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README.md

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@@ -26,7 +26,7 @@ executorlib is the lightest path to take *existing* Python functions and scale t
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library [Executor interface](https://docs.python.org/3/library/concurrent.futures.html#executor-objects) you already
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know, rather than asking you to adopt a new data, actor, or workflow model.
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| | executorlib | [Concurrent futures](https://docs.python.org/3/library/concurrent.futures.html) | [Dask](https://www.dask.org) | [Parsl](https://parsl-project.org) | [Ray](https://www.ray.io) |
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| | executorlib | [Futures](https://docs.python.org/3/library/concurrent.futures.html) | [Dask](https://www.dask.org) | [Parsl](https://parsl-project.org) | [Ray](https://www.ray.io) |
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|---|---|---|---|---|---|
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| Drop-in `Executor` API ||| ⚠️ | ⚠️ ||
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| Per-call resource assignment ||| ⚠️ |||

docs/comparison.md

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## At a glance
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| | executorlib | Concurrent futures | Dask | Parsl | Ray |
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| | executorlib | Futures | Dask | Parsl | Ray |
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| Drop-in `Executor` API ||| ⚠️ | ⚠️ ||
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| Per-call resource assignment ||| ⚠️ |||
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✅ first-class · ⚠️ possible via an add-on or extra configuration · ❌ not supported.
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## [Concurrent futures](https://docs.python.org/3/library/concurrent.futures.html)
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## [Futures](https://docs.python.org/3/library/concurrent.futures.html)
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The [`concurrent.futures`](https://docs.python.org/3/library/concurrent.futures.html) module is where most parallel
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Python starts: `ProcessPoolExecutor` and `ThreadPoolExecutor` run functions in parallel on a single machine. executorlib

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