Predict?

Jenkinsfile_predict

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+pipeline {
+    agent {docker { image 'snowycocoon/ium_434788:4'}}
+	parameters{
+        buildSelector(
+            defaultSelector: lastSuccessful(),
+            description: 'Which build to use for copying artifacts',
+            name: 'WHICH_BUILD'
+        )
+    }
+
+    stages {
+		stage('copy artifacts')
+            {
+                steps
+                {
+                    copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-training/train', selector: buildParameter('WHICH_BUILD'))
+                }
+            }
+            stage('predict')
+            {
+                steps
+                {
+                    catchError {
+                        sh 'rm -r my_model'
+                        sh 'python3.8 Zadanie_09_MLflow_Predict.py ${BATCH_SIZE} ${EPOCHS}'
+                    }
+                }
+            }
+	}
+
+    post {
+        success {
+            mail body: 'SUCCESS',
+                subject: 's434788 mlflow predict',
+                to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        unstable {
+            mail body: 'UNSTABLE', subject: 's434788 mlflow predict', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        failure {
+            mail body: 'FAILURE', subject: 's434788 mlflow predict', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+        changed {
+            mail body: 'CHANGED', subject: 's434788 mlflow predict', to: '26ab8f35.uam.onmicrosoft.com@emea.teams.ms'
+        }
+    }
+}

Zadanie_09_MLflow_Predict.py

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+import mlflow
+import pandas as pd
+
+model = mlflow.keras.load_model("my_model")
+data = pd.read_json('my_model\input_example.json', orient='index')
+print(data)
+print(model.predict(data))

mlruns/0/1e5709985a4a4682ab319f0d3289099d/meta.yaml

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+artifact_uri: file:///C:/Users/domstr2/Desktop/Git%20Repositories/ium_434788/mlruns/0/1e5709985a4a4682ab319f0d3289099d/artifacts
+end_time: 1621792438671
+entry_point_name: ''
+experiment_id: '0'
+lifecycle_stage: active
+name: ''
+run_id: 1e5709985a4a4682ab319f0d3289099d
+run_uuid: 1e5709985a4a4682ab319f0d3289099d
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1621792431816
+status: 3
+tags: []
+user_id: domstr2

mlruns/0/1e5709985a4a4682ab319f0d3289099d/metrics/Accuracy

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+1621792435688 1.0 0

mlruns/0/1e5709985a4a4682ab319f0d3289099d/params/batch_size

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+16

mlruns/0/1e5709985a4a4682ab319f0d3289099d/params/epochs

@@ -0,0 +1 @@
+15

mlruns/0/1e5709985a4a4682ab319f0d3289099d/tags/mlflow.source.git.commit

@@ -0,0 +1 @@
+6757e5b025505bf24aebc3f63982f0ad32995de3

mlruns/0/1e5709985a4a4682ab319f0d3289099d/tags/mlflow.source.name

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+c:/Users/domstr2/Desktop/Git Repositories/ium_434788/Zadanie_08_and_09_MLflow.py

mlruns/0/1e5709985a4a4682ab319f0d3289099d/tags/mlflow.source.type

@@ -0,0 +1 @@
+LOCAL

mlruns/0/1e5709985a4a4682ab319f0d3289099d/tags/mlflow.user

@@ -0,0 +1 @@
+domstr2

my_model/MLmodel

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+flavors:
+  keras:
+    data: data
+    keras_module: tensorflow.keras
+    keras_version: 2.4.0
+    save_format: tf
+  python_function:
+    data: data
+    env: conda.yaml
+    loader_module: mlflow.keras
+    python_version: 3.8.5
+saved_input_example_info:
+  artifact_path: input_example.json
+  format: tf-serving
+  type: ndarray
+signature:
+  inputs: '[{"type": "tensor", "tensor-spec": {"dtype": "float64", "shape": [-1, 14]}}]'
+  outputs: '[{"type": "tensor", "tensor-spec": {"dtype": "int32", "shape": [-1]}}]'
+utc_time_created: '2021-05-23 17:53:55.693430'

my_model/conda.yaml

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+channels:
+- defaults
+- conda-forge
+dependencies:
+- python=3.8.5
+- pip
+- pip:
+  - mlflow
+  - tensorflow==2.4.1
+name: mlflow-env

my_model/data/keras_module.txt

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+tensorflow.keras

my_model/data/save_format.txt

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+tf

my_model/input_example.json

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+{"inputs": [-0.011282211362261343, 0.6825528426633746, -0.8779676567417312, 0.25625952402130414, 0.0325916986923591, 0.1075920895847071, -0.19667888586283722, 0.6801776123326557, -0.13679826698989028, -0.6382195990287619, -0.8663788579309919, -0.787822640922809, -0.7744150275178463, -0.0063619093376772375]}