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c8f207f746
This PR makes a number of major overhauls to the Ray core docs:
Add a key-concepts section for {Tasks, Actors, Objects, Placement Groups, Env Deps}.
Re-org the user guide to align with key concepts.
Rewrite the walkthrough to link to mini-walkthroughs in the key concept sections.
Minor tweaks and additional transition material.
43 lines
1.3 KiB
ReStructuredText
43 lines
1.3 KiB
ReStructuredText
Antipattern: Too fine-grained tasks
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===================================
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**TLDR:** Avoid over-parallelizing. Parallelizing tasks has higher overhead than using normal functions.
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Parallelizing or distributing tasks usually comes with higher overhead than an ordinary function call. Therefore, if you parallelize a function that executes very quickly, the overhead could take longer than the actual function call!
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To handle this problem, we should be careful about parallelizing too much. If you have a function or task that’s too small, you can use a technique called batching to make your tasks do more meaningful work in a single task.
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Code example
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------------
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**Antipattern:**
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.. code-block:: python
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@ray.remote
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def double(number):
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return number * 2
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numbers = list(range(10000))
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doubled_numbers = []
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for i in numbers:
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doubled_numbers.append(ray.get(double.remote(i)))
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**Better approach:** Use batching.
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.. code-block:: python
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@ray.remote
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def double_list(list_of_numbers):
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return [number * 2 for number in list_of_numbers]
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numbers = list(range(10000))
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doubled_list_refs = []
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BATCH_SIZE = 100
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for i in range(0, len(numbers), BATCH_SIZE):
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batch = numbers[i : i + BATCH_SIZE]
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doubled_list_refs.append(double_list.remote(batch))
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doubled_numbers = []
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