Programming Tutorials

  You provisioned an Azure OpenAI resource. You paid for a quota of 30,000 TPM (Tokens Per Minute). You wrote a Python script to process a batch of documents. Everything looks perfect, yet five seconds into execution, your logs are flooded with errors: 429 Too Many Requests: Rate limit is exceeded. Try again in 2 seconds. This is the most common frustration for developers migrating from the public OpenAI API to Azure. You check your math, and you haven't processed anywhere near 30,000 tokens yet. The issue usually isn't your token usage—it’s your request velocity. This article dissects the hidden relationship between TPM and RPM in Azure, explains the aggressive short-window throttling mechanisms, and provides a production-grade Python implementation to handle rate limits gracefully. The Root Cause: It’s Not Just About Tokens To solve the 429 error, you must understand how Azure calculates capacity. Most developers focus entirely on  TPM (Tokens Per Minute)  because that ...

  You push your latest Docker container to a Hugging Face Space. The build logs show a successful image creation. You see "Building..." turn into "Running...". Then, ten seconds later, the status flips to "Runtime Error" or "Paused". The logs provide a cryptic hint:  Connection refused  or simply a silence where the application logs should be. For DevOps engineers and AI prototypers deploying custom stacks—particularly those involving Ollama, heavy CUDA dependencies, or custom Gradio endpoints—this is the most common friction point. The container runs perfectly on  localhost , yet fails immediately inside the Spaces infrastructure. This guide provides the root cause analysis for these networking failures and definitive, production-ready  Dockerfile  configurations to fix them. The Root Cause: Networking Interfaces and Port 7860 To fix the crash, you must understand the constraint. Hugging Face Spaces (specifically Docker Spaces) operate on a stri...