test

Jenkinsfile_predict

@@ -14,6 +14,7 @@ pipeline {
                 steps
                 {
                     sh 'rm -r my_model'
+                    sh 'rm -r model'
                     copyArtifacts(fingerprintArtifacts: true, projectName: 's434695-training/train', selector: buildParameter('WHICH_BUILD'))
                 }
             }

Zadanie_08_and_09_MLflow.py

@@ -12,12 +12,13 @@ import os
 import sys
 import mlflow
 
+mlflow.set_tracking_uri("http://172.17.0.1:5000")
+
 with mlflow.start_run():
 
     batch_param = int(sys.argv[1]) if len(sys.argv) > 1 else 16
     epoch_param = int(sys.argv[2]) if len(sys.argv) > 2 else 15
-
-
+   
     mlflow.set_experiment("s434788")
     mlflow.log_param("batch_size", batch_param)
     mlflow.log_param("epochs", epoch_param)
@@ -75,5 +76,6 @@ with mlflow.start_run():
     print(f"accuracy: {results}")
 
     mlflow.log_metric("Accuracy", results)
-
-    mlflow.keras.save_model(NeuralModel, "my_model", signature= mlflow.models.signature.infer_signature(x_train, y_train), input_example= x_train[0])
+    
+    mlflow.keras.save_model(NeuralModel, "my_model", signature= mlflow.models.signature.infer_signature(x_train, y_train), input_example= x_train[0])
+    mlflow.keras.log_model(NeuralModel, "model",registered_model_name="s434788", signature= mlflow.models.signature.infer_signature(x_train, y_train), input_example= x_train[0])

mlruns/0/4c36c99ff9db4da7939a568673fa40bd/meta.yaml

@@ -0,0 +1,15 @@
+artifact_uri: file:///C:/Users/domstr2/Desktop/Git%20Repositories/ium_434788/mlruns/0/4c36c99ff9db4da7939a568673fa40bd/artifacts
+end_time: null
+entry_point_name: ''
+experiment_id: '0'
+lifecycle_stage: active
+name: ''
+run_id: 4c36c99ff9db4da7939a568673fa40bd
+run_uuid: 4c36c99ff9db4da7939a568673fa40bd
+source_name: ''
+source_type: 4
+source_version: ''
+start_time: 1621808417901
+status: 1
+tags: []
+user_id: domstr2

mlruns/0/4c36c99ff9db4da7939a568673fa40bd/tags/mlflow.source.git.commit

@@ -0,0 +1 @@
+82c812c301d802aacff75cb7f6695a3840b784b1

mlruns/0/4c36c99ff9db4da7939a568673fa40bd/tags/mlflow.source.name

@@ -0,0 +1 @@
+c:\Users\domstr2\Desktop\Git Repositories\ium_434788\Zadanie_08_and_09_MLflow.py

mlruns/0/4c36c99ff9db4da7939a568673fa40bd/tags/mlflow.source.type

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

mlruns/0/4c36c99ff9db4da7939a568673fa40bd/tags/mlflow.user

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

my_model/MLmodel

@@ -1,19 +0,0 @@
-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

@@ -1,10 +0,0 @@
-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

@@ -1 +0,0 @@
-tensorflow.keras

my_model/data/save_format.txt

@@ -1 +0,0 @@
-tf

my_model/input_example.json

@@ -1 +0,0 @@
-{"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]}