The one I was missing was Cloud Build Editor (and maybe Viewer). Using that information I could make searches to figure out which permissions were required to execute those commands. I identified that it calls the builds and deploy subcommands. The ah ha! moment came when I realized/remembered that actually talks to GCP using the gcloud command line tool. I tried many things related to IAM, role, service accounts and the likes, but without success. The next hurdle was that the datasette publish cloudrun command would fail with the error “You do not appear to have access to project “. Another thing I learned is that when the GCP docs ask you to put the service account key in a GitHub secrets, you can just paste the whole JSON as-is. *slaps forehead* I’m sure actions can see secrets in the Environment section somehow, but I don’t know how. That is, until I realized 2 months later (of on-and-off attempts) that I was putting GitHub secrets in Settings > Environment > Secrets, and not in Settings > Secrets. It complained that “No credentials provided, skipping authentication”. But I kept running into GCP authentication issues. I started working off the demo deploy action which took me most of the way there. But publishing it to Google Cloud Compute (GCP) using GitHub Actions so I could automate the daily the content of the indexes repositories turned out to be a multi-month effort. Setting datasette-ripgrep up locally turned out to be pretty easy. Using GitHub to search across this cohesive set of tools, and only this set of tools, doesn’t really work. I thought of creating a datasette-ripgrep instance to search all the packages from the Enthought Tool Suite. A few months ago, he put together a plugin named datasette-ripgrep that uses ripgrep (you use ripgrep, right?) to search folders of files and display the results using datasette’s machinery. Simon Willison has a fascinating data-publishing and data-management project named datasette. I think it’s because it ran out of energy, but maybe I could have made deeper cuts.ĪpPublishing datasette to Google Cloud Compute with GitHub Actions After a 15h bulk rise at 23✬, the dough had almost quadrupled in volume. Two loaves of Ken Forkish’s Overnight Country Blonde. The results are fonts you can download and “use.” In the process, he builds a lot of really neat custom UIs to visualize what the models are doing. Then he pushes things (beyond) their logical conclusions, such as creating lowercase versions of lowercase letters, and uppercase versions of uppercase letters. Tom7 creates the uppestcase and lowestcase letters by training two deep learning models: one to create uppercase letters from lowercase ones, and the other to create lowercase letters from uppercase ones. It reminds me of when your Badland’s little flappy furry balls becomes tiny. VS Code’s dock icon is minuscule and impossible to click on.
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