Create a model using the CLI

This topic describes how to create models using the model CLI.

Open a terminal window and log into cdswctl.
Obtain the project ID as described in the following steps:
1. Run the following command:
```
cdswctl projects list
```
  The project ID, your username, and the project name are displayed. For example:
  
  1: john-smith/petal-length-predictor
2. Note the project ID, which is a number in front of your project name.
  In this case, it is "1".
Run the following command while specifying the project name and note the engine image ID:

note
The following examples are specific to projects configured to use legacy engines and projects configured to use runtimes. Be sure to use the commands appropriate to your project configuration.

For projects configured to use legacy engines:
```
cdswctl engine-images list -p <project-name>
```
For example,
```
cdswctl engine-images list -p john-smith/petal-length-predictor
```
For projects configured to use runtimes:
```
cdswctl runtimes list
```
Depending on your local setup, you may get a more readable output by post-processing the result with the following command:
```
cdswctl runtimes list | python3 -m json.tool
```
For this example you should pick a runtime with a Python kernel and Workbench editor. Depending on your local setup, you may filter the results using the following command:
```
cdswctl runtimes list | jq  '.runtimes[] | select((.editor == "Workbench") and (.kernel | contains("Python")))'
```

Create a model by using the following command:

For projects configured to use legacy engines:

cdswctl models create 
--kernel="python3" 
--targetFilePath="predict.py" 
--targetFunctionName="predict" 
--name="Petal Length Predictor" 
--cpuMillicores=1000 
--memoryMb=2000 
--description="Model of the Iris dataset" 
--replicationType=fixed 
--numReplicas=1 
--visibility="private" 
--autoBuildModel 
--autoDeployModel 
--projectId=<project ID> 
--examples='{"request":{"petal_length":1}}'  
--engineImageId=<engine image ID from before>

For projects configured to use runtimes:

cdswctl models create 
--targetFilePath="predict.py" 
--targetFunctionName="predict" 
--name="Petal Length Predictor" 
--cpuMillicores=1000 
--memoryMb=2000 
--description="Model of the Iris dataset" 
--replicationType=fixed 
--numReplicas=1 
--visibility="private" 
--autoBuildModel 
--autoDeployModel 
--projectId=<project ID> 
--examples='{"request":{"petal_length":1}}'  
--runtimeId=<runtime ID obtained above>

If the command runs successfully, the system displays the model details in a JSON format.

For more information about the models create command parameters, run the following command:
```
cdswctl models create --help
```