• Create a regular AWS account
• Create an educational AWS account

This is the repository for OpenData tutorial content by MLSL.

The tutorial can be run with any SageMaker instance type, but we highly recommend instance type with GPU support. For example, ml.p?.?xlarge series. The EBS volume size should be more than 60GB in order to store all necessary data.

Network training/inference is a memory-intensive process. If you run into out of GPU memory or out of RAM error, consider decrease the number of batch_size in the yml config files in the configs folder.

Once the SageMaker instance is successfully launched, open a terminal and follow the commands below:

$ cd ~/SageMaker/
$ git clone https://github.com/aws-samples/aws-open-data-satellite-lidar-tutorial.git
$ cd aws-open-data-satellite-lidar-tutorial

This will download the repository and take you to the repository directory.

Next, set up a Conda environment by running setup-env.sh as shown below. You can change the environment name from tutorial_env to any other names.

$ ./setup-env.sh tutorial_env

This may take 10--15 minutes to complete.

Then check to make sure you have a new Jupyter kernel called conda_tutorial_env, or conda_[name] if you change the environment name to [name]. You may need to wait for a couple of minutes and refresh the Jupyter page.

Next, download necessary files (data browser) from S3 bucket prepared for this tutorial by running download-from-s3.sh:

$ ./download-from-s3.sh

This may take 5 minutes to complete, and requires at least 23GB of EBS disk size.

Finally, you can launch the notebooks Building-Footprint.ipynb or Road-Network.ipynb and learn to reproduce the tutorial. Note that if the notebook shows "No Kernel", or prompts to "Select Kernel", select the Jupyter kernel created in the previous step.

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file. The NOTICE includes third-party licenses used in this repository.