Decanter AI Core SDK for the easy use of Decanter Core API.
MoBagel Decanter AI Core SDK
Decanter AI is a powerful AutoML tool which enables everyone to build ML models and make predictions without data science background. With Decanter AI Core SDK, you can integrate Decanter AI into your application more easily with Python.
It supports actions such as data uploading, model training, and prediction to run in a more efficient way and access results more easily. You can also use Decanter AI Core SDK in Jupyter Notebook for better visualization.
To know more about Decanter AI and how you can be benefited with AutoML, visit MoBagel website and contact us to try it out!
Install and update using pip:
pip install decanter-ai-core-sdk
Basic Example: Upload Data
from decanter import core core.enable_default_logger() client = core.CoreClient(username=???, password=???, host=???) train_file = open(train_file_path, 'rb') train_data = client.upload(file=train_file, name="train") # in jupyter notebook just run the block # no need to call context.run() client.run() train_data.show()
$ python -m example.file 15:50:09 [ INFO] [Context] no event loop to close 15:50:09 [ INFO] [Context] connect healthy :) Progress UploadTask_train: 55%|█████████████████████████████████████████
Example Dataset Path
examples/data/- store the general dataset
examples/data/ts_data- store the time series dataset
- General Data
- Python Script: example.py
- Jupyter: jupyter_example.ipynb
- Time Series Data
- Python Script: auto_time_series_example.py
Since Jupyter already have an event loop (asyncio), SDK will just use the current event loop. See more in here.
More details about asyncio in learn asyncio
import asyncio loop = asyncio.get_running_loop() loop.is_running()
Tutorial for Jupyter Notebook
- first you need to install jupyter lab:
pip install jupyterlab
- this is required for progress bar to display correctly:
pip install ipywidgets
- (optional, conda venv for jupyter notebook)
conda install nb_conda
- this should open your browser to jupyter lab page.
- If you want to learn how to build ML models with Decanter AI, visit our jupyter_example.ipynb for step by step tutorial.
- If you need to handle running tasks well, refer to our jupyter_jobs_handle_example.ipynb.
Development Guide and Flow
- If you are curious about why Decanter AI Core SDK does certain things the way it does and not differently, visit our Development Guide
To understand how we design Decanter AI Core SDK,
doc/ contains the complete documentation, including the design system, the use of each API, and the required dependencies to install. Refer to our document page to navigate the complete information.
For guidance on setting up a development environment and how to make a contribution to Decanter AI Core SDK, see the contributing guidelines.
For more details on design, guidance on setting up a development environment, and SDK usage.
- Decanter AI Introduction: https://mobagel.com/product/
- Decanter AI SDK Introduction: https://mobagel.github.io/decanter-ai-core-sdk/
- Code: https://github.com/MoBagel/decanter-ai-core-sdk
- Installation: https://mobagel.github.io/decanter-ai-core-sdk/user/install.html
- API interface: https://mobagel.github.io/decanter-ai-core-sdk/api.html
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