Cloudmesh OpenAPI Service Generator

Note: The README.md page is outomatically generated, do not edit it. To modify change the content in https://github.com/cloudmesh/cloudmesh-openapi/blob/main/README-source.md Curley brackets must use two in README-source.md

Publication

A draft paper is available at

• https://github.com/laszewski/laszewski.github.io/raw/master/papers/vonLaszewski-openapi.pdf

The source to the paper is at

• https://github.com/cyberaide/paper-openapi

Prerequisites

• We recommend Python 3.8.2 Python or newer.
• We recommend pip version 20.0.2 or newer
• We recommend that you use a venv (see developer install)
• MongoDB installed as regular program not as service, which can easily be done with cloudmesh on macOS, Linux, and Windows.
• Please run cms gui quick to initialize the password for the mongodb server

Note: On windows you can use gitbash so you can use bash and can use the same commands as on Linux or macOS. Otherwise, please use the appropriate backslashes to access the path.

Installation

The installation is rather simple and is documented next.

python -m venv ~/ENV3
source ~/ENV3/bin/activate 
mkdir cm
cd cm
pip install cloudmesh-installer
cloudmesh-installer get openapi 
cms help
cms gui quick
# fill out mongo variables
# make sure autinstall is True
cms config set cloudmesh.data.mongo.MONGO_AUTOINSTALL=True
cms admin mongo install --force
# Restart a new terminal to make sure mongod is in your path
cms init

If you like to know more about the installation of cloudmesh, please visit the Cloudmesh Manual.

Command Overview

When getting started using cloudmes openapi, please first call to get familiar with the options you have:

cms help openapi

We include the manual page later on in this document.

Quick Start

Next we provide a very simple quickstart guide to steps to generate a simple microservice that returns the CPU information of your computer. We demonstrate how to generate, start, and stop the servive.

Navigate to ~/cm/cloudmesh-openapi folder. In this folder you will have a file called cpu.py from which we will generate the server.

First, generate an OpenAPI YAML file with the convenient command

cms openapi generate get_processor_name \
    --filename=./tests/server-cpu/cpu.py

This will create the file cpu.yaml that contains the OpenAPI specification. To start the service from this specification simply use the command

cms openapi server start ./tests/server-cpu/cpu.yaml

Now that the service is up and running, you can issue a request for example via the commandline with

curl -X GET "http://localhost:8080/cloudmesh/get_processor_name" \
     -H "accept: text/plain"

To view the automatically generated documentation, you can go to your browser and open the link

• http://localhost:8080/cloudmesh/ui

You can also look at the status of the server with the command

cms openapi server list

Once yo no longer need the service, you can stop it with

cms openapi server stop cpu

Quickstart to Creating your own Microservice

Cloudmesh uses introspection to generate an OpenAPI compliant YAML specification that will allow your Python code to run as a web service. For this reason, any code you write must conform to a set of guidelines.

• The parameters and return values of any functions you write must use python typing
• Your functions must include docstrings
• If a function uses or returns a class, that class must be defined as a dataclass in the same file

Next we demonstrate how to create your own microservice. We provide two examples. One in which we return a float, te other one in which the return value is wrapped in a json object.

Returning a Float

We define a function that adds tow values. Note how x, y, and the return value are all typed. In this case they are all float, but other types are supported. The description in the docstring will be added to your YAML specification to help describe what the function does.

def add(x: float, y: float) -> float:
    """
    adding float and float.
    :param x: x value
    :type x: float
    :param y: y value
    :type y: float
    :return: result
    :return type: float
    """
    result = x + y

    return result

To generate, start, retrieve a result, and stop the service you can use the following command sequence:

cms openapi generate add --filename=./tests/add-float/add.py
cms openapi server start ./tests/add-float/add.yaml 
curl -X GET "http://localhost:8080/cloudmesh/add?x=1&y=2" -H  "accept: text/plain"
# This command returns
> 3.0
cms openapi server stop add

Returning a Json Object

Often we like to wrap the return value into a json string object, which can easily be done by modifying the previous example as showcased next.

from flask import jsonify

def add(x: float, y: float) -> str:
    """
    adding float and float.
    :param x: x value
    :type x: float
    :param y: y value
    :type y: float
    :return: result
    :return type: float
    """
    result = {"result": x + y}

    return jsonify(result)

To generate, start, retrieve a result, and stop the service you can use the following command sequence:

cms openapi generate add --filename=./tests/add-json/add.py
cms openapi server start ./tests/add-json/add.yaml 
curl -X GET "http://localhost:8080/cloudmesh/add?x=1&y=2" -H  "accept: text/plain"
# This command returns
> {"result":3.0}
cms openapi server stop add

As usual in both cases the web browser can be used to inspect the documentation as well as to test running the example, by filling out the form.

Details of the cms openapi command

The gaol as stated earlier is to transform a simple python function as a service

Generating OpenAPI specification

Once you have a Python function you would like to deploy as a web service, you can generate the OpenAPI specification. Navigate to your .py file's directory and generate the YAML. This will print information to your console about the YAML file that was generated.

cms openapi generate [function_name] --filename=[filename.py]

If you would like to include more than one function in your web service, like addition and subtraction, use the --all_functions flag. This will ignore functions whose names start with '_'.

cms openapi generate --filename=[filename.py] --all_functions

You can even write a class like Calculator that contains functions for addition, subtraction, etc. You can generate a specification for an entire class by using the --import_class flag.

cms openapi generate [ClassName] --filename=[filename.py] --import_class

Starting a server

Once you have generated a specification, you can start the web service on your localhost by providing the path to the YAML file. This will print information to your console about the server

cms openapi server start ./[filename.yaml]

  Starting: [server name]
  PID:      [PID]
  Spec:     ./[filename.py]
  URL:      http://localhost:8080/cloudmesh
  Cloudmesh UI:      http://localhost:8080/cloudmesh/ui

Sending requests to the server

Now you have two options to interact with the web service. The first is to navigate the the Cloudmesh UI and click on each endpoint to test the functionality. The second is to use curl commands to submit requests.

We have already shown you earlier in our quickstart how to apply this to a service such as our add service

$ curl -X GET "http://localhost:8080/cloudmesh/add?x=1.2&y=1.5" -H "accept: text/plain"
>   2.7

Stopping the server

Now you can stop the server using the name of the server. If you forgot the name, use cms openapi server ps to get a list of server processes.

$ cms openapi server stop [server name]

Basic Auth

To use basic http authentication with a user password for the generated API, add the following flag at the end of a cms openapi generate command:

--basic_auth=<username>:<password>

We plan on supporting more security features in the future. Example:

cms openapi generate get_processor_name \
    --filename=./tests/server-cpu/cpu.py \
    --basic_auth=admin:secret

Manual Page

openapi generate [FUNCTION] --filename=FILENAME
                         [--serverurl=SERVERURL]
                         [--yamlfile=YAML]
                         [--import_class]
                         [--all_functions]
                         [--enable_upload]
                         [--verbose]
                         [--basic_auth=CREDENTIALS]
openapi server start YAML [NAME]
              [--directory=DIRECTORY]
              [--port=PORT]
              [--server=SERVER]
              [--host=HOST]
              [--verbose]
              [--debug]
              [--fg]
              [--os]
openapi server stop NAME
openapi server list [NAME] [--output=OUTPUT]
openapi server ps [NAME] [--output=OUTPUT]
openapi register add NAME ENDPOINT
openapi register filename NAME
openapi register delete NAME
openapi register list [NAME] [--output=OUTPUT]
openapi TODO merge [SERVICES...] [--dir=DIR] [--verbose]
openapi TODO doc FILE --format=(txt|md)[--indent=INDENT]
openapi TODO doc [SERVICES...] [--dir=DIR]
openapi sklearn FUNCTION MODELTAG
openapi sklearnreadfile FUNCTION MODELTAG
openapi sklearn upload --filename=FILENAME

Arguments:
  FUNCTION  The name for the function or class
  MODELTAG  The arbirtary name choosen by the user to store the Sklearn trained model as Pickle object
  FILENAME  Path to python file containing the function or class
  SERVERURL OpenAPI server URL Default: https://localhost:8080/cloudmesh
  YAML      Path to yaml file that will contain OpenAPI spec. Default: FILENAME with .py replaced by .yaml
  DIR       The directory of the specifications
  FILE      The specification

Options:
  --import_class         FUNCTION is a required class name instead of an optional function name
  --all_functions        Generate OpenAPI spec for all functions in FILENAME
  --debug                Use the server in debug mode
  --verbose              Specifies to run in debug mode
                         [default: False]
  --port=PORT            The port for the server [default: 8080]
  --directory=DIRECTORY  The directory in which the server is run
  --server=SERVER        The server [default: flask]
  --output=OUTPUT        The outputformat, table, csv, yaml, json
                         [default: table]
  --srcdir=SRCDIR        The directory of the specifications
  --destdir=DESTDIR      The directory where the generated code
                         is placed

Description:
This command does some useful things.

openapi TODO doc FILE --format=(txt|md|rst) [--indent=INDENT]
    Sometimes it is useful to generate teh openaopi documentation
    in another format. We provide fucntionality to generate the
    documentation from the yaml file in a different formt.

openapi TODO doc --format=(txt|md|rst) [SERVICES...]
    Creates a short documentation from services registered in the
    registry.

openapi TODO merge [SERVICES...] [--dir=DIR] [--verbose]
    Merges tow service specifications into a single servoce
    TODO: do we have a prototype of this?


openapi sklearn sklearn.linear_model.LogisticRegression
    Generates the .py file for the Model given for the generator

openapi sklearnreadfile sklearn.linear_model.LogisticRegression
Generates the .py file for the Model given for the generator which supports reading files

openapi generate [FUNCTION] --filename=FILENAME
                             [--serverurl=SERVERURL]
                             [--yamlfile=YAML]
                             [--import_class]
                             [--all_functions]
                             [--enable_upload]
                             [--verbose]
                             [--basic_auth=CREDENTIALS]
    Generates an OpenAPI specification for FUNCTION in FILENAME and
    writes the result to YAML. Use --import_class to import a class
    with its associated class methods, or use --all_functions to 
    import all functions in FILENAME. These options ignore functions
    whose names start with '_'. Use --enable_upload to add file
    upload functionality to a copy of your python file and the
    resulting yaml file.

    For optional basic authorization, we support (temporarily) a single user
    credential. CREDENTIALS should be formatted as follows:

    user:password

    Example: --basic_auth=admin:secret

openapi server start YAML [NAME]
                  [--directory=DIRECTORY]
                  [--port=PORT]
                  [--server=SERVER]
                  [--host=HOST]
                  [--verbose]
                  [--debug]
                  [--fg]
                  [--os]
    starts an openapi web service using YAML as a specification
    TODO: directory is hard coded as None, and in server.py it
      defaults to the directory where the yaml file lives. Can
      we just remove this argument?

openapi server stop NAME
    stops the openapi service with the given name
    TODO: where does this command has to be started from

openapi server list [NAME] [--output=OUTPUT]
    Provides a list of all OpenAPI services in the registry

openapi server ps [NAME] [--output=OUTPUT]
    list the running openapi service

openapi register add NAME ENDPOINT
    Openapi comes with a service registry in which we can register
    openapi services.

openapi register filename NAME
    In case you have a yaml file the openapi service can also be
    registerd from a yaml file

openapi register delete NAME
    Deletes the names service from the registry

openapi register list [NAME] [--output=OUTPUT]
    Provides a list of all registerd OpenAPI services

Basic Examples

Please follow Pytest Information document for pytests related information

We have included a significant number of tests that aso serve as examples

Example: One function in a python file

1. Please check Python file.

2. Run below command to generate yaml file and start server

   cms openapi generate get_processor_name --filename=./tests/server-cpu/cpu.py
   

Example: Multiple functions in python file

1. Please check Python file

2. Run below command to generate yaml file and start server

   cms openapi generate --filename=./tests/generator-calculator/calculator.py --all_functions
   cms openapi generate server start ./tests/generator-calculator/calculator.py
   

Example: Function(s) in python class file

1. Please check Python file

2. Run below command to generate yaml file and start server

   cms openapi generate Calculator \
       --filename=./tests/generator-testclass/calculator.py \
       --import_class"
   cms openapi server start ./tests/generator-testclass/calculator.yaml
   curl -X GET "http://localhost:8080/cloudmesh/Calculator/multiplyint?x=1&y=5"
   cms openapi server stop Calculator
   

Example: Uploading data

Code to handle uploads is located in cloudmesh-openapi/tests/generator-upload. The code snippet in uploadexample.py and the specification in uploadexample.yaml can be added to existing projects by adding the --enable_upload flag to the cms openapi generate command. The web service will be able to retrieve the uploaded file from ~/.cloudmesh/upload-file/.

Upload example

This example shows how to upload a CSV file and how the web service can retrieve it.

First, generate the OpenAPI specification and start the server

cms openapi generate print_csv2np \
    --filename=./tests/generator-upload/csv_reader.py \
    --enable_upload
cms openapi server start ./tests/generator-upload/csv_reader.yaml

Next, navigate to localhost:8080/cloudmesh/ui. Click to open the /upload endpoint, then click 'Try it out.' Click to choose a file to upload, then upload tests/generator-upload/np_test.csv. Click 'Execute' to complete the upload.

The uploaded file will be located at ~/.cloudmesh/upload-file/[filename]. The file tests/generator-upload/csv_reader.py contains some example code to retrieve the array in the uploaded file. To see this in action, click to open the /print_csv2np endpoint, then click 'Try it out.' Enter "np_test.csv" in the field that prompts for a filename, and then click Execute to view the numpy array defined by the CSV file.

Example: Pipeline Anova SVM Example

This example is based on the sklearn example here

In this example, we will upload a data set to the server, tell the server to train the model, and utilize said model for predictions.

Firstly, ensure we are in the correct directory.

$ pwd
~/cm/cloudmesh-openapi

Then install some requirements needed for this example

$ pip install sklearn pandas

Let us generate the yaml file from our python file to generate the proper specs for our service.

$ cms openapi generate PipelineAnovaSVM \
      --filename=./tests/Scikitlearn-experimental/sklearn_svm.py \
      --import_class --enable_upload

Now let us start the server

$ cms openapi server start ./tests/Scikitlearn-experimental/sklearn_svm.yaml

The server should now be active. Navigate to http://localhost:8080/cloudmesh/ui. We now have a nice user inteface to interact with our newly generated API. Let us upload the data set. We are going to use the iris data set in this example. We have provided it for you to use. Simply navigate to the /upload endpoint by clicking on it, then click Try it out.

We can now upload the file. Click on Choose File and upload the data set located at ./tests/Scikitlearn-experimental/iris.data. Simply hit Execute after the file is uploaded. We should then get a return code of 200 (telling us that everything went ok).

The server now has our dataset. Let us now navigate to the /train endpoint by, again, clicking on it. Similarly, click Try it out. The parameter being asked for is the filename. The filename we are interested in is iris.data. Then click execute. We should get another 200 return code with a Classification Report in the Response Body.

           0       1.00      1.00      1.00         8
           1       0.85      1.00      0.92        11
           2       1.00      0.89      0.94        19

    accuracy                           0.95        38
   macro avg       0.95      0.96      0.95        38
weighted avg       0.96      0.95      0.95        38

Our model is now trained and stored on the server. Let us make a prediction now. As we have done, navigate to the /make_prediction endpoint. The information we need to provide is the name of the model we have trained as well as some test data. The name of the model will be the same as the name of the data-file (ie. iris). So type in iris into the model_name field. Finally for params, let us use the example 5.1, 3.5, 1.4, 0.2 as the model expects 4 values (attributes). After clicking execute, we should received a response with the classification the model has made given the parameters.

The response received should be as follows:

"Classification: ['Iris-setosa']"

We can make as many predictions as we like. When finished, we can shut down the server.

$ cms openapi server stop sklearn_svm

Example to Run AI Services in the Cloud

Google

After you create your google cloud account, it is recommended to download and install Google's Cloud SDK. This will enable CLI. Make sure you enable all the required services.

For example:

gcloud services enable servicemanagement.googleapis.com
gcloud services enable endpoints.googleapis.com

and any other services you might be using for your specific Cloud API function.

To begin using the tests for any of the Google Cloud Platform AI services you must first set up a Google account (set up a free tier account): Google Account Setup

After you create your google cloud account, it is recommended to download and install Google's Cloud SDK. This will enable CLI. Make sure you enable all the required services.

For example:

gcloud services enable servicemanagement.googleapis.com
gcloud services enable servicecontrol.googleapis.com
gcloud services enable endpoints.googleapis.com

and any other services you might be using for your specific Cloud API function.

It is also required to install the cloudmesh-cloud package, if not already installed:

cloudmesh-installer get cloud
cloudmesh-installer install cloud

This will allow you automatically fill out the cloudmesh yaml file with your credentials once you generate the servcie account JSON file.

After you have verified your account is created you must then give your account access to the proper APIs and create a project in the Google Cloud Platform(GCP) console.

1. Go to the project selector

2. Follow directions from Google to create a project linked to your account

Quickstart Google Python API

pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib

• For quickstart in using Google API for Python visit here

Setting up your Google account

Before you generate the service account JSON file for your account you will want to enable a number of services in the GCP console.

• Google Compute
• Billing
• Cloud Natural Language API
• Translate API

1. To do this you will need to click the menu icon in the Dashboard navigation bar. Ensure you are in the correct porject.

2. Once that menu is open hover over the "APIs and Services" menu item and click on "Dashboard" in the submenu.

3. At the dashboard click on the "+ Enable APIs and Services" button at the top of the dashboard

4. Search for cloud natural language to find the API in the search results and click the result

5. Once the page opens click "Enable"

6. Do the same for the translate API to enable that as well

7. Do the same for the compute engine API to enable that as well

You must now properly set up the account roles to ensure you will have access to the API. Follow the directions from Google to set up proper authentication

Make you account an owner for each of the APIs in the IAM tool as directed in the authentication steps for the natural language API. This makes your service account have proper access to the required APIs and once the private key is downloaded those will be stored there.

It is VERY important that you create a service account and download the private key as described in the directions from Google. If you do not the cms google commands will not work properly.

Once you have properly set up your permissions please make sure you download your JSON private key for the service account that has permissions set up for the required API services. These steps to download are found here. Please take note of where you store the downloaded JSON and copy the path string to a easily accessible location.

The client libraries for each API are included in teh requirements.txt file for the openapi proejct and should be isntalled when the package is installed. If not, follow directions outlined by google install each package:

google-cloud-translate
google-cloud-language

To pass the information from your service account private key file ot the cloudmesh yaml file run the following command:

cms register update --kind=google --service=compute --filename=GOOGLE_JSON_FILE

Running the Google Natural Language and Translate REST Services

1. Navigate to the ~/.cloudmesh repo and create a cache directory for your text examples you would like to analyze.

   mkdir text-cache
   

2. Add any plain text files your would like to analyze to this directory with a name that has no special characters or spaces. You can copy the files at this location, ./cloudmesh-openapi/tests/textanaysis-example-text/reviews/ into the text-cache if you want to use provided examples.

3. Navigate to the ./cloudmesh-openapi directory on your machine

4. Utilize the generate command to create the OpenAPI spec

   cms openapi generate TextAnalysis --filename=./tests/generator-natural-lang/natural-lang-analysis.py --all_functions
   

5. Start the server after the yaml file is generated ot the same directory as the .py file

   cms openapie start server ./tests/generator-natural-lang/natural-lang-analysis.yaml
   

6. Run a curl command against the newly running server to verify it returns a result as expected.

   • Sample text file name is only meant to be the name of the file not the full path.

     curl -X GET "http://127.0.0.1:8080/cloudmesh/analyze?filename=SAMPLE_TEXT_FILENAME&cloud=google"
     

   • This is currently only ready to translate a single word through the API.

     curl -X GET "http://127.0.0.1:8080/cloudmesh/translate_text?cloud=google&text=WORD_TO_TRANSLATE&lang=LANG_CODE"
     

7. Stop the server

   cms openapi server stop natural-lang-analysis
   

Example using AWS

Sign up for AWS

• Go to https://portal.aws.amazon.com/billing/signup
• Follow online instructions

Create an IAM User

• For instructions, see here

Set up AWS CLI and AWS SDKs

• To download and instructions to install AWS CLI, see here

Install Boto 3

pip install boto3

• For quickstart, vist here

As long as you enable all the services you need for using AWS AI APIs you should be able to write your functions for OpenAPI

Example using Azure

Setting up Azure Sentiment Analysis and Translation Services

1. Create an Azure subscription. If you do not have one, create a free account

2. Create a Text Analysis resource

   • This link will require you to be logged in to the Azure portal

3. Create a Translation Resource

4. The microsoft packages are included in the openapi package requirements file so they should be installed. If they are not, install the following:

   pip install msrest 
   pip install azure-ai-textanalytics
   

5. Navigate to the ~/.cloudmesh repo and create a cache directory for your text examples you would like to analyze.

   mkdir text-cache
   

6. Add any plain text files your would like to analyze to this directory with a name that has no special characters or spaces. You can copy the files at this location, ./cloudmesh-openapi/tests/textanaysis-example-text/reviews/ into the text-cache if you want to use provided examples.

7. Navigate to the ./cloudmesh-openapi directory on your machine

8. Utilize the generate command to create the OpenAPI spec

   cms openapi generate TextAnalysis --filename=./tests/generator-natural-lang/natural-lang-analysis.py --all_functions
   

9. Start the server after the yaml file is generated ot the same directory as the .py file

   cms openapie start server ./tests/generator-natural-lang/natural-lang-analysis.yaml
   

10. Run a curl command against the newly running server to verify it returns a result as expected.

    • Sample text file name is only meant to be the name of the file not the full path.

      curl -X GET "http://127.0.0.1:8080/cloudmesh/analyze?filename=<<sample text file name>>&cloud=azure"
      

    • This is currently only ready to translate a single word through the API.

    • Available language tags are described in the Azure docs

      curl -X GET "http://127.0.0.1:8080/cloudmesh/translate_text?cloud=azure&text=<<word to translate>>&lang=<<lang code>>"
      

11. Stop the server

    cms openapi server stop natural-lang-analysis
    

The natural langauge analysis API can be improved by allowing for full phrase translation via the API. If you contribute to this API there is room for improvement to add custom translation models as well if preferred to pre-trained APIs.

Setting up Azure ComputerVision AI services

Prerequisite

Using the Azure Computer Vision AI service, you can describe, analyze and/ or get tags for a locally stored image or you can read the text from an image or hand-written file.

• Azure subscription. If you do not have one, create a free account before you continue further.

• Create a Computer Vision resource and get the COMPUTER_VISION_SUBSCRIPTION_KEY and COMPUTER_VISION_ENDPOINT. Follow instructions to get the same.

• Install following Python packages in your virtual environment:

  pip install requests
  pip install Pillow
  

• Install Computer Vision client library

  pip install --upgrade azure-cognitiveservices-vision-computervision
  

Steps to implement and use Azure AI image and text REST-services

• Go to ./cloudmesh-openapi directory

• Run following command to generate the YAML files

  cms openapi generate AzureAiImage --filename=./tests/generator-azureai/azure-ai-image-function.py --all_functions --enable_upload
  cms openapi generate AzureAiText --filename=./tests/generator-azureai/azure-ai-text-function.py --all_functions --enable_upload
  

• Verify the YAML files created in ./tests/generator-azureai directory

  azure-ai-image-function.yaml
  azure-ai-text-function.yaml
  

• Start the REST service by running following command in ./cloudmesh-openapi directory

  cms openapi server start ./tests/generator-azureai/azure-ai-image-function.yaml
  

The default port used for starting the service is 8080. In case you want to start more than one REST service, use a different port in following command:

cms openapi server start ./tests/generator-azureai/azure-ai-text-function.yaml --port=<**Use a different port than 8080**>

• Access the REST service using http://localhost:8080/cloudmesh/ui/

• After you have started the azure-ai-image-function or azure-ai-text-function on default port 8080, run following command to upload the image or text_image file

  curl -X POST "http://localhost:8080/cloudmesh/upload" -H  "accept: text/plain" -H  "Content-Type: multipart/form-data" -F "upload=@tests/generator-azureai/<image_name_with_extension>;type=image/jpeg"
  

  Keep your test image files at ./tests/generator-azureai/ directory

• With azure-ai-text-function started on port=8080, in order to test the azure ai function for text detection in an image, run following command

  curl -X GET "http://localhost:8080/cloudmesh/azure-ai-text-function_upload-enabled/get_text_results?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
  

• With azure-ai-image-function started on port=8080, in order to test the azure ai function for describing an image, run following command

  curl -X GET "http://localhost:8080/cloudmesh/azure-ai-image-function_upload-enabled/get_image_desc?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
  

• With azure-ai-image-function started on port=8080, in order to test the azure ai function for analyzing an image, run following command

  curl -X GET "http://localhost:8080/cloudmesh/azure-ai-image-function_upload-enabled/get_image_analysis?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
  

• With azure-ai-image-function started on port=8080, in order to test the azure ai function for identifying tags in an image, run following command

  curl -X GET "http://localhost:8080/cloudmesh/azure-ai-image-function_upload-enabled/get_image_tags?image_name=<image_name_with_extension_uploaded_earlier>" -H "accept: text/plain"
  

• Check the running REST services using following command:

  cms openapi server ps
  

• Stop the REST service using following command(s):

  cms openapi server stop azure-ai-image-function
  cms openapi server stop azure-ai-text-function
  

List of Tests

The following table lists the different test we have, we provide additional information for the tests in the test directory in a README file. Summaries are provided below the table

Test	Short Description	Link
Generator-calculator	Test to check if calculator api is generated correctly. This is to test multiple function in one python file	test_01_generator.py
Generator-testclass	Test to check if calculator api is generated correctly. This is to test multiple function in one python class file	test_02_generator.py
Server-cpu	Test to check if cpu api is generated correctly. This is to test single function in one python file and function name is different than file name	test_03_generator.py
Server-cms	Test to check if cms api is generated correctly. This is to test multiple function in one python file.	test_04_generator.py
Registry	test_001_registry.py - Runs tests for registry. Description is in tests/README.md	Link
Image-Analysis	image_test.py - Runs benchmark for text detection for Google Vision API and AWS Rekognition. Description in image-analysis/README.md	image

For more information about test cases ,please check tests info

• test_001_registry
• test_003_server_manage_cpu
• test_010_generator
• test_011_generator_cpu
• test_012_generator_calculator
• test_015_generator_azureai
• test_020_server_manage
• test_server_cms_cpu

Note that there a many more tests that you can explore.