This blog post shows how to retrain and deploy a SAP Leonardo Machine Learning Image Classification Model on the SAP Cloud Platform trial version. I use Postman for all API calls and Minio to upload the training data based on the Apparel classification with Style dataset.

Thanks to the trained model, I can now for example see the difference between a blouse and a shirt. 😉

At SAP Help you can find some help about Retraining of Customizable Services, and there are also some (older) blog posts and documentations available:

• SAP Leonardo ML Foundation – Retraining part 1
• EXERCISE 08 – ML Foundation Re-trainable services
• Retrain and Deploy the Image Classification Model through SAP Leonardo Machine Learning Platform

And if you want more details about retraining, have a look at How to Retrain an Image Classifier for New Categories.

About text retraining, see Retrain a Text Classification with Postman and SAP Leonardo Machine Learning Foundation on SAP Cloud Platform Trial – Sentiment Analysis

What do you need?

• A trial account on SAP Cloud Platform.
• Postman
• Minio
• Image Dataset: Apparel classification with Style
• A script to create the training data: Apparel Dataset Creator

Step 1 – SAP Leonardo Machine Learning instance

First, you create a SAP Leonardo Machine Learning instance and a client ID and client secret on a SAP Could Platform (SCP) trial account:

• Log on or register a free SCP trial account.
• Click on Cloud Foundry Trial.
• In the Start Cloud Foundry Trial dialog select Europe (Frankfurt) for the region and click OK.
• This creates a trial account (e.g. p2000894545trial). Click on Go to Space.
• SCP opens Home [Europe (Rot) – Trial] / Europe (Frankfurt) / p2000894545trial / trial / dev
• Click on Service Marketplace (left menu) and search for “ml”.
• Click on ml-foundation-trial-beta.
• An Overview page will open. Click on Instances (left menu) to open the Instances page.
• Click on New Instance.
• The Create Instance dialog opens. Select standard-new as Plan and click Next.
• Click Next on Specify Parameters (Optional) and Assign Application (Optional).
• On the Confirm page fill in the Instance Name e.g. fashion_data and click Finish.
• The Instances page shows the new created fashion_data instance. Open the fashion_data Instance with a click.
• Click on Service Keys (left menu).
• On the Service Keys page click on Create Service Key.
• Add e.g. ml_key for the name on the Create Service Key dialog and click Save.

This creates the Service Key with the following content:

{
  "clientid": "sb-42mn3-3z7p-96r-3c79-0x1pm0l!p216|klgco-lag-vitas!d66",
  …
  "clientsecret": "5ghsdYM/Z567N5LoQ7nrXBkZ0BV=",
  "serviceurls": {
    …
    "IMAGE_RETRAIN_API_URL": "https://mlftrial-retrain-image-api.cfapps.eu10.hana.ondemand.com/api/v2/image/retraining",
    …
    "IMAGE_CLASSIFICATION_URL": "https://mlftrial-image-classifier.cfapps.eu10.hana.ondemand.com/api/v2/image/classification",
    …
  },
  "url": "https://p2000894545trial.authentication.eu10.hana.ondemand.com"
}

Step 2 – Storage

Before you can upload data, you need some storage. Generate a Bearer Token and create the storage with Postman. You can find my Postman collection here: SAP Leonardo Machine Learning Postman Collection

Generate Token

URL: {url}/oauth/token

POST
https://p2000482375trial.authentication.eu10.hana.ondemand.com/oauth/token

Header:
  Content-Type: application/x-www-form-urlencoded

Body:
  grant_type: client_credentials
  client_id: <replace it with clientid>
  client_secret: <replace it with clientsecret>

Test:
  pm.test("Set Bearer Token", function () {
    var data = pm.response.json();
    pm.environment.set("Bearer Token", "Bearer " + data.access_token);
  });

This creates a Bearer Token. With the code in the test folder, the token is also stored in the Postman environment as ‘Bearer Token’. Postman replaces the string {{Bearer Token}}} with the most recent value each time it is called.

Storage

URL: IMAGE_RETRAIN_API_URL/storage
Doc: https://api.sap.com/api/retraining_service_imgc_api/resource

POST
https://mlftrial-retrain-image-api.cfapps.eu10.hana.ondemand.com/api/v2/image/retraining/storage

Header:
  Authorization: {{Bearer Token}}

Result:
  {
    "accessKey": "18gXjKdl5YR",
    "endpoint": "j310p.files.trial.eu-central-1.aws.ml.hana.ondemand.com",
    "message": "The endpoint is ready to use.",
    "secretKey": "b47iOAuRYosEtGT",
    "status": "READY"
  }

The endpoint, accessKey and secretKey are required for step 4.

Step 3 – Training data

The following steps are necessary to create the training data:

• download Apparel classification with Style
• run the Apparel Dataset Creator script (needs Node.js)

The script creates the following structure:

fashion_data
├── test
│   ├── blouses
│   ├── cloak
│   ├── coat
│   ├── jacket
│   ├── jersey__t-shirt__tee_shirt
│   ├── long_dress
│   ├── polo_shirt__sport_shirt
│   ├── robe
│   ├── shirt
│   ├── short_dress
│   ├── suit__suit_of_clothes
│   ├── sweater
│   ├── undergarment__upper_body
│   ├── uniform
│   └── vest__waistcoat
├── training
│   ├── blouses
│   ├── cloak
│   ├── coat
│   ├── jacket
│   ├── jersey__t-shirt__tee_shirt
│   ├── long_dress
│   ├── polo_shirt__sport_shirt
│   ├── robe
│   ├── shirt
│   ├── short_dress
│   ├── suit__suit_of_clothes
│   ├── sweater
│   ├── undergarment__upper_body
│   ├── uniform
│   └── vest__waistcoat
└── validation
    ├── blouses
    ├── cloak
    ├── coat
    ├── jacket
    ├── jersey__t-shirt__tee_shirt
    ├── long_dress
    ├── polo_shirt__sport_shirt
    ├── robe
    ├── shirt
    ├── short_dress
    ├── suit__suit_of_clothes
    ├── sweater
    ├── undergarment__upper_body
    ├── uniform
    └── vest__waistcoat

Other data sets:

• Food 101
• Kaggle Dataset
• Flowers (see also SAP Leonardo Machine Learning Flowers Dataset)
• OpenLogo

Step 4 – Upload

Minio is almost three times faster than sapml via Cloud Foundry. However, if you are looking for the latest version of sapml, you can find it here: SAP Leonardo Machine Learning foundation plugin for SAP Cloud Platform CLI 

• Install Minio: Minio Client Quickstart Guide
• config the host (don’t forget the https)

mc config host add saps3 https://<endpoint> <accessKey> <secretKey>

Example:

mc config host add saps3 https://j310p.files.trial.eu-central-1.aws.ml.hana.ondemand.com 18gXjKdl5YR b47iOAuRYosEtGT

• test (the data folder should exist)

mc ls saps3/

• upload

mc cp --recursive fashion_data saps3/data

This will take some time :coffee: or :tee:

Step 5 – Training

After uploading the training data, start the training (with Postman).

Training

URL: IMAGE_RETRAIN_API_URL/jobs
Doc: https://api.sap.com/api/retraining_service_imgc_api/resource

POST
https://mlftrial-retrain-image-api.cfapps.eu10.hana.ondemand.com/api/v2/image/retraining/jobs

Headers:
  Authorization: {{Bearer Token}}
  Accept application/json
  Content-Type application/json

Body:
  {
   "dataset": "fashion_data",
   "modelName": "apparel_fashion"
  }

Result:
  {
    "id": "fashiondata-2018-10-31t2118z489249"
  }

This will also take some time as the trial environments are not highly prioritized. You can check the job status with a GET IMAGE_RETRAIN_API_URL/jobs call.

Jobs

URL: IMAGE_RETRAIN_API_URL/jobs
Doc: https://api.sap.com/api/retraining_service_imgc_api/resource

GET
https://mlftrial-retrain-image-api.cfapps.eu10.hana.ondemand.com/api/v2/image/retraining/jobs

Results:
  {
    "jobs": [
        {
            "message": "Requested resources are unavailable.
                        Please wait or cancel your job.",
            "id": "fashiondata-2018-10-31t2118z489249",
            "submissionTime": "2018-10-31T20:55:40+00:00",
            "status": "PENDING"
        }
    ]
  }


  {
    "jobs": [
        {
            "startTime": "2018-10-29T08:46:24+00:00",
            "id": "fashiondata-2018-10-31t2118z489249",
            "finishTime": "2018-10-29T09:31:30+00:00",
            "status": "SUCCEEDED",
            "submissionTime": "2018-10-29T08:38:41+00:00",
            "message": ""
        }
    ]
  }

Logs

In case of failure or success, download the job logs:

mc cp --recursive saps3/data/fashiondata-2018-10-31t2118z489249/ logs

Step 6 – Deploy

The model must be deployed after a successful training.

Deploy Model

URL: IMAGE_RETRAIN_API_URL/deployments
Doc: https://api.sap.com/api/retraining_service_imgc_api/resource

POST
https://mlftrial-retrain-image-api.cfapps.eu10.hana.ondemand.com/api/v2/image/retraining/deployments

Header:
  Authorization {{Bearer Token}}
  Content-Type application/json

Body:
  {
    "modelName": "apparel_fashion",
    "modelVersion": “1”
  }

Result:
  {
    "id": "f6b34f68-6bf0-4fe8-98f5-9f9a4310a9b8"
  }

Now the model is available for an image classification.

Step 7 – Test

You can now use your trained model instead of the sandbox version.

Image Classification

URL: IMAGE_CLASSIFICATION_URL/models/{model}/versions/{version}
Doc: https://api.sap.com/api/image_classification_api/resource

POST
https://mlftrial-image-classifier.cfapps.eu10.hana.ondemand.com/api/v2/image/classification/models/apparel_fashion/versions/1

Header:
  Authorization {{Bearer Token}}
  Content-Type application/json

Body:
  files: model-1338993_640.jpg

Here is the result for this image:

…. and as you can see it is 44% a blouse and only 8.8% a shirt.

{
    "id": "4ac738e5-2ae7-40b1-7c71-5811ffefd957",
    "predictions": [
        {
            "name": "model-1338993_640.jpg",
            "results": [
                {
                    "label": "blouses",
                    "score": 0.4417305588722229
                },
                {
                    "label": "sweater",
                    "score": 0.17741338908672333
                },
                {
                    "label": "jersey__t-shirt__tee_shirt",
                    "score": 0.09909668564796448
                },
                {
                    "label": "shirt",
                    "score": 0.08796049654483795
                },
                {
                    "label": "jacket",
                    "score": 0.0525900162756443
                },
                {
                    "label": "cloak",
                    "score": 0.05253848433494568
                },
                {
                    "label": "polo_shirt__sport_shirt",
                    "score": 0.035483747720718384
                },
                {
                    "label": "short_dress",
                    "score": 0.03353172168135643
                },
                {
                    "label": "long_dress",
                    "score": 0.011001537553966045
                },
                {
                    "label": "undergarment__upper_body",
                    "score": 0.002974236151203513
                },
                {
                    "label": "coat",
                    "score": 0.0025379941798746586
                },
                {
                    "label": "robe",
                    "score": 0.0012542953481897712
                },
                {
                    "label": "vest__waistcoat",
                    "score": 0.0008026888244785368
                },
                {
                    "label": "uniform",
                    "score": 0.0006318347295746207
                },
                {
                    "label": "suit__suit_of_clothes",
                    "score": 0.0004524094401858747
                }
            ]
        }
    ],
    "processedTime": "2018-10-31T22:02:29.581062+00:00",
    "status": "DONE"
}

And now you are able to test your model. Here are more images (they are not part of the training data):

• https://pixabay.com/en/girl-fashion-skirt-tshirt-2625460/
• https://pixabay.com/en/vest-waistcoat-fashion-jacket-male-1031127/
• https://pixabay.com/en/man-human-person-portrait-vintage-66941/
• https://pixabay.com/en/streetstyle-street-fashion-cloak-3750053/
• https://pixabay.com/en/judge-court-justice-law-right-67715/
• https://pixabay.com/en/portrait-woman-fashion-people-3190849/
• https://pixabay.com/en/woman-autumn-model-fashion-sweater-1245840/

have fun :goofy face: