accelera-ai

Accelera

Accelera is a hybrid Python/C++ machine learning framework for building graph-based pipelines, running independent branches in parallel, generating HTML reports, and experimenting with automated preprocessing and loop parallelization.

Features

• Graph ML pipelines: build DAG-style workflows with preprocessing, model, predict, metric, merge, and branch nodes.
• Parallel branch execution: compare multiple preprocessing/model/metric combinations in one pipeline run through the C++ graph backend.
• Custom model support: plug in sklearn-compatible estimators or extend CustomClassifier, CustomRegressor, CustomClusterer, and CustomTransformer.
• Reporting: generate graph visualizations and HTML metric reports through GraphReport, ModelReport, and AutoML preprocessing reports.
• Auto preprocessing: tabular, text, image-classification, and segmentation preprocessing utilities with saved preprocessors and visual summaries.
• Dataset retriever: list and download shared CSV datasets into a local cache with accelera.src.utils.dataset_retriever.DatasetRetriever.
• C/C++ code parallelizer: extract loops with Clang AST, derive loop features, call classifier/generator services, and inject OpenMP pragmas into parallelizable for loops. This module is Linux-only.
• Benchmark backend prototype: Express/MongoDB backend scaffolding for benchmarks, users, metrics, and submissions.

Current Status

• The core DAG pipeline, custom estimator interfaces, reports, dataset retrieval, and preprocessing utilities are implemented in this repo.
• The AutoML search agent API exists, but the default search algorithm is still a placeholder.
• The benchmark backend is an early prototype.
• The code parallelizer requires Linux, LLVM/Clang, built pybind bindings, and the remote classifier/generator endpoints configured in accelera/src/config.py.

Quick Start

git clone https://github.com/Mohamed-Ashraf273/accelera.git
cd accelera

python3 -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt
pip install psutil requests gdown graphviz

export PYTHONPATH="$PWD"

# Linux only, required before CMake if you want to build code-parallelizer
# bindings and also because the current Linux CMake config expects LLVM.
sudo bash shell/install_llvm.sh 18

cmake -S . -B build
cmake --build build -j"$(nproc)"

Run Examples

# Parallel sklearn-vs-Accelera pipeline comparison
python examples/sklearn_comp.py

# Full branching pipeline demo with a custom PyTorch classifier and reports
python examples/demo.py

# Run tests
pytest accelera

For notebooks, open examples/dataset_retriever_demo.ipynb, examples/code_optimizer_demo.ipynb, examples/autopreprocessing-classification-v3.ipynb, or examples/segmentation-training-gp.ipynb after exporting PYTHONPATH.

Minimal Usage

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler

from accelera.src.accelera_pipe.core.pipeline import Pipeline

X, y = make_classification(
    n_samples=5000,
    n_features=20,
    n_informative=15,
    random_state=42,
)
X_test, y_test = X[:200], y[:200]

pipe = Pipeline()
pipe.branch(
    "preprocessing",
    pipe.preprocess("standard", StandardScaler(), branch=True),
    pipe.preprocess("minmax", MinMaxScaler(), branch=True),
).model(
    "logreg",
    LogisticRegression(max_iter=1000),
).predict(
    "predict",
    test_data=X_test,
).metric(
    "accuracy",
    "accuracy_score",
    y_true=y_test,
)

predictions, executed_graph = pipe(X, y, select_strategy="max")
best_result = executed_graph(X_test, y_test)
print(predictions)
print(best_result)

More Usage Examples

Dataset Retriever

from accelera.src.utils.dataset_retriever import retriever

print(retriever.available_datasets())

retriever.connect()
housing_df = retriever.retrieve_dataset("Housing", df=True)
print(housing_df.head())
retriever.close()

Tabular Auto Preprocessing

from accelera.src.automl.core.classical_training_preprocessing import (
    ClassicalTrainingPreprocessing,
)
from accelera.src.utils.dataset_retriever import retriever

retriever.connect()
df = retriever.retrieve_dataset("Titanic-Dataset", df=True)

preprocessor = ClassicalTrainingPreprocessing(
    df,
    target_col="Survived",
    problem_type="classification",
    folder_path="./titanic_preprocessing_report",
)
X_train, y_train, X_val, y_val = preprocessor.common_preprocessing()

retriever.close()

Text Auto Preprocessing

import pandas as pd

from accelera.src.automl.core.text_training_preprocessing import (
    TextTrainingPreprocessing,
)

reviews_df = pd.DataFrame(
    {
        "review": ["Great product", "Very bad experience", "I like it"],
        "class": [1, 0, 1],
    }
)

text_preprocessor = TextTrainingPreprocessing(
    reviews_df,
    target_col="class",
    text_col="review",
    folder_path="./reviews_report",
)
X_train, y_train, X_val, y_val = text_preprocessor.common_preprocessing()

Image Auto Preprocessing

from accelera.src.automl.core.classification_image_training_preprocessing import (
    ClassificationImageTrainingPreprocessing,
)

image_preprocessor = ClassificationImageTrainingPreprocessing(
    training_folder_images="./PetImages",  # replace with your class folders
    folder_path="./PetImagesReport",
    split_training=True,
    val_size=0.2,
    images_size=(224, 224),
    augment=True,
)
training_loader, validation_loader = image_preprocessor.common_preprocessing()

Pipeline Graph Report

from accelera.src.utils.accelera_utils import serialize
from accelera.src.accelera_pipe.wrappers.graph_report import GraphReport

predictions, executed_graph = pipe(X, y, select_strategy="max")
serialize(pipe, "pipeline.xml")

report = GraphReport("pipeline_report", "pipeline.xml", predictions)
report.execute()

Standalone Model Report

from sklearn.metrics import accuracy_score

from accelera.src.accelera_pipe.wrappers.model_report import ModelReport

accuracy = accuracy_score(y_test, model.predict(X_test))
results = [
    {
        "metric name": "accuracy",
        "result": accuracy,
        "plot_func": None,
        "labels_name": None,
        "headers_name": None,
    }
]

report = ModelReport("model_report", results=results)
report.execute()

C/C++ Loop Parallelization

from accelera.src.utils.parallelizer import parallelizer

parallelizer.parallelize("examples/test_loops.c")
# Writes examples/parallelized_test_loops.c

Project Map

accelera/
├── accelera/
│   ├── api/                 # generated public API modules
│   ├── bindings/            # pybind11 bindings
│   └── src/
│       ├── accelera_pipe/   # DAG pipeline, execution graph
│       ├── automl/          # preprocessing, reports, AutoML agent scaffold
│       ├── benchmark/       # Node.js backend prototype
│       ├── custom/          # estimator base classes
│       ├── utils/           # dataset retriever, parallelizer and code utilities
│       └── wrappers/        # HTML/report helpers
├── src/                     # C++ core, nodes, AST, and utility sources
├── include/                 # C++ headers
├── examples/                # scripts and notebooks
├── docs/                    # MkDocs documentation
├── shell/                   # setup scripts
└── CMakeLists.txt

Useful Commands

# Regenerate API exports after changing Python modules
python api_gen.py

# Run formatting/lint hooks
pre-commit run --all-files --hook-stage manual

# Serve docs locally
mkdocs serve

License

Apache License 2.0. See LICENSE.