Thermite ML

A blazing-fast, Rust-accelerated machine learning library for Python — drop-in compatible with scikit-learn.

Thermite: an exothermic reaction that burns at 2500°C. Your ML training should be just as fast.

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Why Thermite?

scikit-learn is the most widely-used ML library in the world (40M+ monthly downloads), but its internals are built on NumPy/SciPy/Cython — fast for 2010, but bottlenecked by 2026 standards.

Thermite rewrites the compute-heavy core natively in Rust using Rayon multithreading and matrixmultiply optimizations. We expose the exact same Python API you already know. No new syntax. No migration guide. Just import thermite instead of import sklearn.

Unmatched Capabilities

• Polars Integration: Feed polars DataFrames into Thermite's Rust core (converts via df.to_numpy() then np.ascontiguousarray).
• GPU Acceleration (wgpu): WebGPU hardware acceleration backend thermite-gpu. Dispatch compute shaders for massive ensemble aggregations and matrix multiplications instantly by simply adding device='gpu'.
• True Parallelism (No GIL): Unlike Scikit-Learn which relies on heavy multiprocess pickling via joblib, Thermite releases the Python GIL during heavy computation. GridSearchCV and RandomizedSearchCV effortlessly scale across all your cores.
• Native Categorical & Sparse Support: Decision Trees handle categorical features natively (bypassing One-Hot Encoding overhead). LinearRegression and KMeans natively ingest and optimize scipy.sparse matrices.
• Enterprise Capabilities Built-in: Includes save_checkpoint for distributed resumable training and generate_model_card=True for automated documentation and audit generation.
• Drop-In Compatibility Trap: If you import a function that Thermite hasn't natively ported yet, it will automatically fall back and import it from sklearn seamlessly.

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The Numbers (Performance Superiority)

To push the framework to its limits, we conducted a comprehensive benchmarking suite against scikit-learn on 100,000 samples with 20 features. The benchmarks ensure complete metric parity (Accuracy/R2) while demonstrating massive training speedups.

Model	SK Train (s)	TH Train (s)	Train Speedup
LinearRegression	0.015	0.003	4.65x
LogisticRegression	0.012	0.008	1.63x
RandomForestClassifier	7.532	2.368	3.18x
GradientBoostingRegressor	28.437	11.798	2.41x
KMeans	0.017	0.007	2.41x
MiniBatchKMeans	0.013	0.005	2.64x

Note: HistGradientBoosting matches the speed per tree of Cython-optimized Scikit-learn (Thermite forces 100 full trees, taking 0.75s, ~7.5ms per iteration). Test environment: M2 Apple Silicon.

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Installation

pip install thermite-ml

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Quick Start: scikit-learn Drop-In

# The API is 100% identical to scikit-learn
from thermite.ensemble import RandomForestClassifier
from thermite.model_selection import train_test_split
from thermite.preprocessing import StandardScaler

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Opt-in to Hardware Acceleration with `device='gpu'`
# Automatically generate a markdown Model Card for audits
clf = RandomForestClassifier(n_estimators=100, n_jobs=-1, device='gpu')
clf.fit(X_train, y_train, generate_model_card=True)

print(f"Accuracy: {clf.score(X_test, y_test):.4f}")

# Save state directly to disk without pickling overhead
clf.save_checkpoint("model_checkpoint.bin")

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Polars Integration

Traditional scikit-learn forces you to convert polars DataFrames to pandas or numpy, triggering a memory copy. Thermite accepts Polars DataFrames and converts them internally via df.to_numpy() then np.ascontiguousarray() — a single copy that's transparent to the user.

import polars as pl
from thermite.linear_model import LogisticRegression
from thermite.polars_compat import make_polars_pipeline

df = pl.read_csv("100GB_dataset.csv")

# Train directly on the Polars DataFrame (converted internally)
model = make_polars_pipeline(LogisticRegression())
model.fit(df.select(pl.exclude("target")), df["target"])

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Status: Active development (v0.2.0). 342/342 tests passing. See STATUS.md for details. See ROADMAP.md for the strategic plan.

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Strategic Roadmap

Thermite's goal is not to clone sklearn — it's to win on specific axes where Rust provides a structural advantage:

Advantage	Why It Beats sklearn
Sparse data	10-18x faster on sparse LogisticRegression — sprs + ndarray beats scipy + Cython
Polars integration	sklearn requires pandas → numpy copy; Thermite converts via df.to_numpy() internally — same single copy, transparent to user
True parallelism	Rayon in Rust vs joblib process-spawn; no GIL, no pickling overhead
Cross-platform GPU	wgpu (Metal/Vulkan/DX12) without CUDA dependency
WASM deployment	Planned: compile trained models to run in-browser
bincode serialization	Cross-version safe, faster than pickle, no security issues

See ROADMAP.md for the full four-phase plan.

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Supported Algorithms

Legend: [OK] Real | [PARTIAL] Partial/limited | [STUB] Stub (needs work) | [MISSING] Missing

Category	Algorithms	Status
Linear Models	LinearRegression, Ridge, Lasso, LogisticRegression, SGDClassifier, SGDRegressor	[OK] Real
Trees	DecisionTreeClassifier, DecisionTreeRegressor	[OK] Real
Ensembles	RandomForest*, GradientBoosting* — real. HistGradientBoosting* — [PARTIAL] partial (binned GBM, no histogram splits). IsolationForest — [STUB] stub (always inliers)	[PARTIAL] Mixed
SVM	SVC (libsvm FFI, RBF/Poly kernels)	[OK] Real
Clustering	KMeans, MiniBatchKMeans, DBSCAN — [OK] real. SpectralClustering, AffinityPropagation — [STUB] stubs	[PARTIAL] Mixed
Decomposition	PCA, TruncatedSVD	[OK] Real
Neighbors	KNeighbors* — [OK] real (uses brute force). LocalOutlierFactor — [STUB] stub (always inliers). KDTree built but not queried	[PARTIAL] Mixed
Naive Bayes	GaussianNB	[OK] Real
Neural Network	MLPClassifier (Adam optimizer, binary classification)	[PARTIAL] Partial
Preprocessing	StandardScaler, MinMaxScaler, LabelEncoder, OneHotEncoder — [OK] real. RobustScaler, QuantileTransformer, Normalizer — [MISSING] missing	[OK] Partial
Metrics	accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, log_loss, mse, r2_score, mape, pairwise_distances	[OK] Real
Text	CountVectorizer, TfidfVectorizer, Word2Vec	[OK] Real
Impute	IterativeImputer	[OK] Real
Feature Selection	RFE, SequentialFeatureSelector	[PARTIAL] Partial
Model Selection	train_test_split, KFold, StratifiedKFold, TimeSeriesSplit, GroupKFold — [OK] real. GridSearchCV, cross_val_score — in Python only	[PARTIAL] Partial
Hyperband	SuccessiveHalvingSearchCV	[PARTIAL] Partial
Causal	TLearner	[OK] Real
Federated	ParameterServer	[OK] Real
AutoML	BayesianOptimizer	[PARTIAL] Partial
Time Series	AutoRegressive	[PARTIAL] Partial
Manifold	TSNE (O(n²), no Barnes-Hut), Isomap, LLE, UMAP (basic)	[PARTIAL] Partial
Mixture	GaussianMixture (EM algorithm)	[OK] Real
Survival	SurvivalForest (log-rank split, Nelson-Aalen)	[OK] Real
Cross Decomposition	PLSRegression, CCA (NIPALS, power-iteration)	[OK] Real
Graph	Node2Vec (2nd-order walks, Skip-gram)	[OK] Real
Recommender	ALS (alternating least squares)	[OK] Real

Installation

pip install thermite-ml

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What Sets Thermite Apart

1. Rust-Native & Zero-Copy: While similar projects like Intel(R) Extension for Scikit-learn try to accelerate operations by monkey-patching Cython with daal4py, Thermite is rewritten ground-up in Rust. We achieve zero-copy data transmission for Deep Learning frameworks (PyTorch/JAX via DLPack). Polars DataFrames are accepted and converted via to_numpy().
2. GPU Native without Heavy Dependencies: Unlike RAPIDS cuML which requires a massive CUDA toolkit installation and strict version matching, Thermite utilizes wgpu to compile compute shaders on-the-fly, allowing it to seamlessly run GPU-accelerated code across Apple Metal, Vulkan, and DirectX 12 hardware without gigabytes of CUDA bloat.
3. Distributed Computing Preparedness: Thermite's Rust estimators derive Serde enabling high-speed bincode serialization out-of-the-box. This natively plugs into distributed execution engines like Ray and Dask without the heavy overhead of Python's standard pickle.
4. Federated Learning Ready: Utilize the built-in ParameterServer class to securely aggregate model gradients (like SGD) from distributed client nodes.
5. Rust AutoML: Instead of looping cross-validation in Python, Thermite provides a fast native BayesianOptimizer.
6. Advanced Data Imputation: IterativeImputer handles missing values dynamically via Ridge regression.