def conda_dependencies(self):
"""
Get module conda dependencies
:return: CondaDependencies instance
"""
cd = CondaDependencies()
for c in self._get_value('CondaDependencies/CondaChannels'):
cd.add_channel(c)
for c in self._get_value('CondaDependencies/CondaPackages'):
cd.add_conda_package(c)
for p in self._get_value('CondaDependencies/PipPackages'):
cd.add_pip_package(p)
for p in self._get_value('CondaDependencies/PipOptions'):
cd.set_pip_option(p)
return cd
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.webservice import AciWebservice
from azureml.core.model import Model
from azureml.core import Workspace
import os
ws = Workspace.from_config()
model_name = "net.onnx"
model = Model(workspace=ws, name=model_name)
myenv = CondaDependencies(conda_dependencies_file_path=os.path.join(
os.path.dirname(os.path.realpath(__file__)), '../../',
'conda_dependencies.yml'))
myenv.add_channel("pytorch")
with open("myenv.yml", "w") as f:
f.write(myenv.serialize_to_string())
myenv = Environment.from_conda_specification(name="myenv",
file_path="myenv.yml")
inference_config = InferenceConfig(
entry_script="code_final/deployment/score.py", environment=myenv)
aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,
memory_gb=1,
tags={'demo': 'onnx'},
description='ONNX for text')
def generate_yaml(
directory: str,
ref_filename: str,
needed_libraries: list,
conda_filename: str,
):
"""
Creates a deployment-specific yaml file as a subset of
the image classification environment.yml
Also adds extra libraries, if not present in environment.yml
Args:
directory (string): Directory name of reference yaml file
ref_filename (string): Name of reference yaml file
needed_libraries (list of strings): List of libraries needed
in the Docker container
conda_filename (string): Name of yaml file to be deployed
in the Docker container
Returns: Nothing
"""
with open(os.path.join(directory, ref_filename), "r") as f:
yaml_content = yaml.load(f, Loader=yaml.FullLoader)
# Extract libraries to be installed using conda
extracted_libraries = [
depend for depend in yaml_content["dependencies"]
if any(lib in depend for lib in needed_libraries)
]
# Extract libraries to be installed using pip
if any(isinstance(x, dict) for x in yaml_content["dependencies"]):
# if the reference yaml file contains a "pip" section,
# find where it is in the list of dependencies
ind = [
yaml_content["dependencies"].index(depend)
for depend in yaml_content["dependencies"]
if isinstance(depend, dict)
][0]
extracted_libraries += [
depend for depend in yaml_content["dependencies"][ind]["pip"]
if any(lib in depend for lib in needed_libraries)
]
# Check whether additional libraries are needed
not_found = [
lib for lib in needed_libraries
if not any(lib in ext for ext in extracted_libraries)
]
# Create the deployment-specific yaml file
conda_env = CondaDependencies()
for ch in yaml_content["channels"]:
conda_env.add_channel(ch)
for library in extracted_libraries + not_found:
conda_env.add_conda_package(library)
# Display the environment
print(conda_env.serialize_to_string())
# Save the file to disk
conda_env.save_to_file(base_directory=os.getcwd(),
conda_file_path=conda_filename)
def conda_deps():
deps = CondaDependencies(f'{project_folder}/environment.yml')
deps.add_channel("conda-forge")
deps.add_conda_package('curl')
return deps
workspace_name = "aa-ml-aml-workspace" # The name of the workspace to look for or to create
workspace_region = 'eastus' # Location of the workspace
computetarget_vm = 'Standard_NC6' # Size of the VM to use
experiment_name = 'azureml-gpubenchmark'
score_script = 'score_and_track.py'
conda_file_name = 'fastai.yml'
ws = Workspace(workspace_name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group)
myenv = CondaDependencies()
myenv.add_conda_package("fastai")
myenv.add_conda_package("pytorch")
myenv.add_conda_package("torchvision")
myenv.add_channel("pytorch")
myenv.add_channel("fastai")
with open(conda_file_name, "w") as f:
f.write(myenv.serialize_to_string())
from azureml.core.image import ContainerImage
# Image configuration
image_config = ContainerImage.image_configuration(
execution_script=score_script,
runtime="python",
conda_file=conda_file_name,
enable_gpu=True,
description="Image classficiation service cats vs dogs",
tags={
"data": "cats-vs-dogs",
