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[doc] put individual plugin explanation in front

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Pengtai Xu
2023-09-19 16:27:37 +08:00
parent 10513f203c
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2 changed files with 15 additions and 15 deletions
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docs/source/en/basics/booster_plugins.md
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@@ -19,15 +19,6 @@ We currently provide the following plugins:
More plugins are coming soon.
## Choosing Your Plugin
Generally only one plugin is used to train a model. Our recommended use case for each plugin is as follows.
- [Torch DDP Plugin](#torch-ddp-plugin): It is suitable for models with less than 2 billion parameters.
- [Torch FSDP Plugin](#torch-fsdp-plugin) / [Low Level Zero Plugin](#low-level-zero-plugin): It is suitable for models with less than 10 billion parameters.
- [Gemini Plugin](#gemini-plugin): it is suitable for models with more than 10 billion parameters and is ideal for scenarios with high cross-node bandwidth and medium to small-scale clusters (below a thousand cards).
- [Hybrid Pararllel Plugin](#hybrid-parallel-plugin): It is suitable for models with more than 60 billion parameters, exceptionally long sequences, very large vocabularies, and is best suited for scenarios with low cross-node bandwidth and large-scale clusters (a thousand cards or more).
## Plugins
### Torch DDP Plugin
@@ -96,4 +87,13 @@ This plugin implements the combination of various parallel training strategies a
{{ autodoc:colossalai.booster.plugin.HybridParallelPlugin }}
## Choosing Your Plugin
Generally only one plugin is used to train a model. Our recommended use case for each plugin is as follows.
- [Torch DDP Plugin](#torch-ddp-plugin): It is suitable for models with less than 2 billion parameters.
- [Torch FSDP Plugin](#torch-fsdp-plugin) / [Low Level Zero Plugin](#low-level-zero-plugin): It is suitable for models with less than 10 billion parameters.
- [Gemini Plugin](#gemini-plugin): It is suitable for models with more than 10 billion parameters and is ideal for scenarios with high cross-node bandwidth and medium to small-scale clusters (below a thousand cards).
- [Hybrid Pararllel Plugin](#hybrid-parallel-plugin): It is suitable for models with more than 60 billion parameters, or special models such as those with exceptionally long sequences, very large vocabularies, and is best suited for scenarios with low cross-node bandwidth and large-scale clusters (a thousand cards or more).
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