Huggingface trainer multi gpu. You signed out in another tab or window.

Huggingface trainer multi gpu Note: Although these examples use the DPOTrainer, the customization applies to most (if not all) trainers. You signed out in another tab or window. How can I use the Trainer of HuggingFace to fine-tune a model of about 1. I have several V100 GPUs. Multi-GPU inference. Even when I set use_kd_loss to False (the loss is computed by the super call only), it still does not Hello. While it is advised to max out GPU usage as much as possible, a high number of gradient accumulation steps can result in a more pronounced training slowdown. From what I've read SFTTrainer should support multiple GPUs just fine, but when I run this I see one GPU with high utilization and one with almost none: Expected behaviour would b I am using the code provided in this blog. cuda() but still it is using only one Trainer. 75 GiB total capacity; 9. I am trying to finetune huggingface model with multiple gpus using deepspeed. 🤗 Accelerate abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged. would you please help me to understand how I can change the code or add any extra lines to run it in multiple gpus? for me trainer in Hugging face always needs GPU :0 be free , even if I use GPU 1,2,. Hugging Face Forums Training using multiple GPUs. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional methods outlined in the multi-GPU section. I feel like this is an unexpected act, expecting all GPUs would be busy during training. This guide focuses on practical techniques. Together, these two Should the HuggingFace transformers TrainingArguments dataloader_num_workers argument be set per GPU? Or total across GPUs? And does this answer change depending whether the training is running in DataParallel or DistributedDataParallel mode?. I just want to do the most naive data parallelism with Multi-GPU LLM inference (llama). Beta Was this translation helpful? Give feedback. Cuda is installed and my environment can see the With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. I experimented 3 cases, which are training same model I assume accelerate was added later and has more features like: The Trainer now uses accelerate as the backbone for it (our work the last few months) so it’s "do you want raw accelerate? Or the Trainer API). using 32 samples and a per_device_batch_size of 32. The Trainer automatically manages multiple machines, and this can speed up training tremendously. I am running the model With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. Alternatively, use 🤗 Accelerate to gain full control over the training loop. According to deepspeed integration documentation , calling the script using the deepspeed launcher and adding the --deepspeed ds_config. ORT uses optimization techniques like fusing common operations into a single node and constant folding to reduce the number of computations performed and speedup inference. I have been able to train GPT2 and smaller LLMs no problem. Hi! I am using accelerate for multi-gpu training. It seems that the hugging face implementation still uses nn. While training using model-parallel, I noticed that gpu:0 is actively computing, while other GPUs set idle despite their VRAM are consumed. Gain +20% throughput and reduce memory usage by 60% on LLaMA 3-8B model training. 47B parameters, using two servers (nodes) each with 2 GPUs of RTX 8000 48GB? Thank you In the above example, your effective batch size becomes 4. I’m overriding the evaluation_loop method for the Trainer class, and trying to run model. But now I am trying to train EleutherAI/gpt-neo-2. e. I already know that huggingface’s transformers automatically detect multi-gpu. ; Without gradients for aux: The real model runs without issues in multi-GPU mode. My training script sees all the available GPUs through torch. For evaluation, I just want to accelerate with multi-GPU inference like in normal DDP, while deepspeed raises ValueError: "ZeRO inference only In the above example, your effective batch size becomes 4. thanks! who is doing the cross-instance allreduce? PyTorch DDP? Or Horovod? or some custom HF allreduce? any sample 🙂 ? I’ve tested multiple scripts and it seems that HuggingFace’s Trainer class simply doesn’t work for single-node multi-gpu setups. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for Right now it appears as though it is using (# of layers)/(# of GPUs) to try and split the model evenly between the GPUs, without accounting for the overhead of the various code libraries that also have to be loaded to GPU. Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires massive changes to the model; when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP 0 I was trying to fine-tune Llama 70b on 4 GPUs using unsloth. When using it on your own model, make sure: your model always return tuples or subclasses of ModelOutput. 1 and DeepSpeed 0. 🤗 At Hugging Face, we created the 🤗 Accelerate library to help users easily train a 🤗 Transformers model on any type of distributed setup, whether it is multiple GPU’s on one machine or multiple GPU’s across several machines. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires If training a model on a single GPU is too slow or if the model’s weights do not fit in a single GPU’s memory, transitioning to a multi-GPU setup may be a viable option. ; your model can compute the loss if a labels argument is provided and that loss is returned as the first element of the tuple (if your model Hello, I am training LoRA adaptation of a T5 model in a one-machine multiple GPU setup. The code is using only one gpu. py script and using my own version of early stopping. import torch from GPUtil import showUtilization as gpu_usage Hi, I am using huggingface run_clm. Initially, I successfully trained the model on a single GPU, and now I am attempting to leverage the power of four RTX A5000 GPUs (each with 24GB of RAM) on a single machine. I went through the HuggingFace Docs, but still don't know how to specify which GPU to run on when using HF trainer. 0: 148: December 27, 2023 Cuda out of memory during evaluation but training is fine. state. launch script. Let’s have a look at some practical advice for GPU setups. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. 20. I was able to bypass the multiple GPUs detection by coda by running this command. I am training a model on a single batch of data, i. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for Hi, I am new to the Huggingface community and currently facing difficulty in running an example evaluation script on multi-gpu. Here is the link to google colab notebook here The notebook runs perfectly fine in a machine with single GPU. I use the trainer in hugging face which I understand it will use multiple GPu . First, GPT-2 Large(762M) model is used wherein DDP works with certain batch sizes without throwing Out Of Memory (OOM) errors. ZeRO works in several stages: ZeRO-1, optimizer state partitioning across GPUs; ZeRO-2, gradient partitioning across GPUs Trainer. DeepSpeed is a PyTorch optimization library that makes distributed training memory-efficient and fast. train(), m Hi @muellerzr , I’m curios about how Trainer works. The trainers in TRL 🤗 Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16. I try to train RoBERTa from scratch. How can i use SFTTrainer to leverage all GPUs automatically? If I add device_map=“auto” I get a Cuda out of memory exception. python -m torch. Today I was using the DPOTrainer from the trl library for DPO, and since I wanted to utilize multi-GPU training, I configured it with Accelerate. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex Multi-GPU inference. But, the example from Pytorch here showing that saving model at checkpoint using parameter local_rank. At its core is the Zero Redundancy Optimizer (ZeRO) which enables training large models at scale. Obviously a single H100 or A800 with 80GB VRAM is not sufficient. My guess is that it provides data parallelism (i. Please have a look at Not able to scale Trainer code to single node multi GPU - 🤗Transformers - Hugging Face Forums. launch / accelerate (Just by running the training script like a regular python script: python my_sc It seems that the hugging face implementation still uses nn. Intermediate. It’ll probably run in DataParallel, but most times you want the performance gains of DistributedDataParallel. My problem is: I have 8 gpu machine (each has 40GB gpu memory), but the below code does use only one of them to process batches. amp for PyTorch. cuda commands; however, I observe no speedup when launching the script as the ordinary python command. I am also using the Trainer class to handle the training. To this end, I’ve implemented a HuggingFace model and a Trainer as the following: The custom trainer: class Data2VecTrainer(Trainer): def __init__(self, *args By Strategy, I mean DDP, Tensor Parallel, Model Parallel, Pipeline Parallel etc etc and more importantly, how to use that strategy in HF Trainer to increase max_len I’m trying to train Phi-2 whose Memory footbrint is 1. py it will default to using DP as the strategy, which may be slower than expected . I have 8*A10 GPUs with 24GB each but when I try Trainer. train() However, I am struggling to get this running with 2 GPUs. DataParallel(model). 더 좋은 방법을 찾으시면 알려주세요 ^^; KOAT 재밌게 잘 봤습니다. GPU. , replicates your model Hi, there. but it didn’t worked for me. The training on a single machine works fine, but takes too long so i want to utilize multiple machines / nodes. As I understand from the documentation and forum, if I wanted to utilze these multiple gpu for training in Trainer, I would set the no_cuda parameter to False (which it is by default). 7B and I seem to need a bit more VRAM. But I find the GPU-Util is low, but the cpu is full. ashish-ram September 14, 2023, 3:08pm 17. This has raised some questions for me, and I hope someone can help clarify: How should I combine DPOTrainer Observations: Single-GPU mode: OOM occurs with my real model, as expected for large parameter counts. huggingface transformers: truncation strategy in encode_plus. Therefore, the number of steps should be around 161k / (8 * 4 * 1) = Feature request. I am getting two warnings. 0: 1177: October 29, 2021 Decreasing performance when using Accelerate. My code is based on some very basic llama generation code: model = Preparing the Dataset and Model. generate() in a distributed setting (sharded model with torchrun --nproc_per_node=4), but get RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:2 and cpu! (when checking argument for argument index in method If training a model on a single GPU is too slow or if the model’s weights do not fit in a single GPU’s memory, transitioning to a multi-GPU setup may be a viable option. The system sees two GPUs with 11GB using huggingface Trainer with distributed data parallel. To convert our above code to work within a distributed setup, a few setup configurations must first be defined, detailed in the Getting Started with DDP Tutorial Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. It would be helpful to extend the train method of the Trainer class with additional parameters to specify the GPUs devices we want to use during training. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF However, usi HuggingFace offers training_args like below. This can include multi-node, where you have a number of machines each with a single GPU, or multi-gpu where a single system has multiple GPUs, or some combination of both. ” I’m working on a machine with 8 With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. Find the 🤗 Accelerate example further down in this guide. To enable tensor parallel, pass the argument tp_plan="auto" to from_pretrained(): This doc shows how I can perform training on a single multi-gpu machine (one machine) using the “accelerate config”. @muellerzr Linux (Ubuntu 22. I have tried different learning rates and I see differences, but not good enough. The Trainer class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for NVIDIA GPUs, AMD GPUs, and torch. parallel, distributed & accumulation) = 8 Gradient Accumulation steps = 1 Total Hello, I am running an example summarization training task taken from here (official HuggingFace example) on a multi-GPU machine, using the following versions: torch==1. I am using a customized callback in the Trainer to save only the LoRA weights at each epoch. How can I do this with minimal changes to Trainer (while preserving all the nice features of Trainer like multi-gpu training)? Thanks! In this discussion I have learnt that the Trainer class automatically handles multi-GPU training, we don’t have to do anything special if using the top-rated solution. However, I am not able to find which distribution strategy this I am trying to fine-tune llama on multiple GPU using trl library, and trying to achieve data-parallel and model-parallel both. However, I am not able to run this on multi-gpu. How can I load one batch to multiple gpus? It seems like that I ‘must’ load more than one batch on one gpu. Prior to making this transition, thoroughly explore all the strategies covered in the Methods and tools for efficient training on a single GPU as they are universally applicable to model training on any number of Trainer. I share the code I’m using for this below. If you have access to a machine with multiple GPUs, these approaches are still valid, plus you can leverage additional methods outlined in the multi-GPU section. Hi, I’ve been looking this problem up all day, however, I cannot find a good practice for running multi-GPU LLM inference, information about DP/deepspeed documentation is so outdated. when I use input sequence length = 2048 tokens, and the per_device_train_batch_size=1, it seems it doesn’t fit on A100 (40GB) GPU. It only runs on 1 GPU. ” It seems like a user does not have to configure anything when using the Trainer class for doing distributed training. Specifically, a list of losses([loss1, loss2, ]) is returned in a single model forward, and optimized with a custom optimizer like PCGrad. It’s used in most of the example scripts. Before instantiating your Trainer, create a TrainingArguments to access all the Unable to resume Multi GPU training from checkpoint SFT Trainer. 04. KeyError: 'url' when push huggingface tokenizer to hub in accelerator Loading Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. I’ve read the Trainer and TrainingArguments documents, and I’ve tried the CUDA_VISIBLE_DEVICES thing already. Because I have 8 I find that trainer. 4: 365: April 29, 2024 Dataloader fetches slowly using accelerator for distributed training. How can I get log_history in Multi-GPU training? With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. 11. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires massive changes to the model; when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP Hello, I am again adapting the run_glue_no_trainer. 1: 169: June 17, 2024 Can't use multi GPU in evaluation from Trainer. Hello, can you confirm that your technique actually distributes the model across multiple GPUs (i. 7GBs. When you train bigger models you have essentially three options: bigger GPUs; more GPUs; more CPU and NVMe (offloaded to by DeepSpeed-Infinity) Let’s start at the case where you have a single GPU. Using trainer to train a bart model on 4 gpus failed. But in my case, it is not true I run the pytorch version example run_mlm. The main process computes a boolean condition on whether we need to stop the training (to be precise, whether a certain time limit is exceeded). K80 setup and running. launch --nproc-per-node=4 I’m trying to train a longformer as a classifier, and I’m currently using a test dataset to try to get this working. Hey all, I am using a local HPC to try and train LLMs, all as a test. We have some way to fix. I am trying to train a wav2vec2 model on my own dataset by following this template. Trainer (and thus SFTTrainer) supports multi-GPU training. Greater flexibility in specifying With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. json should implement the training on multi-gpu automatically. I am running the script attached below. In the following sections we go through the steps to run inference on CPU and ONNX Runtime (ORT) is a model accelerator that supports accelerated inference on Nvidia GPUs, and AMD GPUs that use ROCm stack. 2. 69 MiB free; 9. And causing the evaluation to be slow. Tried to allocate 20. I have overridden the evaluate() method and created the evaluation dataset in it. Can you use jupyter notebok to do (I’d been working on there) like this!pip install GPUtil. py to train gptj-6b model with 8 gpu’s. To use DDP (which is generally recommended, see here for more info) you must launch the script with python -m torch. Hello. This still requires the model to fit on each GPU. We covered the fundamentals of FSDP, setting up a multi-GPU environment, and detailed code implementations for loading pretrained models, preparing datasets, and finetuning using FSDP. !pip install --upgrade pip !pip install transformers !pip install datasets !pip install pandas !pip install openpyxl !pip install accelerate from transformers import Trainer, TrainingArguments from transformers import A For a deep dive into GPUs make sure to check out Tim Dettmer’s excellent blog post. However, when I run with multi-gpu, the training deadlocks and makes zero progress (even if given enough time). I have tried changing Model training in Multi GPU. During training, Zero 2 is adopted. This causes per_device_eval_batch_size to be only 1 or it goes OOM. 1. After loading Hi I’m trying to fine-tune model with Trainer in transformers, Well, I want to use a specific number of GPU in my server. 1: 1795: March 17, 2021 Minimal changes for using DataParallel? Beginners. While executing trainer. 1 8b in full precision on 4 gpus of 16 GB VRAM each. 12 GiB already allocated; 10. Hello, I am trying to incorporate knowledge distillation loss into the Seq2SeqTrainer. DataParallel for one node multi-gpu training. 2 LTS), multi-node with 4 nodes and 8 GPUs per node for a total of 32 GPUs (shared file-system and network). launch / accelerate (Just by running the training script like a regular python script: python my_script. It only detects 1 GPUs. environ[“CUDA_VISIBLE_DEVICES”]=“0” However, Information I’m working on wav2vec2. Switching from a single GPU to multiple requires some form of I want to train Trainer scripts on single-node, multi-GPU setting. There is no improvement performance between using single and multi GPUs. I am trying to finetune a model that is loaded on 8bit using Peft/Lora library in huggingface. System Info I'm using transformers. All reactions. I know I’ll eventually want to learn about DeepSpeed as well but for now I am focusing on the base features of Accelerate. I know that when using accelerate (Comparing performance between different device setups), in order to train with the desired learning rate we have to explicitel Hi all, I’m trying to train a language model using HF Trainer on four GPUs (multi-GPU newbie here). As @shreyansh26 said Multi-GPU FSDP Here, we experiment on the Single-Node Multi-GPU setting. I am using Transformers 4. You switched accounts on another tab or window. Multi-GPU mode: The toy example works, but my real model still OOMs. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires massive changes to the model; when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP To speed up performace I looked into pytorches DistributedDataParallel and tried to apply it to transformer Trainer. nn. I have two issues: The model does not seem to be learning much. when I use Accelerate library, the GPU I am trying to implement model parallelism as bf16/fp16 model wont fit on one GPU. The documentation says deepseed should detect them automatically but it does not on my system. When I test with single gpu, the training runs without a problem. Could you please clarify if my understanding is correct? and hi @AndreaSottana, sorry I am trying to fine tune got-neo because of the Cuda memory issue I need to use multiple GPU. . I am trying to finetune the model on the HH-RLHF dataset with 161k rows of training data. Depending on the Rank setting it runs either on GPU 0 or 1 but never on both. 0+cu113 and transformers==4. If you run your script with python script. If I have a 70B LLM and load it with 16bits, it basically requires 140GB-ish VRAM. What are the packages I needs to install ? For example: machine 1, I install accelerate Imalance memory usage on multi gpus while using Trainer and how to solve it. To use it, you don't need to change anything in your training code; you can set everything using just accelerate config. 00 MiB (GPU 0; 10. There seems to be no way to manually tell deepspeed to use 2 GPUs. I am looking for example, how to perform training on 2 multi-gpu machines. It works for cpu and 1 gpu but freezes when I try run on multiple GPUs (stuck at the first batch). This extension can be implemented by setting the environment variable CUDA_VISIBLE_DEVICES appropriately before the training process begins. Before instantiating your Trainer / TFTrainer, create a TrainingArguments / TFTrainingArguments to access all the points of customization during training. Do I need to launch HF with a torch launcher (torch. However, when I run it on machine with Mutiple GPUs (n=4, Nvidia T I’m going through the huggingface tutorials and going through the “Training a causal language model from scratch” sections. distributed, torchX, torchrun, Ray Train, PTL etc) or can According to the following question, the trainer will handle multiple GPU work. Comment options {{title}} Multi-GPU inference. Transitioning from a single GPU to multiple GPUs requires the introduction of some form of parallelism, as the workload must be distributed across the resources. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires massive changes to the model; when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP Hi, I am trying to finetune Whisper according to the blog post here. The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. 1 With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. After I look at the script, I found that when saving model at checkpoint, the script didn’t use local_rank argument to make the script only saving model on first worker. log_history, there was nothing. To enable tensor parallel, pass the argument tp_plan="auto" to from_pretrained(): I use “accelerate launch” to launch the distributed training across multiple GPUs. Trainer with deepspeed. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires massive changes to the model; when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP According to the main page of the Trainer API, “The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. py . py, which from what I understand, uses all 8 GPUs. 9. When I use Single-GPU, log_history was exist. But it is not using all gpus and throwing cuda out of memory error. You just have to use the pytorch launcher to use DistributedDataParallel, see an example here. If you do, it is recommended to put that specific code into a function and call that from within the notebook launcher interface, which will be shown later. I’d like to be able to offload more layers to GPU 0 in order to take advantage of my unused VRAM. My understanding is accelerate distributes tra I figured to use multi-GPU by changing a few settings like device_map and also used notebook_launcher to use accelerate capability in Kaggle notebook. From the logs I can see that now during training, evaluation runs on all four GPUs Below are some examples on how you can apply and test different techniques. As mentioned at earlier, great care should be taken when preparing the DataLoaders and model to make sure that nothing is put on any GPU. 1: Usually model training on two GPUs is there to help you get a bigger batch size: what the Trainer and the example scripts do automatically is that each GPU will treat batch of the given --pre_device_train_batch_size which will result on a training with 2 * per_device_train_batch_size. I although I have 4x Nvidia T4 GPUs. The model takes up about 32GB when loaded, so each graphic is taken up to about 8GB (8*4). Hugging Face Forums Multi gpu training. 🤗Transformers. The size is more than 8b. wise I’m trying to implement the data2vec model with HuggingFace. Well okay, I will use a system with multiple GPUs! I have limited access to a system with a few NVIDIA A100-SXM4-40GB. I am using this LED model here. How Can I fix the problem, and use GPU-Util is full. Prior to making this transition, thoroughly explore all the strategies covered in the Methods and tools for efficient training on a single GPU as they are universally applicable to model training on any number of I am trying to learn how to train large(r) language models and Accelerate seems to be the tool for me. does model parallel loading), instead of just loading the model on one GPU if it is available. Efficient inference with large models in a production environment can be as challenging as training them. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for ORPO Trainer giving error when fine-tuning Llama3-8b in Multi-GPU Loading Does the HF Trainer class support multi-node training? Or only single-host multi-GPU training? I am trying to fine-tune Llama 2 7B with QLoRA on 2 GPUs. Together, these two Why is it that when I use Trainer, multiple GPUs are used for training, but only one GPU is used for evaluation? When I compared the GPU usage for training and evaluation, I found that: only the memory of GPU-0 is increa Types of Multi-GPU Training for a 70B model using Huggingface Trainer In this discussion I have learnt that the Trainer class automatically handles multi-GPU training, we don’t have to do anything special if using the top-rated solution. When I use HF trainer to train my model, I found cuda:0 is used by default. Train on multiple GPUs / nodes. 3: 712: I interpret that to mean: Training a model with batch size 16 on one GPU is equivalent to running a model with Using following code for fine-tuning Llama3-8B with ORPO trainer on Kaggle Notebook with 2 T4 GPUs. Image Captioning on COCO. 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). In other words, in my setup, I have 4 x GPU per machine. The kernel works out of the box with flash attention, PyTorch FSDP, and Microsoft DeepSpeed. Motivation. This situation occurred only on Multi-GPU training. The batch size per GPU and gradient accumulation steps are set to 4 and 1. As an example, I have 3200 examples and I set per_device_train_batch_size=4. My server has two GPUs,(index 0, index 1) and I want to train my model with GPU index 1. If I set gradient_checkpointing=True the training segfaults (core dumped) when CUDA_VISIBLE_DEVICES is set to more than one Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. I use this command to run torchrun --nnodes 1 --nproc_per_node 8 sft. The script had wo write the following to your enviroment (bashrc or zshrc) Trainer freezes after all steps are complete (multi-gpu setting) 🤗Transformers. I successfully train model with Trainer. During evaluation, I want to track performance on downstream tasks, e. py with model bert-base-chinese and my own train/valid dataset. Trainer goes hand-in-hand with the TrainingArguments class, which offers a wide range of options to customize how a model is trained. My question was not about loading the model on a GPU rather than a CPU, but about loading the same model across multiple GPUs using model parallelism. Thus in my opinion, calculating the training loss only on the main process maybe slighly not correct, as the main process could receive different dataset portions. The finetuning works great in a single GPU scenario, however, fails with multi GPU instances. Gradient computation for aux seems to cause a drastic increase in memory usage. After a long time it has finished all the steps but no further output in the logs, no checkpoint saved, and script still seems to be running (with 0% GPU usage). Together, these two Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. os. Built-in Tensor Parallelism (TP) is now available with certain models using PyTorch. 11: 16443: November 30, 2024 CUDA out of memory when using Trainer with compute_metrics. I will note that training progressed long enough to successfully save 1 checkpoint to disk, but failed when trying to write a second checkpoint some training steps later. import os This article explores how to fine-tune the BERT model on multiple GPU nodes using Hugging Face’s Trainer and Accelerate libraries, Suboptimal Batch Size: In multi-GPU setups, Hi! I am working on using Trainer under a multi-task setting. Is it okay to do what the Trainer do? Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. How can I do that? Should I broadcast a boolean tensor with If you'd like to understand how GPU is utilized during training, please refer to the Model training anatomy conceptual guide first. For example if I have a machine with 4 GPUs and 48 CPUs I’m finetuning GPT2 on my corpus for text generation. So I made the following Hi, I am loading flan t5 xxl sharded version using “philschmid/flan-t5-xxl-sharded-fp16” for finetuning. The basic code structure is similar to the DPO example provided under the llama model in the trl library. I want to perform this on huggingface T5 model. Both are supported by the Hugging Face Trainer. test() can be used to do multi gpus inference, but I need to modify the code of testing part in my PL model. But, there is something I couldn’t understand. With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. 0: 334: March 16, 2022 Imbalance memory usage on multi_gpus. The API supports distributed training on multiple GPUs/TPUs, I have a VM with 2 V100s and I am training gpt2-like models (same architecture, fewer layers) using the really nice Trainer API from Huggingface. py or accelerate launch script. any help would be appreciated. Hello! As I can see, now Trainer can runs multi GPU training even without using torchrun / python -m torch. 0 using the following official script of huggingface. I only want the main process to compute that condition so that all processes are on the same page. 2: 32: no checkpoint saved, and script still seems to be running (with 0% GPU usage). I’m using huggingFace Trainer code to train gpt-based large language model. Loading. To enable tensor parallel, pass the argument tp_plan="auto" to from_pretrained():. You just need to copy your code to Kaggle, and enable the accelerator(multiple GPUs or single GPU) from the The Trainer class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for NVIDIA GPUs, AMD GPUs, and torch. I’m using dual 3060s, so I need to use deepspeed to shard the model. py) Can you tell me what algorithm it uses? DP or DDP? And will the fsdp argument (from TrainingArguments) work correctly in this case? Why is it that when I use Trainer, multiple GPUs are used for training, but only one GPU is used for evaluation? When I compared the GPU usage for training and evaluation, I found that: only the memory of GPU-0 is increased, and only its GPU-util is not 0. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for DeepSpeed. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires massive changes to the model; when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP and I use the demo code:Translation - Hugging Face NLP Course I’m trying to launch a custom model training through the Trainer API in the single-node-multi-GPU setup. Next you should prepare your dataset. The only difference is that instead of using google/mt5-small as model I am using facebook/bart-base. Hi @sgugger, Is there Since the dataset is large, I want to utilize a multi-GPU setup but I see that because of this line it’s not currently possible to train in a multi-gpu setting. g. I want to do multi-gpu training using this. distributed. Commented Mar 29, 2020 at 18:02. The With ZeRO see the same entry for “Single GPU” above; ⇨ Multi-Node / Multi-GPU. DataParallel is single-process, multi-thread, and only works on a single machine, while DistributedDataParallel is multi-process and works for both single- and multi- machine training. That page says “If you have access to a machine with multiple GPUs, try to run the code there. deepspeed --num_gpus=1 run_common trainer. I am observing tha Can I please ask if it’s possible to do multi gpu training if the whole model itself doesn’t fit on one gpu when loaded? For example, I’m training using the Trainer from huggingface Llama3. But, When I check the trainer. I use the subclasssed Trainer, which modifies the evaluation_loop() function. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for You signed in with another tab or window. As I see Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. In this 🤗 Accelerate supports training on single/multiple GPUs using DeepSpeed. Would you please help me how you use multiple GPU for fine tunning the Trainer¶. I am trying to train RoBERTa model from scratch. Multi-GPU support lost when overwriting functions for Custom Trainer. 26. Do so, step away from the notebook and use the launch utility. The Trainer is a It can effectively increase multi-GPU training throughput by 20% and reduces memory usage by 60%. The training script that I use is similar to the run_summarization script. @philschmid @nielsr your help would be appreciated import os import torch import pandas as pd from datasets import load_dataset Im training using the trainer class on a multi gpu setup. The script had worked fine on the tiny version of dataset that i used to verify if everything was working. 1. Also, I am training on 2 What caught my eye is that when printing validation loss, I gets printed twice: *****Running training ***** Num examples = 500 Num Epochs = 2 Instantaneous batch size per device = 4 Total train batch size (w. Hyperparameter Search using Trainer API; Inference. I’ll update this on any gotchas when it arrives and I can try some local heterogenous multi-GPU training! – xeb. Unfortunately, as I am Hi All, @phucdoitoan , I am using this code but my issue is that I need multiple gpus, for example using GPU 1,2,3 (not gpu 0) . Reload to refresh your session. In the pytorch documentation page, it clearly states that " It is recommended to use DistributedDataParallel instead of DataParallel to do multi-GPU training, even if there is only a single node. Models. 3: Considering you’re using a multi-GPU set up, I do not think the trainer will automatically run in distributed mode. 좋은 방법을 찾아서 공유드립니다. When I run the training, the I read many discussion，they tell me if I use trainer API, I can automatically use multi-gpu. 75 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. I am using the pytorch back-end. What is the method it uses? DataParallel (DP) or TensorParallel (TP) or PipelineParallel (PP) or PyTorch’s Fully Sharded Data Parallel (FSDP) is a powerful tool designed to address these challenges by enabling efficient distributed training and finetuning across multiple GPUs. Trainer. My code is from transformers im hi All, would you please give me some idea how I can run the attached code with multiple GPUs, with define number of 1,2? As I understand the trainer in HF always goes with gpu:0, but I need to specify the number of GPUs like 1,2. but my results are very strange and very different than when I use 1 GPU. When you have fast inter-node connectivity: ZeRO - as it requires close to no modifications to the model; PP+TP+DP - less communications, but requires massive changes to the model; when you have slow inter-node connectivity and still low on GPU memory: DP+PP+TP Learning rate for the `Trainer` in a multi gpu setup. We compare the performance of Distributed Data Parallel (DDP) and FSDP in various configurations. Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. Beginners. 🤗Accelerate. So the easiest API is made hard by missing to mention this script, which I finally found in one of the forums At Hugging Face, we created the 🤗 Accelerate library to help users easily train a 🤗 Transformers model on any type of distributed setup, whether it is multiple GPU’s on one machine or multiple GPU’s across several machines. I According the results above, it seems that the loss does differ among processes. multi gpu일때, SFT모델을 refe 모델로 활용할때, load하지 않고, lora layer를 제거한채로 카피하여서 활용하는 Does anyone have an end-to-end example of how to do multi-gpu, multi-node distributed training using the trainer? I can’t seem to find one anywhere. And I checked it for myself in training log. 2 and launching my script with deepspeed (thus the parallelization setup is Distributed Data Parallel). I loaded the model with 4bit config, used paged_adam_8bit with Grad checkpointing. The pytorch examples for DDP states that this should at least be faster:. However, Please tell me if you found the approach for multi GPU inferencing. I tried various combinations like converting model to model = torch. Is there a way to do it? I have implemented a trainer method. First I wonder what does accelerate do when using the --multi_gpu flag. Multiple techniques can The Trainer class can auto detect if there are multiple GPUs. I have multiple gpu available to me. Tensor parallelism shards a model onto multiple GPUs, enabling larger model sizes, and parallelizes computations such as matrix multiplication. My impression with HF Trainer is HF has lots of video tutorials and none talks about multi GPU training using Trainer (assuming it is so simple) but the key element is lost in the docs, which is the command to run the trainer script which is really hard to find. My objective is to speed-up the training process When training on a single GPU is too slow or the model weights don’t fit in a single GPUs memory we use a mutli-GPU setup. llpn yynd arfmnm ietkm qmoyp aktezl hzdjs lqljk rzi deewksw