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Source channel @githubtrending · Post #15482 · Feb 10

#python#ai#claude#gemini#llama#llm#openai You can access powerful AI language models for free or with trial credits through multiple legitimate platforms. Services like OpenRouter, Google AI Studio, Groq, and Mistral offer free tiers with varying request limits, while others like Fireworks, Baseten, and Inference.net provide trial credits ranging from $1 to $30. These platforms support diverse models including Llama, Gemma, Qwen, and DeepSeek, enabling you to build and test AI applications without upfront costs. The benefit is clear: you can prototype, develop, and deploy AI-powered features while managing your budget effectively, with options to scale up as your needs grow. https://github.com/cheahjs/free-llm-api-resources

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djangoproject

@djangoproject · Post #118 · 08/08/2016, 11:44 AM

https://docs.python.org/3/library/multiprocessing.html multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows. The #multiprocessing module also introduces #APIs which do not have analogs in the #threading#module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data #parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,