Importing models to a deployment space

Importing a model by using UI

To import a model by using UI:

1. From the Assets tab of your space in Watson Studio, click Import assets.
2. Select Local file and then select Model.
3. Select the model file that you want to import and click Import.

Importing a model object

To import a model object:

1. If your model is located in a remote location, follow Downloading a model that is stored in a remote location, and then De-serializing models.
2. Store the model object in your Watson Machine Learning repository. For details, refer to Storing model in Watson Machine Learning repository.

Importing a model by using a path to a file

To import a model by using a path to a file:

1. If your model is located in a remote location, follow Downloading a model that is stored in a remote location to download it.

2. If your model is located locally, place it in a specific directory:

     !cp <saved model> <target directory>
     !cd <target directory>
   

3. For Scikit-learn, XGBoost, Tensorflow, and PyTorch models, if the downloaded file is not a .tar.gz archive, make an archive:

     !tar -zcvf <saved model>.tar.gz <saved model>
   

   The model file must be at the top level of the directory, for example:

   assets/
   <saved model>
   variables/
   variables/variables.data-00000-of-00001
   variables/variables.index
   

4. Use the path to the saved file to store the model file in your Watson Machine Learning repository. For details, refer to Storing model in Watson Machine Learning repository.

Importing a model by using a path to a directory

To import a model by using a path to a directory:

1. If your model is located in a remote location, refer to Downloading a model stored in a remote location.

2. If your model is located locally, place it in a specific directory:

   !cp <saved model> <target directory>
   !cd <target directory>
   

   For scikit-learn, XGBoost, Tensorflow, and PyTorch models, the model file must be at the top level of the directory, for example:

   assets/
   <saved model>
   variables/
   variables/variables.data-00000-of-00001
   variables/variables.index
   

3. Use the directory path to store the model file in your Watson Machine Learning repository. For details, refer to Storing model in Watson Machine Learning repository.

Downloading a model stored in a remote location

Follow this sample code to download your model from a remote location:

import os
from wget import download

target_dir = '<target directory name>'
if not os.path.isdir(target_dir):
    os.mkdir(target_dir)
filename = os.path.join(target_dir, '<model name>')
if not os.path.isfile(filename):
    filename = download('<url to model>', out = target_dir)

PMML models

• The only supported type for models that are imported from PMML is: web service.
• The PMML file must have the .xml file extension.
• PMML models cannot be used in an SPSS stream flow.
• The PMML file must not contain a prolog. Depending on the library that you are using when you save your model, a prolog might be added to the beginning of the file by default. For example, if your file contains a prolog string such as spark-mllib-lr-model-pmml.xml, remove the string before you import the PMML file to the deployment space.

Depending on the library that you are using when you save your model, a prolog might be added to the beginning of the file by default, like in this example:

::::::::::::::
spark-mllib-lr-model-pmml.xml
::::::::::::::

You must remove that prolog before you can import the PMML file to Watson Machine Learning.

Spark MLlib models

• Only classification and regression models are supported.
• Custom transformers, user-defined functions, and classes are not supported.

Scikit-learn models

• .pkl file is the supported import format.
• To serialize/pickle the model, use the joblib package.
• Only classification and regression models are supported.
• Pandas Dataframe input type for predict() API is not supported.
• Supported deployment types for scikit-learn models are: web service and virtual deployment.

XGBoost models

• .pkl file is the supported import format.
• To serialize/pickle the model, use the joblib package.
• Only classification and regression models are supported.
• Pandas Dataframe input type for predict() API is not supported.
• Supported deployment types for XGBoost models are: web service and virtual deployment.

TensorFlow models

• To save/serialize a TensorFlow model, use the tf.saved_model.save() method.
• tf.estimator is not supported.
• The only supported deployment types for TensorFlow models are: web service and batch.
• For more information on supported frameworks, see Supported frameworks.

PyTorch models

• The only supported deployment type for PyTorch models is: web service.

• For a Pytorch model to be importable to Watson Machine Learning, it must be previously exported to .onnx format. Refer to this code.

  torch.onnx.export(<model object>, <prediction/training input data>, "<serialized model>.onnx", verbose=True, input_names=<input tensor names>, output_names=<output tensor names>)
  

De-serializing scikit-learn and XGBoost models

Use this code to de-serialize your scikit-learn and XGBoost model:

import joblib

<your_model> = joblib.load("<saved model>")

De-serializing Spark MLlib models

Use this code to de-serialize your Spark MLlib model:

from pyspark.ml import PipelineModel

<your model> = PipelineModel.load("<saved model>")