| #!/usr/bin/env python3 | |
| # python3 update of https://gist.github.com/dergachev/7028596 | |
| # Create a basic certificate using openssl: | |
| # openssl req -new -x509 -keyout server.pem -out server.pem -days 365 -nodes | |
| # Or to set CN, SAN and/or create a cert signed by your own root CA: https://thegreycorner.com/pentesting_stuff/writeups/selfsignedcert.html | |
| import http.server | |
| import ssl | |
| httpd = http.server.HTTPServer(('127.0.0.1', 443), http.server.SimpleHTTPRequestHandler) |
I have a couple of AI/ML projects related to mapping things, often conservation related, with remote sensing data. Some details and packages will vary, but the process below describes how I generally approach these types of problems. Some of these tools I have only touched briefly, but I like them, and this is more an outline of how I would like to approach a new project than a retrospective look at my previous work.
We use AWS, so it makes sense to use datasets and services that are already hosted on AWS. The data discovery and loading part of this process would look somewhat different if we were using Azure and Planetary Computer, and very different if we were using GCP and Earth Engine.
All of my analysis will be done using python on an AWS VM in the same region as my data on S3, Probably using VSCode on Sagemaker or [JupyterLab](https://docs.aws.amazo
$ parted /dev/sdX mklabel gpt/dev/sdXY)
$ parted /dev/sdX mkpart esp fat32 1MiB 512MiB$ parted /dev/sdX set 1 esp on$ parted /dev/sdX set 1 boot on$ mkfs.fat -F 32 -n UEFI /dev/sdXY