qxgb
QXGBoost: quantized XGBoost Classifier for FPGA Deployment.
qxgb provides QXGBClassifier inheriting from xgboost.XGBClassifier with quantization aware training and registers the Alkaid tracer plugin for converting trained (Q)XGBoost boosters into RTL designs with Alkaid.
Installation
pip install qxgb
Quick Start
from qxgb import QXGBClassifier
model = QXGBClassifier(
scale=4,
bias=2,
n_estimators=32,
max_depth=3,
)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
hw_bst = model.ibooster() # Integer booster
from alkaid.converter import trace_model
from alkaid.trace import FVArray, trace
inp = FVArray.new(16).quantize(0, 8, 0).as_new()
_, out = trace_model(hw_bst, inputs=inp, mode='mux')
comb = trace(inp, out)
hw_pred = comb.predict(X_test) # == y_pred
Examples
Notebook examples are in examples/:
jsc.ipynbmnist.ipynbnid.ipynb
License
LGPLv3. See LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for qxgb 0.1.0
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