Assurance Module (aprender::qa)

recommend

Recommendation systems.

regularization

Regularization techniques for neural network training.

scoring

100-point model quality scoring system (spec §7) 100-Point Model Quality Scoring System (spec §7)

serialization

Model Serialization Module

showcase

GPU Inference Showcase with PMAT verification (PAR-040)

speech

Speech processing: VAD, ASR, TTS, diarization (spec §5, GH-133) Speech processing module for ASR, TTS, VAD, and diarization.

stack

Sovereign AI Stack integration types (spec §9) Sovereign AI Stack Integration (spec §9)

stats

Traditional descriptive statistics for vector data.

synthetic

Synthetic Data Generation for AutoML.

text

Text processing and NLP utilities.

time_series

Time series analysis and forecasting.

traits

Core traits for ML estimators and transformers.

transfer

Transfer Learning module for cross-project knowledge sharing.

tree

Decision tree algorithms and ensemble methods.

verify

Pipeline verification & visualization system (APR-VERIFY-001) Pipeline Verification & Visualization System (APR-VERIFY-001)

voice

Voice processing: embeddings, style transfer, cloning (GH-132) Voice processing module (GH-132).

wasm

WASM/SIMD integration for browser-based inference (spec §L) WASM/SIMD Integration Module

weak_supervision

Weak Supervision and Label Model.

zoo

Model zoo protocol for sharing and discovery (spec §8) Model Zoo Protocol (spec §8)

Structs

Matrix

A 2D matrix of floating-point values (row-major storage).

Vector

A 1D vector of floating-point values.

Enums

AprenderError

Main error type for Aprender operations.

Traits

Estimator

Primary trait for supervised learning estimators.

Transformer

Trait for data transformers (scalers, encoders, etc.).

UnsupervisedEstimator

Trait for unsupervised learning models.

Type Aliases

Result

Convenience type alias for Results.