Step 1 - SAP Leonardo Machine Learning instance

lars_gregori · ‎11-04-2018

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...

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/a...



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"

}