python 函数服务部署失败

在这里，我附上实际的错误显示。im使用mlrun与docker。特别是mlrun 1.2.0。

--------------------------------------------------------------------------
RunError                                  Traceback (most recent call last)
<ipython-input-20-aab97e08b914> in <module>
      1 serving_fn.with_code(body=" ") # adds the serving wrapper, not required with MLRun >= 1.0.3
----> 2 project.deploy_function(serving_fn)

/opt/conda/lib/python3.8/site-packages/mlrun/projects/project.py in deploy_function(self, function, dashboard, models, env, tag, verbose, builder_env, mock)
   2307         :param mock:        deploy mock server vs a real Nuclio function (for local simulations)
   2308         """
-> 2309         return deploy_function(
   2310             function,
   2311             dashboard=dashboard,

/opt/conda/lib/python3.8/site-packages/mlrun/projects/operations.py in deploy_function(function, dashboard, models, env, tag, verbose, builder_env, project_object, mock)
    344             )
    345 
--> 346         address = function.deploy(
    347             dashboard=dashboard, tag=tag, verbose=verbose, builder_env=builder_env
    348         )

/opt/conda/lib/python3.8/site-packages/mlrun/runtimes/serving.py in deploy(self, dashboard, project, tag, verbose, auth_info, builder_env)
    621             logger.info(f"deploy root function {self.metadata.name} ...")
    622 
--> 623         return super().deploy(
    624             dashboard, project, tag, verbose, auth_info, builder_env=builder_env
    625         )

/opt/conda/lib/python3.8/site-packages/mlrun/runtimes/function.py in deploy(self, dashboard, project, tag, verbose, auth_info, builder_env)
    550             self.status = data["data"].get("status")
    551             self._update_credentials_from_remote_build(data["data"])
--> 552             self._wait_for_function_deployment(db, verbose=verbose)
    553 
    554             # NOTE: on older mlrun versions & nuclio versions, function are exposed via NodePort

/opt/conda/lib/python3.8/site-packages/mlrun/runtimes/function.py in _wait_for_function_deployment(self, db, verbose)
    620         if state != "ready":
    621             logger.error("Nuclio function failed to deploy", function_state=state)
--> 622             raise RunError(f"function {self.metadata.name} deployment failed")
    623 
    624     @min_nuclio_versions("1.5.20", "1.6.10")

RunError: function serving deployment failed

我不知道这个错误背后的原因是什么。因为我是新来的。所以有人请帮我解决这个错误。

我认为有两个步骤可以解决这个问题：

1.相关安装

桌面Docker中的MLRun Community Edition必须安装在相关的HOST_IP下（不使用localhost或127.0.0.1，而是使用稳定的IP地址，请参见ipconfig），并使用相关的SHARED_DIR。请参见相关命令行（从操作系统窗口）：

set HOST_IP=192.168.0.150
set SHARED_DIR=c:\Apps\mlrun-data
set TAG=1.2.0

mkdir %SHARED_DIR%

docker-compose -f "c:\Apps\Desktop Docker Tools\compose.with-jupyter.yaml" up

顺便说一句：YAML文件参见https://docs.mlrun.org/en/latest/install/local-docker.html

2.进出港口

在调用serving_fn.invoke的情况下，您必须打开IP地址上的相关端口（从deploy_function）（基于HOST_IP的设置，参见第一点）。
通常，可以根据防火墙策略或本地防病毒程序阻止此端口。这意味着，您必须在调用调用之前打开对此端口的访问。
顺便说一句：您可以看到问题https://github.com/mlrun/mlrun/issues/2102的焦点

python 函数服务部署失败

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