[GitHub] [dolphinscheduler] zhongjiajie commented on a diff in pull request #10217: [Feature] [MLOps] support mlflow deploy with docker compose

[GitBox <gi...@apache.org>]

zhongjiajie commented on code in PR #10217:
URL: https://github.com/apache/dolphinscheduler/pull/10217#discussion_r886582010


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dolphinscheduler-task-plugin/dolphinscheduler-task-mlflow/src/main/resources/docker-compose.yml:
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@@ -0,0 +1,28 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+version: "3"
+
+services:
+  mlflow-model:
+    image: "${imageName}"
+    ports:
+      - "${deployPort}:8080"
+    deploy:
+      resources:
+        limits:
+          cpus: "${cpuLimit}"
+          memory: "${memoryLimit}"

Review Comment:
   > I think your point is reasonable in most tasks. But in the model deployment task, I think state means something like this:
   Success: Successfully publish the model.
   Running: Deploying the model. In this case, In this case, it means creating the Docker and running the Docker container
   Failure: Deployment failure. In this case, maybe an error in the command from MLflow docker build or an error in run  command
   
   I agree with the running part, if we should monitor the deployment docker container for it lifecycle, the deployment task should not ending, I think it is a little odd for users about "why my deployment task never end". I think the best way to do that is we only make sure the docker container up and start to service (to have specific logs in docker container).
   BTW, I have to mention is the MlFlow allow users write same users define function and start service, when users write some error, it should certainly cause error, and sometime may exit the container, But IMO I think it is more like user herself issue instead of DolphinSchekduler's 



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