[FEAT] Propagate configs to Ray remote functions #3292
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Run release-drafter/release-drafter@v5
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from: '## General Changes\r\n' +
'\r\n' +
'1. Adds serde and pickling `__reduce__` impl for `PyDaftConfig` so it can be sent over the wire in Ray\r\n' +
'2. Parametrizes our runners (both PyRunner and RayRunner) with a `DaftConfig` object upon initialization. We thus also have to make it illegal from the user-API to set the configs once the runners have been instantiated (i.e. when any creation/execution of dataframes has occurred)\r\n' +
'3. I also consolidated our `_RUNNER` and `_DAFT_CONTEXT` global singletons in the `daft.context` module -- the runner now resides inside the `DaftContext` object\r\n' +
'\r\n' +
'## RayRunner changes\r\n' +
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'1. For every call to `@ray.remote`, ensure that we accept a daft_config as the first arg and call `set_config` as the first action\r\n' +
'4. When the `RayRunner` is initialized, we call a `ray.put` to put the DaftConfig into the cluster store that objectref. This objectref is also passed around to various other datastructures that may potentially call `ray.remote` functio
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Run release-drafter/release-drafter@v5
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