Launching your 🤗 Accelerate scripts

In the previous tutorial, you were introduced to how to modify your current training script to use 🤗 Accelerate. The final version of that code is shown below:

from accelerate import Accelerator

accelerator = Accelerator()

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

But how do you run this code and have it utilize the special hardware available to it?

First you should rewrite the above code into a function, and make it callable as a script. For example:

  from accelerate import Accelerator
  
+ def main():
      accelerator = Accelerator()

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

Next you need to launch it with accelerate launch.

Using accelerate launch

🤗 Accelerate has a special CLI command to help you launch your code in your system through accelerate launch. This command wraps around all of the different commands needed to launch your script on various platforms, without you having to remember what each of them are.

You can launch your script quickly by using:

accelerate launch {script_name.py} --arg1 --arg2 ...

Just put accelerate launch at the start of your command, and pass in additional arguments and parameters to your script afterwards like normal!

Since this runs the various torch spawn methods, all of the expected environment variables can be modified here as well. For example, here is how to use accelerate launch with a single GPU:

CUDA_VISIBLE_DEVICES="0" accelerate launch {script_name.py} --arg1 --arg2 ...

You can also use accelerate launch without performing accelerate config first, but you may need to manually pass in the right configuration parameters. In this case, 🤗 Accelerate will make some hyperparameter decisions for you, e.g., if GPUs are available, it will use all of them by default without the mixed precision. Here is how you would use all GPUs and train with mixed precision disabled:

accelerate launch --multi_gpu {script_name.py} {--arg1} {--arg2} ...

To get more specific you should pass in the needed parameters yourself. For instance, here is how you would also launch that same script on two GPUs using mixed precision while avoiding all of the warnings:

accelerate launch --multi_gpu --mixed_precision=fp16 --num_processes=2 {script_name.py} {--arg1} {--arg2} ...

For a complete list of parameters you can pass in, run:

accelerate launch -h

For a visualization of this difference, that earlier accelerate launch on multi-gpu would look something like so with torchrun:

MIXED_PRECISION="fp16" torchrun --nproc_per_node=2 --num_machines=1 {script_name.py} {--arg1} {--arg2} ...

Why you should always use accelerate config

Why is it useful to the point you should always run accelerate config?

Remember that earlier call to accelerate launch as well as torchrun? Post configuration, to run that script with the needed parts you just need to use accelerate launch outright, without passing anything else in:

accelerate launch {script_name.py} {--arg1} {--arg2} ...

Custom Configurations

As briefly mentioned earlier, accelerate launch should be mostly used through combining set configurations made with the accelerate config command. These configs are saved to a default_config.yaml file in your cache folder for 🤗 Accelerate. This cache folder is located at (with decreasing order of priority):

• The content of your environment variable HF_HOME suffixed with accelerate.
• If it does not exist, the content of your environment variable XDG_CACHE_HOME suffixed with huggingface/accelerate.
• If this does not exist either, the folder ~/.cache/huggingface/accelerate.

To have multiple configurations, the flag --config_file can be passed to the accelerate launch command paired with the location of the custom yaml.

An example yaml may look something like the following for two GPUs on a single machine using fp16 for mixed precision:

compute_environment: LOCAL_MACHINE
deepspeed_config: {}
distributed_type: MULTI_GPU
fsdp_config: {}
machine_rank: 0
main_process_ip: null
main_process_port: null
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 2
use_cpu: false

Launching a script from the location of that custom yaml file looks like the following:

accelerate launch --config_file {path/to/config/my_config_file.yaml} {script_name.py} {--arg1} {--arg2} ...