Below is a list of all the available commands 🤗 Accelerate with their parameters
Command:
accelerate config or accelerate-config
Launches a series of prompts to create and save a default_config.yml configuration file for your training system. Should
always be ran first on your machine.
Usage:
accelerate config [arguments]
Optional Arguments:
--config_file CONFIG_FILE (str) — The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content
of the environment HF_HOME suffixed with ‘accelerate’, or if you don’t have such an environment variable, your cache directory
(~/.cache or the content of XDG_CACHE_HOME) suffixed with huggingface.-h, --help (bool) — Show a help message and exitCommand:
accelerate env or accelerate-env
Lists the contents of the passed 🤗 Accelerate configuration file. Should always be used when opening an issue on the GitHub repository.
Usage:
accelerate env [arguments]Optional Arguments:
--config_file CONFIG_FILE (str) — The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content
of the environment HF_HOME suffixed with ‘accelerate’, or if you don’t have such an environment variable, your cache directory
(~/.cache or the content of XDG_CACHE_HOME) suffixed with huggingface.-h, --help (bool) — Show a help message and exitCommand:
accelerate launch or accelerate-launch
Launches a specified script on a distributed system with the right parameters.
Usage:
accelerate launch [arguments] {training_script} --{training_script-argument-1} --{training_script-argument-2} ...Positional Arguments:
{training_script} — The full path to the script to be launched in parallel--{training_script-argument-1} — Arguments of the training scriptOptional Arguments:
-h, --help (bool) — Show a help message and exit--config_file CONFIG_FILE (str)— The config file to use for the default values in the launching script.-m, --module (bool) — Change each process to interpret the launch script as a Python module, executing with the same behavior as ‘python -m’.--no_python (bool) — Skip prepending the training script with ‘python’ - just execute it directly. Useful when the script is not a Python script.--debug (bool) — Whether to print out the torch.distributed stack trace when something fails.The rest of these arguments are configured through accelerate config and are read in from the specified --config_file (or default configuration) for their
values. They can also be passed in manually.
Hardware Selection Arguments:
--cpu (bool) — Whether or not to force the training on the CPU.--multi_gpu (bool) — Whether or not this should launch a distributed GPU training.--mps (bool) — Whether or not this should use MPS-enabled GPU device on MacOS machines.--tpu (bool) — Whether or not this should launch a TPU training.Resource Selection Arguments:
The following arguments are useful for fine-tuning how available hardware should be used
--mixed_precision {no,fp16,bf16} (str) — Whether or not to use mixed precision training. Choose between FP16 and BF16 (bfloat16) training. BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.--num_processes NUM_PROCESSES (int) — The total number of processes to be launched in parallel.--num_machines NUM_MACHINES (int) — The total number of machines used in this training.--num_cpu_threads_per_process NUM_CPU_THREADS_PER_PROCESS (int) — The number of CPU threads per process. Can be tuned for optimal performance.Training Paradigm Arguments:
The following arguments are useful for selecting which training paradigm to use.
--use_deepspeed (bool) — Whether or not to use DeepSpeed for training.--use_fsdp (bool) — Whether or not to use FullyShardedDataParallel for training.--use_megatron_lm (bool) — Whether or not to use Megatron-LM for training.Distributed GPU Arguments:
The following arguments are only useful when multi_gpu is passed or multi-gpu training is configured through accelerate config:
--gpu_ids (str) — What GPUs (by id) should be used for training on this machine as a comma-seperated list--same_network (bool) — Whether all machines used for multinode training exist on the same local network.--machine_rank MACHINE_RANK (int) — The rank of the machine on which this script is launched.--main_process_ip MAIN_PROCESS_IP (str) — The IP address of the machine of rank 0.--main_process_port MAIN_PROCESS_PORT (int) — The port to use to communicate with the machine of rank 0.--rdzv_conf (str) — Additional rendezvous configuration (<key1>=<value1>,<key2>=<value2>,…).--max_restarts (int) — Maximum number of worker group restarts before failing.--monitor_interval (float) — Interval, in seconds, to monitor the state of workers.TPU Arguments:
The following arguments are only useful when tpu is passed or TPU training is configured through accelerate config:
--main_training_function MAIN_TRAINING_FUNCTION (str) — The name of the main function to be executed in your script.--downcast_bf16 (bool) — Whether when using bf16 precision on TPUs if both float and double tensors are cast to bfloat16 or if double tensors remain as float32.DeepSpeed Arguments:
The following arguments are only useful when use_deepspeed is passed or deepspeed is configured through accelerate config:
--deepspeed_config_file (str) — DeepSpeed config file.--zero_stage (int) — DeepSpeed’s ZeRO optimization stage.--offload_optimizer_device (str) — Decides where (none|cpu|nvme) to offload optimizer states.--offload_param_device (str) — Decides where (none|cpu|nvme) to offload parameters.--gradient_accumulation_steps (int) — No of gradient_accumulation_steps used in your training script.--gradient_clipping (float) — Gradient clipping value used in your training script.--zero3_init_flag (str) — Decides Whether (true|false) to enable deepspeed.zero.Init for constructing massive models. Only applicable with DeepSpeed ZeRO Stage-3.--zero3_save_16bit_model (str) — Decides Whether (true|false) to save 16-bit model weights when using ZeRO Stage-3. Only applicable with DeepSpeed ZeRO Stage-3.--deepspeed_hostfile (str) — DeepSpeed hostfile for configuring multi-node compute resources.--deepspeed_exclusion_filter (str) — DeepSpeed exclusion filter string when using mutli-node setup.--deepspeed_inclusion_filter (str) — DeepSpeed inclusion filter string when using mutli-node setup.--deepspeed_multinode_launcher (str) — DeepSpeed multi-node launcher to use.Fully Sharded Data Parallelism Arguments:
The following arguments are only useful when use_fdsp is passed or Fully Sharded Data Parallelism is configured through accelerate config:
--fsdp_offload_params (str) — Decides Whether (true|false) to offload parameters and gradients to CPU.--fsdp_min_num_params (int) — FSDP’s minimum number of parameters for Default Auto Wrapping.--fsdp_sharding_strategy (int) — FSDP’s Sharding Strategy.--fsdp_auto_wrap_policy (str) — FSDP’s auto wrap policy.--fsdp_transformer_layer_cls_to_wrap (str) — Transformer layer class name (case-sensitive) to wrap, e.g, BertLayer, GPTJBlock, T5Block …--fsdp_backward_prefetch_policy (str) — FSDP’s backward prefetch policy.--fsdp_state_dict_type (str) — FSDP’s state dict type.Megatron-LM Arguments:
The following arguments are only useful when use_megatron_lm is passed or Megatron-LM is configured through accelerate config:
--megatron_lm_tp_degree (“) — Megatron-LM’s Tensor Parallelism (TP) degree.--megatron_lm_pp_degree (“) — Megatron-LM’s Pipeline Parallelism (PP) degree.--megatron_lm_num_micro_batches (“) — Megatron-LM’s number of micro batches when PP degree > 1.--megatron_lm_sequence_parallelism (“) — Decides Whether (true|false) to enable Sequence Parallelism when TP degree > 1.--megatron_lm_recompute_activations (“) — Decides Whether (true|false) to enable Selective Activation Recomputation.--megatron_lm_use_distributed_optimizer (“) — Decides Whether (true|false) to use distributed optimizer which shards optimizer state and gradients across Data Pralellel (DP) ranks.--megatron_lm_gradient_clipping (“) — Megatron-LM’s gradient clipping value based on global L2 Norm (0 to disable).AWS SageMaker Arguments:
The following arguments are only useful when training in SageMaker
--aws_access_key_id AWS_ACCESS_KEY_ID (str) — The AWS_ACCESS_KEY_ID used to launch the Amazon SageMaker training job--aws_secret_access_key AWS_SECRET_ACCESS_KEY (str) — The AWS_SECRET_ACCESS_KEY used to launch the Amazon SageMaker training jobaccelerate tpu-config
Usage:
accelerate tpu-config [arguments]
Optional Arguments:
-h, --help (bool) — Show a help message and exitConfig Arguments:
Arguments that can be configured through accelerate config.
--config_file (str) — Path to the config file to use for accelerate.--tpu_name (str) — The name of the TPU to use. If not specified, will use the TPU specified in the config file.--tpu_zone (str) — The zone of the TPU to use. If not specified, will use the zone specified in the config file.TPU Arguments:
Arguments for options ran inside the TPU.
--command_file (str) — The path to the file containing the commands to run on the pod on startup.--command (str) — A command to run on the pod. Can be passed multiple times.--install_accelerate (bool) — Whether to install accelerate on the pod. Defaults to False.--accelerate_version (str) — The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify ‘dev’ to install from GitHub.--debug (bool) — If set, will print the command that would be run instead of running it.accelerate test or accelerate-test
Runs accelerate/test_utils/test_script.py to verify that 🤗 Accelerate has been properly configured on your system and runs.
Usage:
accelerate test [arguments]Optional Arguments:
--config_file CONFIG_FILE (str) — The path to use to store the config file. Will default to a file named default_config.yaml in the cache location, which is the content
of the environment HF_HOME suffixed with ‘accelerate’, or if you don’t have such an environment variable, your cache directory
(~/.cache or the content of XDG_CACHE_HOME) suffixed with huggingface.-h, --help (bool) — Show a help message and exit