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I'm trying to deploy a Node.js API with Elastic beanstalk. I want to set the node command to start the app. This is my nodecommand.config: option_settings: aws:elasticbeanstalk:container:nodejs: NodeCommand: "npm start" This is my file structure: Whenever I try to run eb deploy , I get this error: 2020-05-13 19:03:44 INFO Environment update is starting. 2020
I want this section removed from Windows 11 Explorer: The registry method to remove the key on [HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Desktop\NameSpace_41040327] did not work for me. This method was mentioned on some websites. I couldn't find any similar question asked about this on Superuser. There's no option to hide this se
A performance change should improve the measured workload without sacrificing correctness or wasting capacity. Which measurements and bottlenecks should be considered first?
I've looked all over AWS docs and stack overflow (even went to page 4 of google!!!) but I could not for the life of me work out how to stream a file from S3. The docs for V3 are pretty useless and all the examples I find are from V2. The send command that V3 uses only returns a promise so how do I get a stream and pipe it instead of waiting for the whole fil
I want to execute AZ cli commands from my Azure DevOps Pipeline. In my YAML file I have this: trigger: - master pool: vmImage: 'ubuntu-latest' variables: buildConfiguration: 'Release' steps: - task: UsePythonVersion@0 inputs: versionSpec: '3.x' architecture: 'x64' # Updating pip to latest - script: python -m pip install --upgrade pip displayName: 'Upgrade pi
I read on https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions : openai.api_version = "2023-05-15" and on https://learn.microsoft.com/en-us/answers/questions/1193969/how-to-integrate-tiktoken-library-with-azure-opena : openai.api_version = "2023-03-15-preview" This makes me wonder: How
A performance change should improve the measured workload without sacrificing correctness or wasting capacity. Which measurements and bottlenecks should be considered first?
I am using postgresql 14.1, and I re-created my live database using parititons for some tables. since i did that, i could create index when the server wasn't live, but when it's live i can only create the using concurrently but unfortunately when I try to create an index concurrently i get an error. running this: create index concurrently foo on foo_table(co
Compare suitability, operational responsibilities and limits before choosing this technology for a project. Which trade-offs should guide the decision?
go
azure
python
numpy
postgresql
git