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[doc] deepspeed universal checkpoint (#35015)
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* universal checkpoint

* Update docs/source/en/deepspeed.md

Co-authored-by: Steven Liu <[email protected]>

* Update docs/source/en/deepspeed.md

Co-authored-by: Steven Liu <[email protected]>

* Update docs/source/en/deepspeed.md

Co-authored-by: Steven Liu <[email protected]>

---------

Co-authored-by: Steven Liu <[email protected]>
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huyiwen and stevhliu authored Jan 9, 2025
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}
```

### Universal Checkpointing

[Universal Checkpointing](https://www.deepspeed.ai/tutorials/universal-checkpointing) is an efficient and flexible feature for saving and loading model checkpoints. It enables seamless model training continuation and fine-tuning across different model architectures, parallelism techniques, and training configurations.

Resume training with a universal checkpoint by setting [load_universal](https://www.deepspeed.ai/docs/config-json/#checkpoint-options) to `true` in the config file.

```yaml
{
"checkpoint": {
"load_universal": true
}
}
```

## Deployment

DeepSpeed can be deployed by different launchers such as [torchrun](https://pytorch.org/docs/stable/elastic/run.html), the `deepspeed` launcher, or [Accelerate](https://huggingface.co/docs/accelerate/basic_tutorials/launch#using-accelerate-launch). To deploy, add `--deepspeed ds_config.json` to the [`Trainer`] command line. It’s recommended to use DeepSpeed’s [`add_config_arguments`](https://deepspeed.readthedocs.io/en/latest/initialize.html#argument-parsing) utility to add any necessary command line arguments to your code.
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