GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cpd4.8/notebooks/python_sdk/experiments/deep_learning/Use Keras and HPO to recognize hand-written digits.ipynb
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Kernel: Python 3 (ipykernel)

Use Keras and hyperparameter optimization (HPO) to recognize hand-written digits with `ibm-watson-machine-learning`

This notebook contains steps and code to demonstrate support of Deep Learning experiments in the Watson Machine Learning service. It introduces commands for data retrieval, training definition persistance, experiment training, model persistance, model deployment and scoring.

Some familiarity with Python is helpful. This notebook uses Python 3.10.

Learning goals

The learning goals of this notebook are:

Working with the Watson Machine Learning service.
Training Deep Learning models (TensorFlow).
Saving trained models in Watson Machine Learning repository.
Online deployment and scoring of the trained model.

This notebook contains the following parts:

1. Set up the environment

Before you use the sample code in this notebook, you must perform the following setup tasks:

Contact with your Cloud Pack for Data administrator and ask him for your account credentials

Connection to WML

Authenticate the Watson Machine Learning service on IBM Cloud Pack for Data. You need to provide platform url, your username and api_key.

In [ ]:

username = 'PASTE YOUR USERNAME HERE'
api_key = 'PASTE YOUR API_KEY HERE'
url = 'PASTE THE PLATFORM URL HERE'

In [76]:

wml_credentials = {
    "username": username,
    "apikey": api_key,
    "url": url,
    "instance_id": 'openshift',
    "version": '4.8'
}

Alternatively you can use username and password to authenticate WML services.

wml_credentials = {
    "username": ***,
    "password": ***,
    "url": ***,
    "instance_id": 'openshift',
    "version": '4.8'
}

Install and import the `ibm-watson-machine-learning` package

Note: ibm-watson-machine-learning documentation can be found here.

In [ ]:

!pip install -U ibm-watson-machine-learning

In [2]:

from ibm_watson_machine_learning import APIClient

client = APIClient(wml_credentials)

Working with spaces

First of all, you need to create a space that will be used for your work. If you do not have space already created, you can use {PLATFORM_URL}/ml-runtime/spaces?context=icp4data to create one.

Click New Deployment Space
Create an empty space
Go to space Settings tab
Copy space_id and paste it below

Tip: You can also use SDK to prepare the space for your work. More information can be found here.

Action: Assign space ID below

In [3]:

space_id = 'PASTE YOUR SPACE ID HERE'

You can use list method to print all existing spaces.

In [ ]:

client.spaces.list(limit=10)

To be able to interact with all resources available in Watson Machine Learning, you need to set space which you will be using.

In [3]:

client.set.default_space(space_id)

Out[3]:

'SUCCESS'

2. Create model definition

For the purpose of this example two Keras model definitions have been prepared:

Multilayer Perceptron (MLP)
Convolution Neural Network (CNN)

2.1 Prepare model definition metadata

In [6]:

metaprops = {
    client.model_definitions.ConfigurationMetaNames.NAME: "MNIST mlp model definition",
    client.model_definitions.ConfigurationMetaNames.DESCRIPTION: "MNIST mlp model definition",
    client.model_definitions.ConfigurationMetaNames.COMMAND: "mnist_mlp.py",
    client.model_definitions.ConfigurationMetaNames.PLATFORM: {"name": "python", "versions": ["3.10"]},
    client.model_definitions.ConfigurationMetaNames.VERSION: "2.0",
}

2.2 Get sample model definition content files from git (Python scripts with CNN and MLP)

Hint: You need to have wget installed: pip install wget

In [7]:

import os, wget

filename_mnist = 'MNIST.zip'

if not os.path.isfile(filename_mnist):
    filename_mnist = wget.download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd4.8/definitions/keras/mnist/MNIST.zip')

Tip: Convert below cell to code and run it to see model deinition's code.

!unzip -oqd . MNIST.zip && cat mnist_mlp.py

2.3 Publish model definition

In [29]:

model_definition_details = client.model_definitions.store(filename_mnist, meta_props=metaprops)

In [30]:

model_definition_id = client.model_definitions.get_id(model_definition_details)
print(model_definition_id)

Out[30]:

7e651240-38a1-45f4-a73c-b9b1c8e68b08

List model definitions

In [ ]:

client.model_definitions.list(limit=5)

3. Train model

Warning: Before executing deep learning experiment make sure that training data is saved in a folder where Watson Machine Learning Accelerator is installed.

3.1 Prepare training metadata

In [35]:

training_metadata = {
    client.training.ConfigurationMetaNames.NAME: "Keras-MNIST",
    client.training.ConfigurationMetaNames.DESCRIPTION: "Keras-MNIST predict written digits",
    client.training.ConfigurationMetaNames.TRAINING_RESULTS_REFERENCE:  {
      "name":"MNIST results",
      "connection":{
         
      },
      "location":{
         "path":f"spaces/{space_id}/assets/experiment"
      },
      "type":"fs"
   },
  client.training.ConfigurationMetaNames.MODEL_DEFINITION:{
        "id": model_definition_id,
        "hardware_spec": {
          "name": "K80",
          "nodes": 1
        },
        "software_spec": {
          "name": "tensorflow_rt23.1-py3.10"
        }
      },
  client.training.ConfigurationMetaNames.TRAINING_DATA_REFERENCES: [
      {
         "name":"training_input_data",
         "type":"fs",
         "connection":{
            
         },
         "location":{
            "path": "tf-mnist"
         },
         "schema":{
            "id":"idmlp_schema",
            "fields":[
               {
                  "name":"text",
                  "type":"string"
               }
            ]
         }
      }
   ]
}

3.2 Train model in background

In [36]:

training = client.training.run(training_metadata)

3.3 Get training id and status

In [37]:

training_id = client.training.get_id(training)

In [40]:

client.training.get_status(training_id)["state"]

Out[40]:

'completed'

3.4 Get training details

In [ ]:

import json

training_details = client.training.get_details(training_id)
print(json.dumps(training_details, indent=2))

List trainings

In [ ]:

client.training.list(limit=5)

Cancel training

You can cancel the training run by calling the method below. Tip: If you want to delete train runs and results add hard_delete=True as a parameter.

client.training.cancel(training_id)

4. Persist trained model

4.1 Publish model

In [42]:

software_spec_uid = client.software_specifications.get_id_by_name('tensorflow_rt23.1-py3.10')

In [43]:

model_meta_props = {
    client.repository.ModelMetaNames.NAME: "Keras MNIST",
    client.repository.ModelMetaNames.TYPE: "tensorflow_2.12",
    client.repository.ModelMetaNames.SOFTWARE_SPEC_UID: software_spec_uid,
}

published_model = client.repository.store_model(training_id, meta_props=model_meta_props)
model_uid = client.repository.get_model_id(published_model)

4.2 Get model details

In [ ]:

model_details = client.repository.get_details(model_uid)
print(json.dumps(model_details, indent=2))

List stored models

In [ ]:

client.repository.list_models(limit=5)

5. Deploy and score

5.1 Create online deployment for published model

In [45]:

deployment = client.deployments.create(model_uid, meta_props={
                                            client.deployments.ConfigurationMetaNames.NAME: "Keras MNIST",
                                            client.deployments.ConfigurationMetaNames.ONLINE: {}})

deployment_uid = client.deployments.get_id(deployment)

Out[45]:

#######################################################################################

Synchronous deployment creation for uid: '45a2b1bb-59a6-4d40-ab43-39d348ca471f' started

#######################################################################################


initializingNote: online_url is deprecated and will be removed in a future release. Use serving_urls instead.

Note: online_url is deprecated and will be removed in a future release. Use serving_urls instead.
.Note: online_url is deprecated and will be removed in a future release. Use serving_urls instead.
.Note: online_url is deprecated and will be removed in a future release. Use serving_urls instead.
.Note: online_url is deprecated and will be removed in a future release. Use serving_urls instead.

ready


------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='498157f7-8576-4bd4-8f05-0ae8d695f2c7'
------------------------------------------------------------------------------------------------

5.2 Get deployments details

In [ ]:

deployments_details = client.deployments.get_details(deployment_uid)
print(json.dumps(deployments_details, indent=2))

List deployments

In [ ]:

client.deployments.list(limit=5)

5.3 Score deployed model

Let's plot two digits. Action: Please install matplotlib, numpy

In [47]:

import wget

dataset_filename='mnist.npz'

if not os.path.isfile(dataset_filename):
    dataset_filename = wget.download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cpd4.8/data/mnist/mnist.npz')

In [48]:

import numpy as np

mnist_dataset = np.load(dataset_filename)
x_test = mnist_dataset['x_test']

In [49]:

%matplotlib inline
import matplotlib.pyplot as plt

In [50]:

for i, image in enumerate([x_test[0], x_test[1]]):
    plt.subplot(2, 2, i + 1)
    plt.axis('off')
    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')

Out[50]:

Our input node expects to get data with shape (784,) so we need to reshape our two digits.

In [51]:

image_1 = x_test[0].ravel() / 255
image_2 = x_test[1].ravel() / 255

Prepare scoring payload and score.

In [52]:

scoring_payload = {
    client.deployments.ScoringMetaNames.INPUT_DATA : [
        {'values': [image_1.tolist(), image_2.tolist()]}
    ]
}
scores = client.deployments.score(deployment_uid, meta_props=scoring_payload)
print("Scoring result:\n" + json.dumps(scores, indent=2))

Out[52]:

Scoring result:
{
  "predictions": [
    {
      "id": "dense_2",
      "fields": [
        "prediction",
        "prediction_classes",
        "probability"
      ],
      "values": [
        [
          [
            3.728355935095351e-08,
            2.211097012150276e-07,
            1.2561032235680614e-05,
            2.3223246898851357e-05,
            7.352957132056304e-10,
            5.163227001503401e-07,
            4.822294993878096e-11,
            0.9999523162841797,
            8.347302582478733e-08,
            1.1019522389688063e-05
          ],
          7,
          [
            3.728355935095351e-08,
            2.211097012150276e-07,
            1.2561032235680614e-05,
            2.3223246898851357e-05,
            7.352957132056304e-10,
            5.163227001503401e-07,
            4.822294993878096e-11,
            0.9999523162841797,
            8.347302582478733e-08,
            1.1019522389688063e-05
          ]
        ],
        [
          [
            8.220833042082987e-11,
            8.570060003876279e-07,
            0.9999991655349731,
            7.721396855231433e-09,
            7.992036936757541e-13,
            3.23251495015775e-12,
            3.037745749390197e-11,
            3.7391287982524446e-11,
            9.958642799290374e-10,
            2.8225443849712187e-15
          ],
          2,
          [
            8.220833042082987e-11,
            8.570060003876279e-07,
            0.9999991655349731,
            7.721396855231433e-09,
            7.992036936757541e-13,
            3.23251495015775e-12,
            3.037745749390197e-11,
            3.7391287982524446e-11,
            9.958642799290374e-10,
            2.8225443849712187e-15
          ]
        ]
      ]
    }
  ]
}

6. Clean up

If you want to clean up all created assets:

experiments
trainings
pipelines
model definitions
models
functions
deployments

please follow up this sample notebook.

7. Summary and next steps

You successfully completed this notebook! You learned how to use ibm-watson-machine-learning-client to run experiments. Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Author

Jan Sołtysik, Intern in Watson Machine Learning.

Use Keras and hyperparameter optimization (HPO) to recognize hand-written digits with `ibm-watson-machine-learning`

Learning goals

Contents

1. Set up the environment

Connection to WML

Install and import the `ibm-watson-machine-learning` package

Working with spaces

2. Create model definition

2.1 Prepare model definition metadata

2.2 Get sample model definition content files from git (Python scripts with CNN and MLP)

2.3 Publish model definition

List model definitions

3. Train model

Warning: Before executing deep learning experiment make sure that training data is saved in a folder where Watson Machine Learning Accelerator is installed.

3.1 Prepare training metadata

3.2 Train model in background

3.3 Get training id and status

3.4 Get training details

List trainings

Cancel training

4. Persist trained model

4.1 Publish model

4.2 Get model details

List stored models

5. Deploy and score

5.1 Create online deployment for published model

5.2 Get deployments details

List deployments

5.3 Score deployed model

Prepare scoring payload and score.

6. Clean up

7. Summary and next steps

Author

Product

Resources

Company

Use Keras and hyperparameter optimization (HPO) to recognize hand-written digits with ibm-watson-machine-learning

Learning goals

Contents

1. Set up the environment

Connection to WML

Install and import the ibm-watson-machine-learning package

Working with spaces

2. Create model definition

2.1 Prepare model definition metadata

2.2 Get sample model definition content files from git (Python scripts with CNN and MLP)

2.3 Publish model definition

List model definitions

3. Train model

Warning: Before executing deep learning experiment make sure that training data is saved in a folder where Watson Machine Learning Accelerator is installed.

3.1 Prepare training metadata

3.2 Train model in background

3.3 Get training id and status

3.4 Get training details

List trainings

Cancel training

4. Persist trained model

4.1 Publish model

4.2 Get model details

List stored models

5. Deploy and score

5.1 Create online deployment for published model

5.2 Get deployments details

List deployments

5.3 Score deployed model

Prepare scoring payload and score.

6. Clean up

7. Summary and next steps

Author

Use Keras and hyperparameter optimization (HPO) to recognize hand-written digits with `ibm-watson-machine-learning`

Install and import the `ibm-watson-machine-learning` package