##
Run  `accelerate config` and answer the questionnaire accordingly. 
Below is an example yaml for BF16 mixed-precision training using PyTorch FSDP with CPU offloading on 8 GPUs.
<pre>
compute_environment: LOCAL_MACHINE                                                                                             
deepspeed_config: {}
distributed_type: FSDP
downcast_bf16: 'no'
dynamo_backend: 'NO'
fsdp_config:
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_backward_prefetch_policy: BACKWARD_PRE
  fsdp_offload_params: true
  fsdp_sharding_strategy: 1
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_transformer_layer_cls_to_wrap: T5Block
machine_rank: 0
main_training_function: main
megatron_lm_config: {}
mixed_precision: bf16
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
use_cpu: false
</pre>
##
<pre>
  from accelerate import Accelerator
  
+ def main():
    accelerator = Accelerator()

    model = accelerator.prepare(model)

    optimizer, training_dataloader, scheduler = accelerator.prepare(
        optimizer, training_dataloader, scheduler
    )

    for batch in training_dataloader:
        optimizer.zero_grad()
        inputs, targets = batch
        outputs = model(inputs)
        loss = loss_function(outputs, targets)
        accelerator.backward(loss)
        optimizer.step()
        scheduler.step()

    ...

+ if __name__ == "__main__":
+     main()
</pre>

Launching a script using default accelerate config file looks like the following:
```
accelerate launch {script_name.py} {--arg1} {--arg2} ...
```

Alternatively, you can use `accelerate launch` with right config params for multi-gpu training as shown below
```
accelerate launch \
    --use_fsdp \
    --num_processes=8 \
    --mixed_precision=bf16 \
    --fsdp_sharding_strategy=1 \
    --fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP \
    --fsdp_transformer_layer_cls_to_wrap=T5Block \
    --fsdp_offload_params=true \
    {script_name.py} {--arg1} {--arg2} ...
```

##
For PyTorch FDSP, you need to prepare the model first before preparing the optimizer since FSDP will shard parameters in-place and this will break any previously initialized optimizers. Same in outlined in the above code snippet. For transformer models, please use `TRANSFORMER_BASED_WRAP` auto wrap policy as shown in the config above. 


##
To learn more checkout the related documentation:
- <a href="https://huggingface.co/docs/accelerate/usage_guides/fsdp" target="_blank">How to use FSDP</a>
- <a href="https://huggingface.co/blog/pytorch-fsdp" target="_blank">Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel</a>