# Helpful Utilities Below are a variety of utility functions that 🤗 Accelerate provides, broken down by use-case. ## Data Classes These are basic dataclasses used throughout 🤗 Accelerate and they can be passed in as parameters. [[autodoc]] utils.DistributedType [[autodoc]] utils.LoggerType [[autodoc]] utils.PrecisionType [[autodoc]] utils.ProjectConfiguration ## Data Manipulation and Operations These include data operations that mimic the same `torch` ops but can be used on distributed processes. [[autodoc]] utils.broadcast [[autodoc]] utils.concatenate [[autodoc]] utils.gather [[autodoc]] utils.pad_across_processes [[autodoc]] utils.reduce [[autodoc]] utils.send_to_device ## Environment Checks These functionalities check the state of the current working environment including information about the operating system itself, what it can support, and if particular dependencies are installed. [[autodoc]] utils.is_bf16_available [[autodoc]] utils.is_torch_version [[autodoc]] utils.is_tpu_available ## Environment Configuration [[autodoc]] utils.write_basic_config When setting up 🤗 Accelerate for the first time, rather than running `accelerate config` [~utils.write_basic_config] can be used as an alternative for quick configuration. ## Memory [[autodoc]] utils.get_max_memory [[autodoc]] utils.find_executable_batch_size ## Modeling These utilities relate to interacting with PyTorch models [[autodoc]] utils.extract_model_from_parallel [[autodoc]] utils.get_max_layer_size [[autodoc]] utils.offload_state_dict ## Parallel These include general utilities that should be used when working in parallel. [[autodoc]] utils.extract_model_from_parallel [[autodoc]] utils.save [[autodoc]] utils.wait_for_everyone ## Random These utilities relate to setting and synchronizing of all the random states. [[autodoc]] utils.set_seed [[autodoc]] utils.synchronize_rng_state [[autodoc]] utils.synchronize_rng_states ## PyTorch XLA These include utilities that are useful while using PyTorch with XLA. [[autodoc]] utils.install_xla