Enterprise Accelerator on AWS

Let's make sure you have everything you need to get up and running with Descartes Labs

By now you should have been able to create an account, verify your email, and log in to your account.

Login to your portal

Ensure that you are logged in by going to https://iam.descarteslabs.com/ and signing in using the credentials you created during the setup process.

Install the Python Client

The Enterprise Accelerator (EA) leverages Descartes Labs (DL) Python API’s for building and scaling pipelines. To get started using EA, install the Python client and run a quick test to make sure everything is working. Whether on a VM, Sagemaker instance, or your local machine, you will be able to run the DL Python client once you follow these steps:

Take a look at our best practices for managing your development environment
Install the Python client
Authenticate the Python client with the Platform and test the connection

Enterprise Accelerator Notebooks

The notebooks in this Github repo demonstrate how to use the Enterprise Accelerator (EA), building from basic concepts and API usage to creating a simple web application that utilizes EA services. To get started, clone this repository locally and run through the Jupyter notebooks in the "notebooks" folder.

Sample Notebooks include examples of working with the API, models on demand, models at scale. See the Github repo for more information.

Python APIs

The Enterprise Accelerator (EA) grants you access to the following Python APIs/services:

Scenes: The DL Scenes API allows users to seamlessly access our curated raster data products from the DL Data Catalog. With this API you can find products, filter imagery metadata, and pull stacks of imagery with a few lines of code.

For more information about the Python API’s please view the full API documentation at https://docs.descarteslabs.com/api.html.