Question: I've extensively look over the internet, hugging face's (hf's) discuss forum & repo but found no end to end example of how to properly do ddp/distributed data parallel with HF (links at the end). This is what I need to be capable of running it end to end:  do we wrap the hf model in DDP? (script needs to know how to synchronize stuff at some point somehow somewhere, otherwise just launching torch.distributed from the command line) do we change the args to trainer or trainer args in anyway? wrap the optimizer in any distributed trainer (like cherry? cherry is a pytorch lib for things like this) do we do the usual init group that is usually needed for ddp? what is the role of local rank? terminal launch script e.g. python -m torch.distributed.launch --nproc_per_node=2 distributed_maml.py how do we use the world size to shard the data at each loop e.g. see https://github.com/learnables/learn2learn/blob/master/examples/vision/distributed_maml.py  given answers to those I think I could write my own notebook and share it widely. This is my starter code that I want to complete but unsure if I am doing it right (especially since I don't know which args to trainer to change): """ - training on multiple gpus: https://huggingface.co/docs/transformers/perf_train_gpu_many#efficient-training-on-multiple-gpus - data paralelism, dp vs ddp: https://huggingface.co/docs/transformers/perf_train_gpu_many#data-parallelism - github example: https://github.com/huggingface/transformers/tree/main/examples/pytorch#distributed-training-and-mixed-precision - above came from hf discuss: https://discuss.huggingface.co/t/using-transformers-with-distributeddataparallel-any-examples/10775/7 ⇨ Single Node / Multi-GPU Model fits onto a single GPU: DDP - Distributed DP ZeRO - may or may not be faster depending on the situation and configuration used. ...https://huggingface.co/docs/transformers/perf_train_gpu_many#scalability-strategy python -m torch.distributed.launch \ --nproc_per_node number_of_gpu_you_have path_to_script.py \ --all_arguments_of_the_script python -m torch.distributed.launch --nproc_per_node 2 main_data_parallel_ddp_pg.py python -m torch.distributed.launch --nproc_per_node 2 ~/ultimate-utils/tutorials_for_myself/my_hf_hugging_face_pg/main_data_parallel_ddp_pg.py e.g. python -m torch.distributed.launch \ --nproc_per_node 8 pytorch/text-classification/run_glue.py \ --model_name_or_path bert-large-uncased-whole-word-masking \ --task_name mnli \ --do_train \ --do_eval \ --max_seq_length 128 \ --per_device_train_batch_size 8 \ --learning_rate 2e-5 \ --num_train_epochs 3.0 \ --output_dir /tmp/mnli_output/ """ # %% # - init group # - set up processes a la l2l # local_rank: int = local_rank: int = int(os.environ["LOCAL_RANK"]) # get_local_rank() # print(f'{local_rank=}') ## init_process_group_l2l(args, local_rank=local_rank, world_size=args.world_size, init_method=args.init_method) # init_process_group_l2l bellow # if is_running_parallel(rank): # print(f'----> setting up rank={rank} (with world_size={world_size})') # # MASTER_ADDR = 'localhost' # MASTER_ADDR = '127.0.0.1' # MASTER_PORT = master_port # # set up the master's ip address so this child process can coordinate # os.environ['MASTER_ADDR'] = MASTER_ADDR # print(f"---> {MASTER_ADDR=}") # os.environ['MASTER_PORT'] = MASTER_PORT # print(f"---> {MASTER_PORT=}") # # # - use NCCL if you are using gpus: https://pytorch.org/tutorials/intermediate/dist_tuto.html#communication-backends # if torch.cuda.is_available(): # backend = 'nccl' # # You need to call torch_uu.cuda.set_device(rank) before init_process_group is called. https://github.com/pytorch/pytorch/issues/54550 # torch.cuda.set_device( # args.device) # is this right if we do parallel cpu? # You need to call torch_uu.cuda.set_device(rank) before init_process_group is called. https://github.com/pytorch/pytorch/issues/54550 # print(f'---> {backend=}') # rank: int = torch.distributed.get_rank() if is_running_parallel(local_rank) else -1 # https://huggingface.co/docs/transformers/tasks/translation import datasets from datasets import load_dataset, DatasetDict books: DatasetDict = load_dataset("opus_books", "en-fr") print(f'{books=}') books: DatasetDict = books["train"].train_test_split(test_size=0.2) print(f'{books=}') print(f'{books["train"]=}') print(books["train"][0]) """ {'id': '90560', 'translation': {'en': 'But this lofty plateau measured only a few fathoms, and soon we reentered Our Element.', 'fr': 'Mais ce plateau élevé ne mesurait que quelques toises, et bientôt nous fûmes rentrés dans notre élément.'}} """ # - t5 tokenizer from transformers import AutoTokenizer, PreTrainedTokenizerFast, PreTrainedTokenizer tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained("t5-small") print(f'{isinstance(tokenizer, PreTrainedTokenizer)=}') print(f'{isinstance(tokenizer, PreTrainedTokenizerFast)=}') source_lang = "en" target_lang = "fr" prefix = "translate English to French: " def preprocess_function(examples): inputs = [prefix + example[source_lang] for example in examples["translation"]] targets = [example[target_lang] for example in examples["translation"]] model_inputs = tokenizer(inputs, max_length=128, truncation=True) with tokenizer.as_target_tokenizer(): labels = tokenizer(targets, max_length=128, truncation=True) model_inputs["labels"] = labels["input_ids"] return model_inputs # Then create a smaller subset of the dataset as previously shown to speed up the fine-tuning: (hack to seep up tutorial) books['train'] = books["train"].shuffle(seed=42).select(range(100)) books['test'] = books["test"].shuffle(seed=42).select(range(100)) # # use 🤗 Datasets map method to apply a preprocessing function over the entire dataset: # tokenized_datasets = dataset.map(tokenize_function, batched=True, batch_size=2) # todo - would be nice to remove this since gpt-2/3 size you can't preprocess the entire data set...or can you? # tokenized_books = books.map(preprocess_function, batched=True, batch_size=2) from uutils.torch_uu.data_uu.hf_uu_data_preprocessing import preprocess_function_translation_tutorial preprocessor = lambda examples: preprocess_function_translation_tutorial(examples, tokenizer) tokenized_books = books.map(preprocessor, batched=True, batch_size=2) print(f'{tokenized_books=}') # - load model from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("t5-small") # - to DDP # model = model().to(rank) # from torch.nn.parallel import DistributedDataParallel as DDP # ddp_model = DDP(model, device_ids=[rank]) # Use DataCollatorForSeq2Seq to create a batch of examples. It will also dynamically pad your text and labels to the # length of the longest element in its batch, so they are a uniform length. # While it is possible to pad your text in the tokenizer function by setting padding=True, dynamic padding is more efficient. from transformers import DataCollatorForSeq2Seq # Data collator that will dynamically pad the inputs received, as well as the labels. data_collator: DataCollatorForSeq2Seq = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model) """ At this point, only three steps remain: - Define your training hyperparameters in Seq2SeqTrainingArguments. - Pass the training arguments to Seq2SeqTrainer along with the model, dataset, tokenizer, and data collator. - Call train() to fine-tune your model. """ report_to = "none" if report_to != 'none': import wandb wandb.init(project="playground", entity="brando", name='run_name', group='expt_name') from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer # fp16 = True # cuda # fp16 = False # cpu import torch fp16 = torch.cuda.is_available() # True for cuda, false for cpu training_args = Seq2SeqTrainingArguments( output_dir="./results", evaluation_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, weight_decay=0.01, save_total_limit=3, num_train_epochs=1, fp16=fp16, report_to=report_to, ) trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=tokenized_books["train"], eval_dataset=tokenized_books["test"], tokenizer=tokenizer, data_collator=data_collator, ) trainer.train() print('\n ----- Success\a')   All references I consulted when writing this question:  https://discuss.huggingface.co/t/using-transformers-with-distributeddataparallel-any-examples/10775/3 https://huggingface.co/docs/transformers/perf_train_gpu_many#scalability-strategy https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Trainer Setting Hugging Face dataloader_num_workers for multi-GPU training using huggingface Trainer with distributed data parallel Why, using Huggingface Trainer, single GPU training is faster than 2 GPUs? https://discuss.huggingface.co/t/lm-example-run-clm-py-isnt-distributing-data-across-multiple-gpus-as-expected/3239/6 https://discuss.huggingface.co/t/which-data-parallel-does-trainer-use-dp-or-ddp/16021/3 https://github.com/huggingface/transformers/tree/main/examples/pytorch#distributed-training-and-mixed-precision https://pytorch.org/tutorials/intermediate/ddp_tutorial.html dist maml: https://github.com/learnables/learn2learn/blob/master/examples/vision/distributed_maml.py cross: https://discuss.huggingface.co/t/how-to-run-an-end-to-end-example-of-distributed-data-parallel-with-hugging-faces-trainer-api-ideally-on-a-single-node-multiple-gpus/21750 

Answer: You don't need to setup anything, just do: python -m torch.distributed.launch --nproc_per_node 2 ~/src/main_debug.py  or torchrun --nproc_per_node=2 --nnodes=2 --use_env ~/src/main_debug.py  then monitor the gpus with nvidia-smi see:  Example from alpaca: torchrun --nproc_per_node=4 --master_port=<your_random_port> train.py \ --model_name_or_path <your_path_to_hf_converted_llama_ckpt_and_tokenizer> \ --data_path ./alpaca_data.json \ --bf16 True \ --output_dir <your_output_dir> \ --num_train_epochs 3 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --gradient_accumulation_steps 8 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 2000 \ --save_total_limit 1 \ --learning_rate 2e-5 \ --weight_decay 0. \ --warmup_ratio 0.03 \ --lr_scheduler_type "cosine" \ --logging_steps 1 \ --fsdp "full_shard auto_wrap" \ --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \ --tf32 True  ref: https://github.com/tatsu-lab/stanford_alpaca#fine-tuning