The “accelerate” command is a library that allows PyTorch code to be run across any distributed configuration. It provides a set of functionalities to accelerate the training and inference of models. This article will illustrate each of the following use cases of the “accelerate” command:

Use case 1: Print environment information

Code:

accelerate env

Motivation: Printing environment information can be helpful for troubleshooting and verifying the setup of the accelerate library. This command provides information such as the version of accelerate and PyTorch, the current CUDA version, and the devices available.

Explanation: The “env” subcommand is used to retrieve and print the environment information.

Example OUTPUT:

accelerate 0.3.0
PyTorch 1.9.0
CUDA 11.1
Available devices:
- GPU: 0
- GPU: 1

Use case 2: Interactively create a configuration file

Code:

accelerate config

Motivation: Creating a configuration file allows for easy customization of the accelerate library. This interactive command prompts the user to choose the desired options, such as the distributed backend, the number of processes, and the batch size. It then generates a YAML configuration file with the selected options.

Explanation: The “config” subcommand opens an interactive prompt where the user can select the desired options for the configuration file.

Example OUTPUT:

Select the distributed backend:
1. torch.distributed
2. deepspeed

Enter your choice: 1

Enter the local rank: 0

Enter the world size: 2

Enter the batch size: 32

Configuration file saved at path/to/config.yaml

Use case 3: Print the estimated GPU memory cost of running a huggingface model with different data types

Code:

accelerate estimate-memory name/model

Motivation: Estimating the GPU memory cost is crucial when working with large models or limited resources. This command helps in determining the approximate GPU memory required for running a specific huggingface model with different data types, such as float16 or float32.

Explanation: The “estimate-memory” subcommand is used to estimate the GPU memory cost of running a huggingface model. The argument name/model represents the name or path of the Hugging Face model.

Example OUTPUT:

Estimating GPU memory cost for Hugging Face model: name/model

Float16 memory cost: 123.45 MB
Float32 memory cost: 246.89 MB
Float64 memory cost: 493.78 MB

Use case 4: Test an Accelerate configuration file

Code:

accelerate test --config_file path/to/config.yaml

Motivation: Testing an Accelerate configuration file ensures that it is valid and all the specified settings are correct. This command runs a series of tests on the provided configuration file to verify its integrity.

Explanation: The “test” subcommand is used to test the provided Accelerate configuration file. The argument --config_file specifies the path to the YAML configuration file.

Example OUTPUT:

Testing Accelerate configuration file: path/to/config.yaml

All tests passed successfully.

Use case 5: Run a model on CPU with Accelerate

Code:

accelerate launch path/to/script.py --cpu

Motivation: Running a model on CPU can be useful when GPU resources are limited or for development purposes. The “accelerate” command allows for easy execution of the PyTorch script with the accelerate library on a CPU.

Explanation: The “launch” subcommand is used to launch the PyTorch script with the accelerate library. The argument path/to/script.py specifies the path to the PyTorch script, and the --cpu flag indicates that the script should be run on CPU.

Example OUTPUT:

Running model on CPU with Accelerate...

...
Training complete.

Use case 6: Run a model on multi-GPU with Accelerate, with 2 machines

Code:

accelerate launch path/to/script.py --multi_gpu --num_machines 2

Motivation: Running a model on multiple GPUs can significantly speed up the training process. This command launches the PyTorch script with the accelerate library on a multi-GPU setup using multiple machines.

Explanation: The “launch” subcommand is used to launch the PyTorch script with the accelerate library. The argument path/to/script.py specifies the path to the PyTorch script. The --multi_gpu flag indicates that the training should be performed on multiple GPUs, and the --num_machines option specifies the number of machines (2 in this case).

Example OUTPUT:

Running model on multi-GPU with Accelerate...

...
Training complete.

Conclusion:

The “accelerate” command provides a comprehensive set of functionalities to accelerate the training and inference of models in PyTorch. It allows for easy configuration, estimation of GPU memory cost, testing of configuration files, and running models on CPU or multi-GPU setups. By following the examples provided in this article, users can effectively utilize the “accelerate” command to improve the efficiency and performance of their PyTorch models.