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Async implementation of driver/adapter #171
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elijahbenizzy
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skrawcz
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Basically we pass the coroutine the entire way through. When a function depends on other data, we create a coroutine that awaits everything. Finally, we do a gather at the end. This involves no modification to the functiongraph code
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If we use tasks, we can await them twice. Furthermore, they'll begin scheduling earlier, and python handles keeping track of them. This is a much cleaner way to handle it.
skrawcz
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Aug 8, 2022
examples/async/fastapi_example.py
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| ad_hoc_utils.create_temporary_module( | ||
| pipeline, | ||
| computation1, | ||
| foo, | ||
| bar, | ||
| some_data, | ||
| computation2)) |
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We should probably not do this in an example. This doesn't feel very "ad hoc".
Otherwise 💡, fastapi could be a possible code compilation target too.
hamilton/experimental/h_async.py
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| if display_graph: | ||
| raise ValueError(f'display_graph=True is not supported for the async graph adapter. ' | ||
| f'Instead you should be using visualize_execution.') | ||
| return await await_dict_of_tasks({key: asyncio.create_task(process_value(memoized_computation[key])) for key in final_vars}) |
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| return await await_dict_of_tasks({key: asyncio.create_task(process_value(memoized_computation[key])) for key in final_vars}) | |
| task_dict = {key: asyncio.create_task(process_value(memoized_computation[key])) for key in final_vars} | |
| return await await_dict_of_tasks(task_dict) |
Also is process_value needed here? Seems a bit redundant with the result being wrapped in a task and that awaited?
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process_value is needed but create_task is not I think...
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okay. But this seems like an important line that should be at least two... break out the dict comprehension at least.
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hamilton/experimental/h_async.py
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| callabl = node.callable | ||
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| async def new_fn(fn=callabl, **fn_kwargs): | ||
| fn_kwargs = await await_dict_of_tasks({key: process_value(value) for key, value in fn_kwargs.items()}) |
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can move this dict comprehension to its own line.
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1. Fixes docstring formats 2. Removes redundant call to create_task 3. Breaks example into modules
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@skrawcz this is what I meant
Basically we pass the coroutine the entire way through.
When a function depends on other data, we create a coroutine that
awaits everything. Finally, we do a gather at the end.
I think this is optimal but will need to dig in a bit/look into
things.
Otherwise, nifty POC with very rough edges.
The biggest hack is that I'm using a cache to store coroutine values as python makes it very hard to access them.
I think we can do better, but the point is we don't have to mess with execution.
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Python - local testing