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powered library. \nIt provides an async API that mimics concurrent.futures, with support for task-graph executors.\n\nThese notebooks can be viewed using [nbviewer](https://nbviewer.jupyter.org/github/agoose77/parallels/tree/main/) until the documentation generator is complete.\n\n## What?\nIn Python there are several standard APIs for interacting with `executors`. AsyncIO has the `run_in_executor` API, `concurrent.futures` has the `Executor` API, and other\nlibraries like Dask and Ray have equivalent approaches. `concurrent.futures` is often available within other libraries, but its reduced features-set prevents the underlying\nlibrary from implementing useful optimisations like Dask's deferred computation or task graph building.\n\nParallels implements a standard `Executor` interface which defines a synchronous `submit` method, and an asynchronous `retrieve` method. \nThese methods operate upon value-less `asyncio.Future` handles which yield `True` upon task success, and raise an `Exception` otherwise. \nThe Dask and Ray implementations accept these handles as arguments to future `submit()` calls, which can be used to build task graphs\nand avoid copying data to the local machine.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagoose77%2Fparallels","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fagoose77%2Fparallels","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fagoose77%2Fparallels/lists"}