Metadata-Version: 2.1
Name: clevrml
Version: 0.6
Summary: The Official Package for clevrML.
Home-page: UNKNOWN
License: Apache 2.0
Description: clevrML is an End-to-End platform for creating, editing, deploying and using Machine Learning models using clevr's state-of-the-art technology Active Memory Learning (AML). With AML, you can build and deploy your Machine Learning model in as little as 10 seconds. For more information, please visit www.clevrml.com
        
        ## Table of Contents
        * Install clevrML SDK
        * Quickstart
        * Model Types
        * Official Documentation
        
        
        
        ## Install clevrML SDK
        Currently, the clevrML SDK for the clevrML Developer API is only supported in Python 3, however releases in other languages are coming soon.
        Language | Installation
        ------------ | -------------
        Python 3 | `pip install clevrml`
        
        
        
        
        ### Quickstart
        
        Building a model on clevrML is simple, smooth and efficient. Using the clevrML Python SDK, here is how you can build a **cat and dog Image Classification Model and automatically deploy the model:**
        
        ```python
        from clevrml import Image_Model
        import os
        
        model = Image_Model()
        key = os.environ['clevrml-key']    # see official clevrML docs for obtaining an API Key
        
        model.build_model(
           api_key=key,
           class_names=["cat", "dog"],
           example_folders=["/cat_image_examples/", "/dog_image_examples/"],
           model_name="Pet-Classifier"
        )
        ```
        
        And getting a prediction for your model is just as simple:
        
        
        ```python
        from clevrml import Image_Model
        import os
        
        model = Image_Model()
        key = os.environ['clevrml-key']     # see official clevrML docs for obtaining an API Key
        
        model.predict(
           api_key=key,
           image_file_path="cat_example.jpg",
           model_name="Pet-Classifier"
        )
        ```
        
        With Active Memory Learning, you can edit your model by simply adding new inputs to your models "Memory" to improve performance. You can add to existing classes or add new ones:
        
        
        ```python
        from clevrml import Image_Model
        import os
        
        model = Image_Model()
        key = os.environ['clevrml-key']     # see official clevrML docs for obtaining an API Key
        
        # adding to our cat and dog classes along with creating a new "fish" class.
        model.edit_model(
           api_key=key,
           class_names=["cat", "dog", "fish"],            
           example_folders=["/more_cat_images/", "/more_dog_images/", "/fish_images/"],
           model_name="Pet-Classifier"
        )
        ```
        
        
        ### Model Types
        
        clevrML currently supports the following types of custom models:
        
         Models 
        ------------ |
        Image Classification 
        Forecasting
        Text Classification
        
        
        ### Official Documentation
        
        Please see: https://www.clevrml.com/sdk-docs
Platform: UNKNOWN
Description-Content-Type: text/markdown
