aagnone3

FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7

# System-related
WORKDIR /app
COPY . .

# Install package
RUN pip3 install --upgrade pip && \
    python3 setup.py install

aagnone3

from fastapi import FastAPI

from fastapi_sentiment import sentiment_analysis
from fastapi_sentiment.models import (
    SentimentDocument,
    SentimentScoring,
    Message
)

app = FastAPI()

aagnone3

# get predictions from the models for the same data
y_pred = clf.predict(X_test)
y_pred_cpu = clf_cpu.predict(X_test)
y_pred_gpu = clf_gpu.predict(X_test)


def plot_distributions(data_label_pairs, title='Score Distribution(s)'):
    """
    Plot the distribution of a list of prediction vectors.

aagnone3

%%timeit
clf_gpu.predict(X_test)

aagnone3

%%timeit
clf_cpu.predict(X_test)

aagnone3

%%timeit
clf.predict(X_test)

aagnone3

print('Creating CPU Hummingbird model\n\t', end='')
clf_cpu = compile_pytorch(clf, force_cpu=True)

print('Creating GPU Hummingbird model\n\t', end='')
clf_gpu = compile_pytorch(clf)

aagnone3

def compile_pytorch(model, force_cpu=False):
    """
    Use Hummingbird to compile a PyTorch model, moving it to a GPU if available.

    @param Model model: trained ML model which has a predict() method for inference.
    @param bool force_cpu: whether to keep the model on CPU, regardless of the presence of a GPU.
    @return Model: augmented ML model, which uses tensor computations to perform inference
    """
    model = convert(clf, 'pytorch', extra_config={"tree_implementation":"gemm"})
    if torch.cuda.is_available() and not force_cpu:

aagnone3

X_train, y_train = make_data(n_samples=1e3)
X_test, y_test = make_data(n_samples=1e4)

clf = RandomForestRegressor(n_estimators=250, max_depth=10)
clf.fit(X_train, y_train)

aagnone3

def make_data(n_samples=1000):
    """
    Return some synthetic classification data from sklearn.datasets.

    @param int n_samples: number of samples the data set should have.
    @param int n_features: number of features the data set should have.
    @return tuple:
        [0]: numpy.ndarray data set of shape (n_samples, n_features)
        [1]: numpu.ndarray labels of shape (n_samples, )
    """