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azure - 无法运行 Azure Docker 映像

问题描述

我们已经运行了一年多的 mlops-azure 容器 ( https://hub.docker.com/_/microsoft-mlops-python )，用于运行我们的评分图像的创建。在此图像的顶部，我们正在安装这些要求：

azureml-sdk
cryptography==3.3.1
flake8
flake8_formatter_junit_xml
pytest==4.3.0
python-dotenv>=0.10.3
requests>=2.2
numpy==1.19.5
pandas==1.1.5
scikit-learn==0.24.1
nltk==3.5
fuzzywuzzy==0.18.0
inflection==0.5.1
python-Levenshtein==0.12.0
seaborn
retrying
pymysql
pyyaml==5.3.1
pydantic==1.7.3
yamlloader==0.5.5

我们正在运行的代码是：

import os
import glob
import sys
import yaml

from azureml.core import Workspace
from azureml.core.model import Model, InferenceConfig
from azureml.core.environment import Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.authentication import ServicePrincipalAuthentication
from dotenv import load_dotenv

load_dotenv()

TENANT_ID = os.environ.get("TENANT_ID")
APP_ID = os.environ.get("SP_APP_ID")
APP_SECRET = os.environ.get("SP_APP_SECRET")
SUBSCRIPTION_ID = os.environ.get("SUBSCRIPTION_ID")
REGISTRY_CONTAINER_IMAGE = os.environ.get("REGISTRY_CONTAINER_IMAGE")
REGISTRY_CONTAINER_USERNAME = os.environ.get("REGISTRY_CONTAINER_USERNAME")
REGISTRY_CONTAINER_PASSWORD = os.environ.get("REGISTRY_CONTAINER_PASSWORD")
REGISTRY_BASE_IMAGE = os.environ.get("REGISTRY_BASE_IMAGE")
MODEL_NAME = os.environ.get("MODEL_NAME")
MODEL_VERSION = os.environ.get("MODEL_VERSION")

WORKSPACE_NAME = os.environ.get("BASE_NAME")
RESOURCE_GROUP = os.environ.get("BASE_NAME")
if WORKSPACE_NAME and RESOURCE_GROUP:
    WORKSPACE_NAME += "-AML-WS"
    RESOURCE_GROUP += "-AML-RG"


def load_requirements_into_conda_yml(conda_yml: str):
    # Load requirements
    f = open("requirements.txt", "r")
    requirements = str(f.read()).split("\n")

    print(requirements)

    # Load conda dependencies
    with open(conda_yml, "r") as stream:
        try:
            conda_yaml = yaml.safe_load(stream)
        except yaml.YAMLError as exc:
            print(exc)
    # Get conda pip package
    conda_dependencies = conda_yaml["dependencies"][1]["pip"]
    # Check if the package is already there
    for requirement in requirements:
        if requirement and requirement not in conda_dependencies:
            conda_yaml["dependencies"][1]["pip"].append(requirement)
    # Save the new requirements
    with open(conda_yml, "w") as outfile:
        yaml.dump(conda_yaml, outfile, default_flow_style=False)


output_dir = os.path.abspath(sys.argv[1])
os.makedirs(output_dir, exist_ok=True)

SP_AUTH = ServicePrincipalAuthentication(
    tenant_id=TENANT_ID, service_principal_id=APP_ID, service_principal_password=APP_SECRET
)

ws = Workspace.get(WORKSPACE_NAME, SP_AUTH, SUBSCRIPTION_ID, RESOURCE_GROUP)
model = Model(ws, name=MODEL_NAME, version=MODEL_VERSION)

# We are downloading the wheel from data tools into the path src/score
# With this loop we are looking for the name of the wheel
os.chdir("./src")
files_whl = []
for file_whl in glob.glob("score/*.whl"):
    files_whl.append(file_whl)

CONDA_YAML = "score/conda_dependencies.yml"
load_requirements_into_conda_yml(conda_yml=CONDA_YAML)
conda_dep = CondaDependencies(conda_dependencies_file_path=CONDA_YAML)
conda_dep.add_conda_package("pip==20.2.4")

try:
    for file_whl in files_whl:
        whl_url = Environment.add_private_pip_wheel(workspace=ws, file_path=file_whl, exist_ok=True)
        conda_dep.add_pip_package(whl_url)
except Exception:
    print("Not able to add the wheel from data tools")

myenv = Environment(name="myenv")

myenv.docker.base_image_registry.address = REGISTRY_CONTAINER_IMAGE
myenv.docker.base_image_registry.username = REGISTRY_CONTAINER_USERNAME
myenv.docker.base_image_registry.password = REGISTRY_CONTAINER_PASSWORD

myenv.docker.enabled = True
myenv.python.user_managed_dependencies = False
myenv.docker.base_image = REGISTRY_BASE_IMAGE
myenv.inferencing_stack_version = "latest"
myenv.python.conda_dependencies = conda_dep

inference_config = InferenceConfig(
    entry_script="score/score.py", source_directory=".", environment=myenv
)

package = Model.package(
    workspace=ws, inference_config=inference_config, models=[model], generate_dockerfile=True
)

package.wait_for_creation(show_output=True)

if package.state != "Succeeded":
    raise Exception("package creation status: {package.state}")

package.save(output_dir)

Docker 版本为 20.10.2，在 ubuntu18 中运行。

运行 docker 镜像的命令是：

docker run \
  -v $code_dir:/code \
  -v $package_dir:/package \
  -w=/code \
  -e BASE_NAME=$BASE_NAME \
  -e SP_APP_ID=$SP_APP_ID \
  -e SP_APP_SECRET=$(SP_APP_SECRET) \
  -e SUBSCRIPTION_ID=$SUBSCRIPTION_ID \
  -e TENANT_ID=$TENANT_ID \
  -e REGISTRY_CONTAINER_IMAGE=$REGISTRY_CONTAINER_IMAGE \
  -e REGISTRY_CONTAINER_PASSWORD=$REGISTRY_CONTAINER_PASSWORD \
  -e REGISTRY_CONTAINER_USERNAME=$REGISTRY_CONTAINER_USERNAME \
  -e REGISTRY_BASE_IMAGE=$SCORING_IMAGE \
  -e MODEL_NAME=$MODEL_NAME  \
  -e MODEL_VERSION=$MODEL_VERSION \
  $(SCORING_IMAGE) \
 python ml_service/util/create_scoring_package.py /package

我们收到的错误是：

Failed to create Docker client. Is Docker running/installed?
When you deploy locally, we download a dockerfile
execute docker build on it, and docker run the built container for you
Error: Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))

Unable to log into Docker registry (is Docker installed and running?). Building the saved Dockerfile may fail when pulling the base image. To login manually, or on another machine, use the credentials returned by package.get_container_registry()

太感谢了！

标签： azuredocker

解决方案

它说图像移动：

我们搬家了！我们正在迁移到 mcr.microsoft.com，您可以在其中提取最新的 MlOps Python 容器。我们将不再使用更新来更新此容器存储库，我们最终将从此存储库中删除所有容器映像。

在https://hub.docker.com/_/microsoft-mlops-python访问我们的新 Docker Hub 页面。

在https://azure.microsoft.com/en-us/blog/microsoft-syndicates-container-catalog了解有关 Microsoft 容器注册表的更多信息。

所以而不是这个

docker pull microsoft/mlopspython

尝试

docker pull mcr.microsoft.com/mlops/python

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