Keras - Train classifier: Improved the README.md

components/sample/keras/train_classifier/README.md

@@ -1,20 +1,77 @@
-## Train classification model using Keras ##
+# Keras - Train classifier
+### Trains classifier using Keras sequential model
+
+## Inputs
+|Name|Type|Default|Description|
+|---|---|---|---|
+|training_set_features_path|GcsPath: {data_type: TSV}||Local or GCS path to the training set features table.|
+|training_set_labels_path|GcsPath: {data_type: TSV}||Local or GCS path to the training set labels (each label is a class index from 0 to num-classes - 1).|
+|output_model_uri|GcsPath: {data_type: Keras model}||Local or GCS path specifying where to save the trained model. The model (topology + weights + optimizer state) is saved in HDF5 format and can be loaded back by calling keras.models.load_model|
+|model_config|GcsPath: {data_type: Keras model config json}||JSON string containing the serialized model structure. Can be obtained by calling model.to_json() on a Keras model.|
+|number_of_classes|Integer||Number of classifier classes.|
+|number_of_epochs|Integer|100|Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided.|
+|batch_size|Integer|32|Number of samples per gradient update|
+
+## Outputs
+|Name|Type|Default|Description|
+|---|---|---|---|
+|output_model_uri|GcsPath: {data_type: Keras model}||GCS path where the trained model has been saved. The model (topology + weights + optimizer state) is saved in HDF5 format and can be loaded back by calling keras.models.load_model|
+
+## Container image
+gcr.io/ml-pipeline/components/sample/keras/train_classifier
+
+## Usage:
+
+```python
+import os
+from pathlib import Path
+import requests
+
+import kfp
+
+component_url_prefix = 'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/sample/keras/train_classifier/'
+test_data_url_prefix = component_url_prefix + 'tests/testdata/'
+
+#Prepare input/output paths and data
+input_data_gcs_dir = 'gs://<my bucket>/<path>/'
+output_data_gcs_dir = 'gs://<my bucket>/<path>/'
+
+#Downloading the training set (to upload to GCS later)
+training_set_features_local_path = os.path.join('.', 'training_set_features.tsv')
+training_set_labels_local_path = os.path.join('.', 'training_set_labels.tsv')
+
+training_set_features_url = test_data_url_prefix + '/training_set_features.tsv'
+training_set_labels_url = test_data_url_prefix + '/training_set_labels.tsv'
+
+Path(training_set_features_local_path).write_bytes(requests.get(training_set_features_url).content)
+Path(training_set_labels_local_path).write_bytes(requests.get(training_set_labels_url).content)
+
+#Uploading the data to GCS where it can be read by the trainer
+training_set_features_gcs_path = os.path.join(input_data_gcs_dir, 'training_set_features.tsv')
+training_set_labels_gcs_path = os.path.join(input_data_gcs_dir, 'training_set_labels.tsv')
+
+gfile.Copy(training_set_features_local_path, training_set_features_gcs_path)
+gfile.Copy(training_set_labels_local_path, training_set_labels_gcs_path)
+
+output_model_uri_template = os.path.join(output_data_gcs_dir, '{{workflow.uid}}/{{pod.name}}/output_model_uri/data')
+
+xor_model_config = requests.get(test_data_url_prefix + 'model_config.json').content
 
-Usage:
 
-~~~~
 #Load the component
-train_op = comp.load_component(url='https://raw.githubusercontent.com/Ark-kun/pipelines/Added-sample-component/components/sample/keras/train_classifier/component.yaml')
+train_op = kfp.components.load_component_from_url(component_url_prefix + 'component.yaml')
 
 #Use the component as part of the pipeline
-def pipeline():
+@kfp.dsl.pipeline(name='Test keras/train_classifier', description='Pipeline to test keras/train_classifier component')
+def pipeline_to_test_keras_train_classifier():
     train_task = train_op(
-        training_set_features_path=os.path.join(testdata_root, 'training_set_features.tsv'),
-        training_set_labels_path=os.path.join(testdata_root, 'training_set_labels.tsv'),
-        output_model_uri=os.path.join(temp_dir_name, 'outputs/output_model/data'),
-        model_config=Path(testdata_root).joinpath('model_config.json').read_text(),
+        training_set_features_path=training_set_features_gcs_path,
+        training_set_labels_path=training_set_labels_gcs_path,
+        output_model_uri=output_model_uri_template,
+        model_config=xor_model_config,
         number_of_classes=2,
         number_of_epochs=10,
         batch_size=32,
     )
-~~~~
+    #Use train_task.outputs['output_model_uri'] to obtain the reference to the trained model URI that can be a passed to other pipeline tasks (e.g. for prediction or analysis)
+```