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AzureML Quick Launch: Taxi Fare Prediction with RAPIDS + Dask

Logo for AzureML Quick Launch: Taxi Fare Prediction with RAPIDS + Dask
Description
Download pre-configured setup files to launch and train a Taxi Fare Prediction model with RAPIDS using Dask on AzureML using the NGC-AzureML Quick Launch Toolkit
Publisher
NVIDIA
Latest Version
v1
Modified
April 4, 2023
Compressed Size
1.53 KB

Overview

Download pre-configured setup files to quick launch a multi-node multi-GPU NVIDIA RAPIDS example using NGC-AzureML Quick Launch Toolkit. azureml-ngc-tools is a CLI toolkit that enables you to launch a fully setup Jupyter notebook environment on AzureML with a combination of NGC assets pre-installed. These ready-to-use config files work seamlessly with the NGC-AzureML Quick Launch CLI Toolkit (azureml-ngc-tools) to provide you direct access to a fully setup and optimally configured Azure Machine Learning environment with the RAPIDS container and the additional resources required for this example, pre-installed for you.

The two ready-to-use files are:

  1. azure_config.json: Defines the most optimal Azure environment setup for this use-case. Simply insert your Azure credentials in the indicated fields and use right away.
  2. ngc_config.json: Pre-installs RAPIDS, and ready-to-use code to fit a Random Forest Model for a Taxi fare prediction task on your AzureML environment. No modifications are required; use this file as is.

Usage Instructions

After inserting your Azure credentials in the azure_config.json file downloaded from here, quick launch a ready-to-use Jupyter notebook on AzureML with the following bash commands:

pip install azureml-ngc-tools
azureml-ngc-tools --login azure_config.json --app ngc_config.json
pip install azure-cli

License

The config files listed here when used with the NGC-AzureML Quick Launch CLI toolkit (azureml-ngc-tools) uses RAPIDS which is to be used in accordance with the End User License Agreement included with it. Licenses are also available along with the model application zip file. By pulling and using the RAPIDS container and downloading models, you accept the terms and conditions of these licenses.