GitHub Repository: ibm/watson-machine-learning-samples
Path: blob/master/cloud/notebooks/python_sdk/deployments/pytorch/Use Pytorch to recognize hand-written digits.ipynb
⁶⁴⁰⁵ views

Kernel: Python 3 (ipykernel)

Use Pytorch to recognize hand-written digits with `ibm-watsonx-ai`

This notebook facilitates Pytorch ML library in watsonx.ai Runtime service. It contains steps and code to work with ibm-watsonx-ai library available in PyPI repository. It also introduces commands for getting model and training data, persisting model, deploying model and scoring it.

Some familiarity with Python is helpful. This notebook uses Python 3.11.

Learning goals

The learning goals of this notebook are:

Download an externally trained Pytorch model with dataset.
Persist an external model in watsonx.ai Runtime repository.
Deploy model for online scoring using client library.
Score sample records using client library.

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:

Create a watsonx.ai Runtime Service instance (a free plan is offered and information about how to create the instance can be found here).

Install and import the `ibm-watsonx-ai` and dependecies

Note: ibm-watsonx-ai documentation can be found here.

In [ ]:

!pip install wget
!pip install -U ibm-watsonx-ai | tail -n 1

Connection to watsonx.ai Runtime

Authenticate the watsonx.ai Runtime service on IBM Cloud. You need to provide platform api_key and instance location.

You can use IBM Cloud CLI to retrieve platform API Key and instance location.

API Key can be generated in the following way:

ibmcloud login
ibmcloud iam api-key-create API_KEY_NAME

In result, get the value of api_key from the output.

Location of your watsonx.ai Runtime instance can be retrieved in the following way:

ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance INSTANCE_NAME

In result, get the value of location from the output.

Tip: Your Cloud API key can be generated by going to the Users section of the Cloud console. From that page, click your name, scroll down to the API Keys section, and click Create an IBM Cloud API key. Give your key a name and click Create, then copy the created key and paste it below. You can also get a service specific url by going to the Endpoint URLs section of the watsonx.ai Runtime docs. You can check your instance location in your watsonx.ai Runtime Service instance details.

You can also get service specific apikey by going to the Service IDs section of the Cloud Console. From that page, click Create, then copy the created key and paste it below.

Action: Enter your api_key and location in the following cell.

In [29]:

api_key = 'PASTE YOUR PLATFORM API KEY HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'

In [1]:

from ibm_watsonx_ai import Credentials

credentials = Credentials(
    api_key=api_key,
    url='https://' + location + '.ml.cloud.ibm.com'
)

In [2]:

from ibm_watsonx_ai import APIClient

client = APIClient(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 Deployment Spaces Dashboard to create one.

Click New Deployment Space
Create an empty space
Select Cloud Object Storage
Select watsonx.ai Runtime instance and press Create
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 watsonx.ai Runtime, you need to set space which you will be using.

In [4]:

client.set.default_space(space_id)

Out[4]:

'SUCCESS'

2. Download externally created Pytorch model and data

In this section, you will download externally created Pytorch models and data used for training it.

In [5]:

import os
import wget

data_dir = 'MNIST_DATA'
if not os.path.isdir(data_dir):
    os.mkdir(data_dir)
    
model_path = os.path.join(data_dir, 'mnist_pytorch.tar.gz')
if not os.path.isfile(model_path):
    wget.download('https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/models/pytorch/mnist_pytorch.tar.gz', out=data_dir)

In [6]:

import os
import wget

data_dir = 'MNIST_DATA'
if not os.path.isdir(data_dir):
    os.mkdir(data_dir)
    
filename = os.path.join(data_dir, 'mnist.npz')
if not os.path.isfile(filename):
    wget.download('https://s3.amazonaws.com/img-datasets/mnist.npz', out=data_dir)

In [7]:

import numpy as np

dataset = np.load(filename)
x_test = dataset['x_test']

3. Persist externally created Pytorch ONNX model

In this section, you will learn how to store your model in watsonx.ai Runtime repository by using the IBM watsonx.ai SDK.

3.1: Publish model

Publish model in watsonx.ai Runtime repository on Cloud.

Define model name, autor name and email.

In [8]:

sofware_spec_id = client.software_specifications.get_id_by_name("runtime-24.1-py3.11")

In [9]:

metadata = {
            client.repository.ModelMetaNames.NAME: 'External pytorch model',
            client.repository.ModelMetaNames.TYPE: 'pytorch-onnx_2.1',
            client.repository.ModelMetaNames.SOFTWARE_SPEC_ID: sofware_spec_id
}

published_model = client.repository.store_model(
    model=model_path,
    meta_props=metadata)

3.2: Get model details

In [12]:

import json

published_model_id = client.repository.get_model_id(published_model)
model_details = client.repository.get_details(published_model_id)
print(json.dumps(model_details, indent=2))

Out[12]:

{
  "entity": {
    "hybrid_pipeline_software_specs": [],
    "software_spec": {
      "id": "336b29df-e0e1-5e7d-b6a5-f6ab722625b2",
      "name": "runtime-23.1-py3.10"
    },
    "type": "pytorch-onnx_2.0"
  },
  "metadata": {
    "created_at": "2024-03-06T13:07:08.463Z",
    "id": "a2aeb6f4-e2cf-4358-8460-793ede32149d",
    "modified_at": "2024-03-06T13:07:18.370Z",
    "name": "External pytorch model",
    "owner": "IBMid-55000091VC",
    "resource_key": "d038c6cc-de01-4121-a564-66cb2e90fe56",
    "space_id": "93ee84d1-b7dd-42b4-b2ca-121bc0c86315"
  },
  "system": {
    "warnings": []
  }
}

3.3 Get all models

In [ ]:

models_details = client.repository.list_models()

4. Deploy and score in a Cloud

In this section you will learn how to create online scoring and to score a new data record by using the IBM watsonx.ai SDK.

4.1: Create model deployment

Create online deployment for published model

In [14]:

metadata = {
    client.deployments.ConfigurationMetaNames.NAME: "Deployment of external pytorch model",
    client.deployments.ConfigurationMetaNames.ONLINE: {}
}

created_deployment = client.deployments.create(published_model_id, meta_props=metadata)

Out[14]:

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

Synchronous deployment creation for uid: 'a2aeb6f4-e2cf-4358-8460-793ede32149d' started

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

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

ready

------------------------------------------------------------------------------------------------
Successfully finished deployment creation, deployment_uid='8062b683-26c3-43b8-ae4b-f488f14015e8'
------------------------------------------------------------------------------------------------

Note: Here we use deployment url saved in published_model object. In next section, we show how to retrive deployment url from watsonx.ai Runtime instance.

In [15]:

deployment_id = client.deployments.get_id(created_deployment)

Now you can print an online scoring endpoint.

In [ ]:

scoring_endpoint = client.deployments.get_scoring_href(created_deployment)
print(scoring_endpoint)

You can also list existing deployments.

In [ ]:

client.deployments.list()

4.2: Get deployment details

In [ ]:

client.deployments.get_details(deployment_id)

4.3: Score

You can use below method to do test scoring request against deployed model.

Let's first visualize two samples from dataset, we'll use for scoring.

In [23]:

%matplotlib inline
import matplotlib.pyplot as plt

In [24]:

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[24]:

Prepare scoring payload with records to score.

In [25]:

score_0 = [x_test[0].tolist()]
score_1 = [x_test[1].tolist()]

In [26]:

scoring_payload = {"input_data": [{"values": [score_0, score_1]}]}

Use client.deployments.score() method to run scoring.

In [27]:

predictions = client.deployments.score(deployment_id, scoring_payload)

Let's print the result of predictions.

In [28]:

print(json.dumps(predictions, indent=2))

Out[28]:

{
  "predictions": [
    {
      "values": [
        [
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          1.0,
          2.9382650407717056e-39,
          2.9382650407717056e-39
        ],
        [
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          1.0,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39,
          2.9382650407717056e-39
        ]
      ]
    }
  ]
}

As you can see, prediction probabilities point to proper classes as displayed above from test dataset.

5. 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.

6. Summary and next steps

You successfully completed this notebook! You learned how to use Pytorch machine learning library as well as watsonx.ai Runtime for model creation and deployment. Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.

Authors

Daniel Ryszka, Software Engineer

Mateusz Szewczyk, Software Engineer at watsonx.ai

Use Pytorch to recognize hand-written digits with `ibm-watsonx-ai`

Learning goals

Contents

1. Set up the environment

Install and import the `ibm-watsonx-ai` and dependecies

Connection to watsonx.ai Runtime

Working with spaces

2. Download externally created Pytorch model and data

3. Persist externally created Pytorch ONNX model

3.1: Publish model

Publish model in watsonx.ai Runtime repository on Cloud.

3.2: Get model details

3.3 Get all models

4. Deploy and score in a Cloud

4.1: Create model deployment

Create online deployment for published model

4.2: Get deployment details

4.3: Score

5. Clean up

6. Summary and next steps

Authors

Product

Resources

Company

Use Pytorch to recognize hand-written digits with ibm-watsonx-ai

Learning goals

Contents

1. Set up the environment

Install and import the ibm-watsonx-ai and dependecies

Connection to watsonx.ai Runtime

Working with spaces

2. Download externally created Pytorch model and data

3. Persist externally created Pytorch ONNX model

3.1: Publish model

Publish model in watsonx.ai Runtime repository on Cloud.

3.2: Get model details

3.3 Get all models

4. Deploy and score in a Cloud

4.1: Create model deployment

Create online deployment for published model

4.2: Get deployment details

4.3: Score

5. Clean up

6. Summary and next steps

Authors

Use Pytorch to recognize hand-written digits with `ibm-watsonx-ai`

Install and import the `ibm-watsonx-ai` and dependecies