Metadata-Version: 2.1
Name: easytensor
Version: 0.0.8
Summary: The official python cient of EasyTensor
Home-page: https://github.com/EasyTensor/python-client
Author: Kamal Kamalaldin
Author-email: kamal@easytensor.com
License: UNKNOWN
Description: # EasyTensor
        
        The official python client for [EasyTensor](https://easytensor.com).
        
        # Installation
        
        Pretty straightforward.
        
        ```shell
        pip install easytensor
        ```
        
        # Usage
        
        Once you have a model exported to your local storage, you can upload it to easytensor in one line of code.
        
        ## TensorFlow
        
        ### Exporting and uploading a model
        
        ```python
        import easytensor
        import os
        export_path = os.path.join(os.getcwd(), "my_model")
        print("export_path: {}".format(export_path))
        
        # Export the model
        tf.keras.models.save_model(
            model,
            export_path,
            overwrite=True,
            include_optimizer=True,
            save_format=None,
            signatures=None,
            options=None
        )
        
        # Upload it to easytensor.
        model_id, access_token = easytensor.tensorflow.upload_model("My first model", export_path)
        print("model ID:", model_id)
        print("access token:", access_token)
        ```
        
        ### Running prediction on the cloud
        
        ```python
        from pprint import pprint
        import requests
        response = requests.post(
            "https://app.easytensor.com/query/",
            json={
                "instances": [
                    image_to_predict.numpy().tolist()
                ]
            },
            headers={"accessToken": access_token}
        )
        print("Response from server:")
        pprint(response.json())
        ```
        
        # Examples
        
        The library comes with a few example Jupyter notebooks that walk you through a few possible workflows. They are helpful if you are starting out with ML or remote model prediction.
        
        #### Requirements
        
        - Tensorflow 2. TF2 currently (early 2021) [requires a python version 3.5-3.8](https://www.tensorflow.org/install). You will have to install a compatible version of python.
        - virtualenv
        - jupyter notebook
        
        ##### For Mac
        
        ```
        brew install python@3.8
        ```
        
        ##### For Ubuntu
        
        ```
        sudo apt install python3.8 python3.8-dev
        ```
        
        To run the examples, create a python virtual env, and install jupyter notebook.
        
        ```bash
        # install virtualenv
        pip3 install virtualenv
        
        # create a virtualenv with python3.8 in ~/virtualenv-3.8
        virtualenv --python=$(which python3.8) ~/virtualenv-3.8
        
        # activate the virtual env
        source ~/virtualenv-3.8/bin/activate
        
        # install jupyter notebook and necessary widgets
        pip install notebook ipywidgets
        
        # run jupyter notebook
        jupyter notebook
        ```
        
        # Questions and Help
        
        If you have any querstions about how EasyTensor works or want help with serving your ML model, please contact me directly at [kamal@easytensor.com](mailto:kamal@easytensor.com). I'm here to help!
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
