def test_session_dump_zip(replace: bool):
'''Tests session dump zip'''
def my_transform(x: int, y: int) -> int:
return x + y
model = Model(transform=my_transform)
model_name = 'my-model'
session = AcumosSession()
with tempfile.TemporaryDirectory() as tdir:
model_zip_path = Path(tdir) / f"{model_name}.zip"
session.dump_zip(model, model_name, model_zip_path)
import zipfile
with zipfile.ZipFile(model_zip_path, "r") as model_zip:
assert set(model_zip.namelist()) == set(_REQ_FILES)
if replace is False:
with pytest.raises(AcumosError):
session.dump_zip(model, model_name,
model_zip_path) # file already exists
else:
session.dump_zip(
model, model_name, model_zip_path,
replace=replace) # file already exists but it will be replaced
from acumos.modeling import Model, List, create_dataframe
iris = load_iris()
X = iris.data
y = pd.get_dummies(iris.target).values
clf = Sequential()
clf.add(Dense(3, input_dim=4, activation='relu'))
clf.add(Dense(3, activation='softmax'))
clf.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
clf.fit(X, y)
X_df = pd.DataFrame(
X, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
IrisDataFrame = create_dataframe('IrisDataFrame', X_df)
def classify_iris(df: IrisDataFrame) -> List[int]:
'''Returns an array of iris classifications'''
X = np.column_stack(df)
return clf.predict_classes(X)
model = Model(classify=classify_iris)
session = AcumosSession()
session.dump_zip(model, 'keras', './keras.zip',
replace=True) # creates ./keras.zip
})
print("Epoch {} | Loss {}".format(epoch, loss))
prediction = tf.argmax(logits, 1)
yhat = sess.run([prediction], {x: data})[0]
# note: this predicts on the training set for illustration purposes only
print(classification_report(target, yhat))
# =============================================================================
# create a acumos model from the tensorflow model
# =============================================================================
X_df = pd.DataFrame(
data,
columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
IrisDataFrame = create_dataframe('IrisDataFrame', X_df)
def classify_iris(df: IrisDataFrame) -> List[int]:
'''Returns an array of iris classifications'''
X = np.column_stack(df)
return prediction.eval({x: X}, sess)
model = Model(classify=classify_iris)
session = AcumosSession()
session.dump_zip(model, 'tensorflow', './tensorflow.zip',
replace=True) # creates ./tensorflow.zip
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===============LICENSE_END=========================================================
'''
Dumps an example model for illustrating acumos_model_runner usage
'''
from acumos.session import AcumosSession
from acumos.modeling import Model, new_type
# allow users to specify a "raw" bytes type. no protobuf message is generated here
Image = new_type(bytes, 'Image', {
'dcae_input_name': 'a',
'dcae_output_name': 'a'
}, 'example description')
def image_func(image: Image) -> Image:
'''Return an image'''
return Image(image)
if __name__ == '__main__':
'''Main'''
model = Model(imgae_func=image_func)
session = AcumosSession()
session.dump_zip(model, 'raw', './raw.zip',
replace=True) # creates ./raw.zip
import io
import PIL
from acumos.modeling import Model, create_namedtuple
from acumos.session import AcumosSession
ImageShape = create_namedtuple('ImageShape', [('width', int), ('height', int)])
def get_format(data: bytes) -> str:
'''Returns the format of an image'''
buffer = io.BytesIO(data)
img = PIL.Image.open(buffer)
return img.format
def get_shape(data: bytes) -> ImageShape:
'''Returns the width and height of an image'''
buffer = io.BytesIO(data)
img = PIL.Image.open(buffer)
shape = ImageShape(width=img.width, height=img.height)
return shape
model = Model(get_format=get_format, get_shape=get_shape)
session = AcumosSession()
session.dump_zip(model, 'image-model', './image-model.zip', replace=True) # creates ./image-model.zip