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Add deepaas-execute command
This commit includes a new deepaas-execute command that allows to directly execute the models from the commandline, rather than spawning a server and making HTTP requests. This is useful for running jobs on HPC and HTC facilities.
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‎deepaas/cmd/execute.py

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#!/usr/bin/env python -*- coding: utf-8 -*-
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# Copyright 2020 Spanish National Research Council (CSIC)
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import mimetypes
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import os
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import shutil
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import sys
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from oslo_config import cfg
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from oslo_log import log
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from deepaas.model import loading
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from deepaas.model.v2.wrapper import UploadedFile
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cli_opts = [
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cfg.StrOpt('model-name',
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help="""
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Add the name of the model from which you want
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to obtain the prediction.
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If there are multiple models installed and youd don't
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specify the name of the one you want to use the program will fail.
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If there is only one model installed, that will be used
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to make the prediction.
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"""),
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cfg.StrOpt('input-file',
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short="i",
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help="""
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Set local input file to predict.
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"""),
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cfg.StrOpt('content-type',
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default='application/json',
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short='ct',
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help="""
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Especify the content type of the output file. The selected
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option must be available in the model used.
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(by default application/json).
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"""),
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cfg.StrOpt('output',
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short="o",
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help="""
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Save the result to a local file.
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"""),
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cfg.BoolOpt('url',
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short='u',
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default=False,
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help="""
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Run as input file an URL.
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If this option is set to True, we can use the URL
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of an image as an input file.
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"""),
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]
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CONF = cfg.CONF
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CONF.register_cli_opts(cli_opts)
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LOG = log.getLogger(__name__)
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# Loading the model installed
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def get_model_name():
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model_name = CONF.model_name
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models = loading.get_available_models("v2")
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if model_name:
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model_obj = models.get(model_name)
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if model_obj is None:
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sys.stderr.write(
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"ERROR: The model {} is not available.\n".format(model_name))
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sys.exit(1)
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return model_name, model_obj
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elif len(models) == 1:
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return models.popitem()
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else:
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sys.stderr.write(
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'ERROR: There are several models available ({}) '
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'you have to choose one.\n'.format(list(models.keys())))
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sys.exit(1)
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def prediction(input_file, file_type, content_type):
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model_name, model_obj = get_model_name()
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predict_data = model_obj.predict_data
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predict_url = model_obj.predict_url
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if file_type is True:
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input_data = {'urls': [input_file], 'accept': content_type}
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output_pred = predict_url(input_data)
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else:
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content_type_in, fileEncoding = mimetypes.guess_type(input_file)
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file = UploadedFile(name=input_file,
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filename=input_file,
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content_type=content_type_in)
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input_data = {'files': [file], 'accept': content_type}
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output_pred = predict_data(input_data)
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return (output_pred)
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def main():
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cfg.CONF(sys.argv[1:])
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input_file = CONF.input_file
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content_type = CONF.content_type
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file_type = CONF.url
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output = CONF.output
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# Checking required argument
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if input_file is None:
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sys.stderr.write(
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"ERROR: Option input_file is required.\n")
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sys.exit(1)
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if output is None:
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sys.stderr.write(
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"ERROR: Option output is required.\n")
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sys.exit(1)
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output_pred = prediction(input_file, file_type, content_type)
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extension = mimetypes.guess_extension(content_type)
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if extension is None or output_pred is None:
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sys.stderr.write(
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"ERROR: Content type {} not valid.\n".format(content_type))
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sys.exit(1)
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if extension == ".json":
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name_image = os.path.splitext(os.path.basename(input_file))[0]
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out_file_name = "out_" + name_image
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f = open(out_file_name + ".json", "w+")
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f.write(repr(output_pred) + '\n')
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f.close()
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if not os.path.exists(output): # Create path if does not exist
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os.makedirs(output)
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dir_name = output + f.name
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shutil.move(f.name, os.path.join(output, f.name))
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else:
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output_path_image = output_pred.name
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dir_name = output + os.path.basename(output_path_image)
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if not os.path.exists(output): # Create path if does not exist
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os.makedirs(output)
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shutil.copy(output_path_image, output)
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print("Output saved at {}" .format(dir_name))
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if __name__ == "__main__":
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main()

‎deepaas/tests/test_cmd.py

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# under the License.
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import os
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import shutil
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import sys
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import urllib.request
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import mock
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from deepaas.cmd import execute
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from deepaas.cmd import run
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from deepaas.cmd import wsk
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from deepaas.tests import base
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wsk.main()
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m_proxy_main.assert_called_once()
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m_handle_signals.assert_called_once()
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class TestExecute(base.TestCase):
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@mock.patch("deepaas.cmd.execute.prediction")
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def test_execute_data(self, m_out_pred):
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in_file = "file"
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out_file = "deepaas/tests/out_test/"
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self.flags(input_file=in_file)
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self.flags(output=out_file)
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m_out_pred.return_value = [{
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'value1': {'pred': 1}, 'value2': {'pred': 0.9}}]
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with mock.patch.object(sys, 'argv', ["deepaas-predict"]):
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execute.main()
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@mock.patch("deepaas.cmd.execute.prediction")
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def test_execute_url(self, m_out_pred):
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in_file = "https://xxxxxxxxxx"
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out_file = "deepaas/tests/out_test/"
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self.flags(input_file=in_file)
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self.flags(output=out_file)
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self.flags(url=True)
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m_out_pred.return_value = [{
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'value1': {'pred': 1}, 'value2': {'pred': 0.9}}]
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with mock.patch.object(sys, 'argv', ["deepaas-predict"]):
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execute.main()
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@mock.patch("deepaas.cmd.execute.prediction")
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def test_execute_ct(self, m_out_pred):
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in_file = "file"
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out_file = "deepaas/tests/out_test/"
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cont_type = "application/zip"
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self.flags(input_file=in_file)
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self.flags(output=out_file)
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self.flags(content_type=cont_type)
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output_dir = "deepaas/tests/tmp_dir"
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if os.path.exists(output_dir):
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shutil.rmtree(output_dir)
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os.mkdir(output_dir)
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out_json = [{
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'value1': {'pred': 1}, 'value2': {'pred': 0.9}}]
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f_json = open(output_dir + 'output' + ".json", "w+")
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f_json.write(repr(out_json) + '\n')
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f_json.close
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url = ('https://storage.googleapis.com/'
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'tfjs-models/assets/posenet/frisbee.jpg')
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urllib.request.urlretrieve(url, "example_image.jpg")
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shutil.move(f_json.name, output_dir)
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shutil.move("example_image.jpg", output_dir)
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f = shutil.make_archive(base_name=output_dir,
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format='zip',
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root_dir=output_dir)
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m_out_pred.return_value = open(f, 'rb')
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with mock.patch.object(sys, 'argv', ["deepaas-predict"]):
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execute.main()
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shutil.rmtree(output_dir)
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os.remove(output_dir + ".zip")

‎doc/source/cli/deepaas-predict.rst

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===========
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deepaas-predict
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===========
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Synopsis
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========
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:program:`deepaas-predict` [options]
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Description
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===========
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:program:`deepaas-predict` It is a command that allows you to obtain,
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through the command line, the prediction of a file or the url of
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a file, of the models that are loaded through the ``deepaas.v2.models``
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entrypoint API.
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Options
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=======
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.. option:: --input-file INPUT_FILE, -i INPUT_FILE
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Set local input file to predict. This option is required.
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.. option:: --content-type CONTENT_TYPE, -c CONTENT_TYPE
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Especify the content type of the output file. The selected
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option must be available in the model used.
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(by default application/json).
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.. option:: --model-name CONTENT_TYPE, -c CONTENT_TYPE
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Add the name of the model from which you want
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to obtain the prediction.
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If there are multiple models installed and youd don't
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specify the name of the one you want to use the program will fail.
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If there is only one model installed, that will be used
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to make the prediction.
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.. option:: --output OUTPUT_DIR, -o OUTPUT_DIR
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Save the result to a local file. This option is required.
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.. option:: --url, -u
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If set to true, the input file is the url o a file to predict.
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Files
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=====
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None
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See Also
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========
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Documentation: `DEEPaaS API <https://docs.deep-hybrid-datacloud.eu/projects/deepaas/>`_
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Reporting Bugs
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==============
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Bugs are managed at `GitHub <https://github.com/indigo-dc/deepaas>`_
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‎doc/source/user/predict.rst

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.. _predict:
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DEEPaaS API as a command line action
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==================================
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Support for execution from the command line for DEEPaaS. In your Dockerfile,
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you must ensure that you execute `` deepaas-predict`` in one of the following ways:
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Basic form of execution.
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.. code-block:: console
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(...)
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CMD ["sh", "-c", "deepaas-predict -i INPUT_FILE -o OUTPUT_DIR"]
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Execution specifying a content type option.
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.. code-block:: console
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(...)
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CMD ["sh", "-c", "deepaas-predict -i INPUT_FILE -c CONTENT_TYPE -o OUTPUT_DIR"]
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Execution specifying an url as an input file.
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.. code-block:: console
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(...)
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CMD ["sh", "-c", "deepaas-predict -i INPUT_FILE --url -o OUTPUT_DIR"]
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Execution specifying the name of a model.
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.. code-block:: console
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(...)
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CMD ["sh", "-c", "deepaas-predict -i INPUT_FILE --model-name MODEL_NAME -o OUTPUT_DIR"]
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‎setup.cfg

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console_scripts =
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deepaas-run = deepaas.cmd.run:main
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deepaas-wsk = deepaas.cmd.wsk:main
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deepaas-predict = deepaas.cmd.execute:main
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oslo.config.opts =
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deepaas = deepaas.opts:list_opts

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