[英]Azure Machine learning fails when trying to deploy model
I'm currently trying to deploy a model on azure and expose it's endpoint to my application but I kept running into errors我目前正在尝试在 azure 上部署一个模型并将它的端点公开给我的应用程序,但我一直遇到错误
DEPLOYMENT CODE部署代码
model = run.register_model(model_name='pytorch-modeloldage', model_path="outputs/model") print("Starting.........")
inference_config = InferenceConfig(runtime= "python",
entry_script="pytorchscore.py",
conda_file="myenv.yml")
aciconfig = AciWebservice.deploy_configuration(cpu_cores=1,auth_enabled=True,
memory_gb=1,
tags={'name':'oldageml', 'framework': 'pytorch'},
description='oldageml training')
service = Model.deploy(workspace=ws,
name='pytorch-olageml-run',
models=[model],
inference_config=inference_config,
overwrite=True,
deployment_config=aciconfig)
service.wait_for_deployment(True)
# print(service.get_logs()) print("bruh did you run", service.scoring_uri) print(service.state)
ERROR错误
ERROR - Service deployment polling reached non-successful terminal state, current service state: Transitioning
More information can be found here:
Error:
{
"code": "EnvironmentBuildFailed",
"statusCode": 400,
"message": "Failed Building the Environment."
}
I had this error, too, and I was convinced it was working a few days ago!我也有这个错误,几天前我确信它可以正常工作! Anyway, I realised that I was using python 3.5 in my environment definition.
无论如何,我意识到我在我的环境定义中使用了 python 3.5。 I changed that to 3.6 and it works!
我将其更改为 3.6 并且它有效! I notice that there was a new release of azureml-code on 9 Dec 2019.
我注意到 2019 年 12 月 9 日发布了新版本的 azureml-code。
This is my code for changing the environment;这是我改变环境的代码; I add the environment for a variable rather than a file as you do, so that's a bit different.
我像你一样为变量而不是文件添加环境,所以这有点不同。
myenv=Environment(name="env-keras")
conda_packages = ['numpy']
pip_packages = ['tensorflow==2.0.0', 'keras==2.3.1', 'azureml-sdk','azureml-defaults']
mycondaenv = CondaDependencies.create(conda_packages=conda_packages, pip_packages=pip_packages, python_version='3.6.2')
myenv.python.conda_dependencies=mycondaenv
myenv.register(workspace=ws)
inference_config = InferenceConfig(entry_script='score.py',source_directory='.',environment=myenv)
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