2

我已经很好地遵循了此处概述的文档。

我通过以下方式设置了我的 azure 机器学习环境：

from azureml.core import Workspace

# Connect to the workspace
ws = Workspace.from_config()

from azureml.core import Environment
from azureml.core import ContainerRegistry

myenv = Environment(name = "myenv")

myenv.inferencing_stack_version = "latest"  # This will install the inference specific apt packages.

# Docker
myenv.docker.enabled = True
myenv.docker.base_image_registry.address = "myazureregistry.azurecr.io"
myenv.docker.base_image_registry.username = "myusername"
myenv.docker.base_image_registry.password = "mypassword"
myenv.docker.base_image = "4fb3..." 
myenv.docker.arguments = None

# Environment variable (I need python to look at folders 
myenv.environment_variables = {"PYTHONPATH":"/root"}

# python
myenv.python.user_managed_dependencies = True
myenv.python.interpreter_path = "/opt/miniconda/envs/myenv/bin/python" 

from azureml.core.conda_dependencies import CondaDependencies
conda_dep = CondaDependencies()
conda_dep.add_pip_package("azureml-defaults")
myenv.python.conda_dependencies=conda_dep

myenv.register(workspace=ws) # works!

我有一个为推理配置的 score.py 文件（与我遇到的问题无关）......

然后我设置推理配置

from azureml.core.model import InferenceConfig
inference_config = InferenceConfig(entry_script="score.py", environment=myenv)

我设置了我的计算集群：

from azureml.core.compute import ComputeTarget, AksCompute
from azureml.exceptions import ComputeTargetException

# Choose a name for your cluster
aks_name = "theclustername" 

# Check to see if the cluster already exists
try:
    aks_target = ComputeTarget(workspace=ws, name=aks_name)
    print('Found existing compute target')
except ComputeTargetException:
    print('Creating a new compute target...')
    prov_config = AksCompute.provisioning_configuration(vm_size="Standard_NC6_Promo")

    aks_target = ComputeTarget.create(workspace=ws, name=aks_name, provisioning_configuration=prov_config)

    aks_target.wait_for_completion(show_output=True)

from azureml.core.webservice import AksWebservice

# Example
gpu_aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,
                                                    num_replicas=3,
                                                    cpu_cores=4,
                                                    memory_gb=10)

一切顺利；然后我尝试部署模型进行推理：

from azureml.core.model import Model

model = Model(ws, name="thenameofmymodel")

# Name of the web service that is deployed
aks_service_name = 'tryingtodeply'

# Deploy the model
aks_service = Model.deploy(ws,
                           aks_service_name,
                           models=[model],
                           inference_config=inference_config,
                           deployment_config=gpu_aks_config,
                           deployment_target=aks_target,
                           overwrite=True)

aks_service.wait_for_deployment(show_output=True)
print(aks_service.state)

它没有说它找不到环境。更具体地说，我的环境版本是版本 11，但它一直在尝试查找版本号比当前环境高 1 的环境（即版本 12 ）：

FailedERROR - Service deployment polling reached non-successful terminal state, current service state: Failed
Operation ID: 0f03a025-3407-4dc1-9922-a53cc27267d4
More information can be found here: 
Error:
{
  "code": "BadRequest",
  "statusCode": 400,
  "message": "The request is invalid",
  "details": [
    {
      "code": "EnvironmentDetailsFetchFailedUserError",
      "message": "Failed to fetch details for Environment with Name: myenv Version: 12."
    }
  ]
}

我尝试手动编辑环境 JSON 以匹配 azureml 尝试获取的版本，但没有任何效果。任何人都可以看到这段代码有什么问题吗？

更新

更改环境的名称（例如，my_inference_env）并将其传递给InferenceConfig似乎是在正确的轨道上。但是，错误现在更改为以下

Running..........
Failed
ERROR - Service deployment polling reached non-successful terminal state, current service state: Failed
Operation ID: f0dfc13b-6fb6-494b-91a7-de42b9384692
More information can be found here: https://some_long_http_address_that_leads_to_nothing
Error:
{
  "code": "DeploymentFailed",
  "statusCode": 404,
  "message": "Deployment not found"
}

解决方案

关于 azure ML 环境的使用，下面 Anders 的回答确实是正确的。但是，我得到的最后一个错误是因为我使用摘要值（a sha）而不是图像名称和标签（例如， ）来设置容器图像imagename:tag。注意第一个块中的代码行：

myenv.docker.base_image = "4fb3..."

我引用了摘要值，但应该将其更改为

myenv.docker.base_image = "imagename:tag"

一旦我进行了更改，部署就成功了！:)

4

1 回答 1

1

我花了一段时间才明白的一个概念是注册和使用 Azure ML 的分歧Environment。如果您已经注册myenv了您的环境，并且您的环境的任何细节都没有更改，则无需重新注册它myenv.register()。您可以像这样简单地获取已经注册的环境Environment.get()：

myenv = Environment.get(ws, name='myenv', version=11)

我的建议是将您的环境命名为新的名称：例如"model_scoring_env". 注册一次，然后将其传递给InferenceConfig.

于 2020-08-17T22:08:41.697 回答