Huggingface trainer use gpu 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. train(), the kernel crashes. We trained our model using the Hugging Face Trainer with a PyTorch backend using an AMD GPU. I want to finetune a BERT model on a dataset (just like it is demonstrated in the course), but when I run it, it gives me +20 hours of runtime. I would expect all 4 GPU usage bars in the following screenshot to be all the way up, but devices 1-3 show 0% usage: I Trainer. Together, these two I’m trying to launch a custom model training through the Trainer API in the single-node-multi-GPU setup. Then, i found that we could put devices_ids directly to nn. Note that this will not work with SageMaker Studio. distributed, torchX, torchrun, Ray Train, PTL etc) or can the HF Trainer alone use multiple GPUs without being launched by a third-party distributed launcher? 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. e. Why, using Huggingface Trainer, single GPU training is faster than 2 GPUs? Note, that you would require a GPU to run mixed-8bit models as the kernels have been compiled for GPUs only. I am observing tha Hi, I want to train Trainer scripts on single-node, multi-GPU setting. ; 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 Hugging Face Forums How to restrict Trainer to use certain GPUs? Beginners. This makes it easier to start training faster without manually writing your DeepSpeed. 0%. ; 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 This section demonstrated how you can directly use the Hugging Face Transformer Trainer APIs to fine-tune a model to a new text classification problem. It just puts everything on gpu:0, The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. You can turn off the device placement with the TrainingArguments setting no_cuda: TL;DR Training with packed instruction tuning examples (without padding) is now compatible with Flash Attention 2 in Hugging Face, thanks to a recent PR and the new DataCollatorWithFlattening. launch --nproc-per-node=4 Trainer¶. But in my case, it is not true I run the pytorch version example run_mlm. 25: 36971: July 21, 2024 How to run single-node, multi-GPU training with HF Trainer? 🤗Transformers. 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. This article describes how to fine-tune a Hugging Face model with the Hugging Face transformers library on a single GPU. launch / accelerate (Just by running the training script like a regular python script: python my_script. While training using model-parallel, I noticed that gpu:0 is actively computing, while other GPUs set idle despite their VRAM are consumed. ; The Transformers Trainer is only using 1 out of 4 possible GPUs. Now this is right time to use M1 GPU as Hello. If you use the Hugging Face Trainer, as of transformers v4. The new --sharded_ddp and --deepspeed command line Trainer arguments Hello, I am using huggingface on my google colab pro+ instance, and I keep getting errors like RuntimeError: CUDA out of memory. 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. 5. However, the Accelerator fails to work properly. 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. When trying to run the last line trainer. I’ve read the Trainer and TrainingArguments documents, and I’ve tried the CUDA_VISIBLE_DEVICES thing already. Together, these two Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. 🤗Transformers. I therefore tried to run the code with my GPU by importing torch, but the time does not go down. ), and the Trainer class takes care of the rest. In TRL we provide an easy-to-use API to create your SFT models and train them with few lines of code on your dataset. I tried the following settings: How to run an end to end example of distributed data parallel with hugging face's trainer api (ideally on a single node multiple gpus What algorithm Trainer uses for multi GPU training (without torchrun) Beginners. Where I should focus to implement multiple GPU training? I nee Hello, I’ve been trying to train a BERT-based model for SQuAD with the Trainer API, and I would like to use TPUs from COLAB to speed-up the training. diquest0508 August 4, 2023, 6:44am 1 !pip install How to get the Trainer API to use GPU? Beginners. It can provide up to 2x improvement in training throughput while maintaining convergence quality. It works for cpu and 1 gpu but freezes when I try run on multiple GPUs (stuck at the first batch). I have 8*A10 GPUs with 24GB each but when I try Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. This extension can be In this section we have a look at a few tricks to reduce the memory footprint and speed up training for large models and how they are integrated in the Trainer and 🤗 Accelerate. Execute training. Obviously a single H100 or A800 with 80GB VRAM is not sufficient. ; 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 Once you’ve done all the data preprocessing work in the last section, you have just a few steps left to define the Trainer. 75 GiB total capacity; 9. Do I need to launch HF with a torch launcher (torch. It takes ~3 sec to process 128 samples (16 per each GPU). 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. When reducing the batch size to 1, it works, but it is exceeeedingly slooow The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. 🤗 Transformers provides a Trainer class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. cuda. 00 MiB (GPU 0; 15. For more usage examples, see Inspecting Training Results. Sagemaker inference : how to load model. co/docs/transformers/main_classes/trainer#pytorch-fully-sharded-data-parallel and further on the I’m training my own prompt-tuning model using transformers package. Hello, I am training LoRA adaptation of a T5 model in a one-machine multiple GPU setup. I am using Transformers 4. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. It’s used in most of the example scripts. 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. By default it uses device:0. For training, we used a validation split of the wikiText-103-raw-v1 data set, but this can be easily replaced with a train The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when used with other models. I have followed the official notebook for training a SQuAD model, and then, I have followed the suggestions on this older topic: Tpu Trainer Which says the only thing to edit is to set a max_length parameter for When Apple has introduced ARM M1 series with unified GPU, I was very excited to use GPU for trying DL stuffs. Therefore, the number of steps should be around 161k / (8 * 4 * 1) = 5k steps. rabiulawal July 13, 2021, I am trying to set gpu device for HF trainer. I am trying to use a GPU device instead of the default CPU. The API supports distributed training on multiple GPUs/TPUs, The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex 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. I have multiple gpu available to me. 12 GiB already allocated; 10. train? Beginners. set_device(). 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. The Trainer API supports a wide range of To speed up performace I looked into pytorches DistributedDataParallel and tried to apply it to transformer Trainer. data. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional 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. The methods that you can apply to improve training efficiency on a single GPU With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of the art. Motivation. DataParallel(model, devices_ids[0,1,2]). DeepSpeed, powered by Zero Redundancy Optimizer (ZeRO), is an optimization library for training and fitting very large models onto a GPU. How can I load one batch to multiple gpus? It seems like that I ‘must’ load more than one batch on one gpu. 3: Hello, I am new to the huggingface library and I am currently going over the course. py with model bert-base-chinese and my own train/valid dataset. ; num_samples (int) — The number of samples in our dataset. 8. What is the method it uses? DataParallel (DP) or TensorParallel (TP) or PipelineParallel (PP) or DPP, what? I’m training environment is the one-machine-multiple-gpu setup. I tried using torch. In this guide, you’ll learn how to use FlashAttention-2 (a more memory-efficient attention mechanism), BetterTransformer (a PyTorch native fastpath execution), and bitsandbytes to quantize your I’m finetuning GPT2 on my corpus for text generation. train() in runpod's multi gpu? 🤗Transformers. Supervised fine-tuning (or SFT for short) is a crucial step in RLHF. I noticed that the model gets moved to the GPU, since the memory increases, but the utilization remains at 0% througout training. Trainer. How Can I fix the problem, and use GPU-Util is full. 2. I use this command to run torchrun --nnodes 1 --nproc_per_node 8 sft. When i use model. ; 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 Hi everyone, I’m pretty new to this. amp for PyTorch. ; 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 Fine-tune Hugging Face models for a single GPU. Pytorch NLP Huggingface: model not loaded on GPU. I use the subclasssed Trainer, which modifies the evaluation_loop() function. Training New AutoTokenizer Hugging Face. If it’s for an evaluation during training, you should use a smaller validation set. The script had worked fine on the tiny version of dataset that i used to verify if everything was working. For example, the language modeling examples can be run on TPU. 75 MiB free; 13. 26. But I find the GPU-Util is low, but the cpu is full. Trainer. Even when I set use_kd_loss to False (the loss is computed by the super call only), it still does not Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. python -m torch. ; 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 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. But when I run my Trainer, nvtop shows that only GPU 0 is computing anything. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional We will utilize Hugging Face’s Trainer API, which offers an easy interface for training models while supporting distributed training on multiple GPU nodes using the Accelerate library. 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). I use the trainer in hugging face which I understand it will use multiple GPu . Make sure that you have enough GPU memory to store the quarter (or half if your model weights are in half precision) of the model before using this feature. When running DeepSpeed on a single GPU, it helps in the following ways:- I am running the script attached below. The batch size per GPU and gradient accumulation steps are set to 4 and 1. ; 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 When training on a single GPU is too slow or the model weights don’t fit in a single GPUs memory we use a multi-GPU setup. How can i use SFTTrainer to leverage all GPUs automatically? If I add device_map=“auto” I get a Cuda out of memory exception. Could you please clarify if my understanding is correct? and The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex 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. , I am getting same speed. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional According to what I’ve read (HuggingFace doc), deepspeed automatically identifies the GPUs and as I have stage 2 zero optimisation (see config below) the memory used in training of each gpu should be lower than if I am using accelarteor to train a model on multiple GTX 1080 GPU. utils. How to use Pytorch to create a custom EfficientNet with the last layer written correctly. cuda commands; however, I observe no speedup when launching the script as the ordinary python command. 2: 2059: October 18, 2023 Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. @philschmid @nielsr your help would be appreciated import os import torch import pandas as pd from datasets import load_dataset When I run . 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). Each method can We’re on a journey to advance and democratize artificial intelligence through open source and open science. I’m following the training framework in the official example to train the model. I feel like this is an unexpected act, expecting all GPUs would be busy during training. This is my proposal: tokenizer = BertTokenizer. 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. 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 Supervised Fine-tuning Trainer. I am using the pytorch back-end. 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 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. device_count() . It is available in several ZeRO stages, where each stage progressively saves more GPU memory by partitioning the optimizer state, gradients, parameters, and enabling offloading to a CPU or NVMe. This still requires the model to fit on each GPU. Browse the Examples for end-to-end examples of how to use Ray Train. I tried to use cuda and jit from numba like this example to add function decorators, but it still doesn’t help. Even using A100 GPU. When using it with your own model, make sure: your model always return tuples or subclasses of ModelOutput; 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 returns tuples) The Trainer API does support TPUs. The hardest part is likely to be preparing the environment to run Trainer. Modified 1 year, therefore I checked the GPU usage by nvidia-smi and found that both GPU units I read many discussion，they tell me if I use trainer API, I can automatically use multi-gpu. CUDA_VISIBLE_DEVICES = "3,4,5,6" accelerate launch training_script. ; 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 trying to incorporate knowledge distillation loss into the Seq2SeqTrainer. 10. 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. 8: 2816: March 7, 2024 Setting specific device for Trainer. Ask Question Asked 1 year, 2 months ago. . I have several V100 GPUs. This makes it easier to start training faster without manually writing your Hugging Face Forums How can I use trainer. I experimented 3 cases, which are training same model Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. 0: 1189: May 21, 2021 Can I use CUDA with Trainer. 4: I have multiple GPUs available in my enviroment, but I am just trying to train on one GPU. I would like to train some models to multiple GPUs. Can someone tell me if the following script is correct? The only thing I am c Why, using Huggingface Trainer, single GPU training is faster than 2 GPUs? 0. There is no improvement performance between using single and multi GPUs. 1 and DeepSpeed 0. In this article, We will learn how to effectively use DeepSpeed Library with a single GPU and how to integrate it with HuggingFace Trainer API. When I use Trainer module, I am getting faster pr After reading the documentation about the trainer https://huggingface. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF However, usi How does one use accelerate with the hugging face (HF) trainer? Ask Question Asked 1 year, 5 months ago. I’m doing my prototyping at home on a Windows 10 machine with a 4-core CPU with a 1060 gtx. any help would be appreciated. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex Trainer. Shouldn’t it be at 100% consistently until the training it complete? Here is my train. from_pretrained("bert-base 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. But, there is something I couldn’t understand. However, I only want to train the model on a specific number of gpus (maybe 1 or 2). If you don’t have a GPU set up, you can get access to free GPUs or TPUs on Google Colab. I want to use a custom device. When using it on your own model, make sure: your model always return tuples or subclasses of ModelOutput. can you please help me with that? 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. How does one use accelerate with the hugging face (HF) trainer? pytorch, nlp, huggingface-transformers, huggingface. ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" from transformers import Trainer If you’re using Jupyter, you can also use magic commands (again, at the top, How to restrict training to one GPU if multiple are available, co. I'm running this with python train. I’m training environment is the one-machine-multiple-gpu setup. sorry, have u been use multiple GPU by using trainer API? I use it but the results are very strange in comparison with using 1 gpu. The size is more than 8b. 75 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. This causes per_device_eval_batch_size to be only 1 or it goes OOM. I have my data, model, and trainer all set up, and my dataset is of type torch. The Trainer is a complete training and evaluation loop for PyTorch models implemented in the Transformers library. When training on a single GPU is too slow or the model weights don’t fit in a single GPUs memory we use a multi-GPU setup. 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,. 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 I am trying to fine-tune llama on multiple GPU using trl library, and trying to achieve data-parallel and model-parallel both. world_size (int) — The number of processes used in the distributed training. What is the reason of it using CPU instead of GPU? How can I solve it and make the process run on Hello, I’m having a problem in using CUDA with Trainer. But then the device is Hi @Indramal sorry I am trying to fine tune got-neo because of the Cuda memory issue I need to use multiple GPU. Why is that? Hi, I’ve set CUDA_VISIBLE_DEVICES=0,1,2,3 and torch. ” It seems like a user does not have to configure anything when using the Trainer class for doing distributed training. Loading. Using following code for fine-tuning Llama3-8B with ORPO trainer on Kaggle Notebook with 2 T4 GPUs. Supervised Fine-tuning Trainer. 1: 832: January 19, 2023 Trainer API for Model Parallelism on Multiple GPUs. 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 Tried to allocate 20. The API supports distributed training on multiple GPUs/TPUs, Trainer¶. Hello! As I can see, now Trainer can runs multi GPU training even without using torchrun / python -m torch. 9. ; make_multiple_of (int, optional) — If passed, the class assumes the datasets passed to each process are made to be a multiple of this argument (by adding samples). I am using a customized callback in the Trainer to save only the LoRA weights at each epoch. but it didn’t worked How can i use SFTTrainer to leverage all GPUs automatically? If I add device_map=“auto” I get a Cuda out of memory exception. 0 you have the experimental support for DeepSpeed's and FairScale's ZeRO features. And check if the training process can work well normally. This has raised some questions for me, and I hope someone can help clarify: How should I combine DPOTrainer Trainer. I know that when using accelerate (Comparing performance between different device setups), in order to train with the desired learning rate we have to explicitely multiply by the number of gpus. 2 and launching my script with deepspeed (thus the parallelization setup is Distributed Data Parallel). It also includes Databricks-specific recommendations for loading data from the lakehouse and logging models to MLflow, which enables you to use and govern your models on Databricks. when I use Accelerate library, the GPU Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. 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. Let suppose that I use model from HF library, but I am using my own trainers,dataloader,collators etc. If I have a 70B LLM and load it with 16bits, it basically requires 140GB-ish VRAM. I already know that huggingface’s transformers automatically detect multi-gpu. Switching from a single GPU to multiple requires some form of parallelism as the work needs to be distributed. 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. As I see 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) . Tried to allocate 256. I am also using the Trainer class to handle the training. 94 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. py file: import os from tok In the above example, your effective batch size becomes 4. The most common case is where you have a single GPU. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for Trainer¶. Fine-tunning llama2 with multiple GPU hugging face trainer. to("cuda:0"), the GPU with id 0 has 100% consommation and memory usage. from_pretrained('bert-base-uncased') model = BertForNextSentencePrediction. And to fix the issue with the datasets, set their format to torch with . It is irrelevant that you moved the model to cpu or cuda, the trainer will not check it and move your model to cuda if available. After you have converted your Hugging Face Transformers training script to use Ray Train: See User Guides to learn more about how to perform specific tasks. ; 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 Hugging Face Forums How to set gpu device for hugging trainer? 🤗Transformers. Get Started with PyTorch / XLA on TPUs See the “Running on TPUs” section under the Hugging Face examples to get started. from_pretrained('bert-base-uncased', return_dict=True) Specify the GPU you want to use: using huggingface Trainer with distributed data parallel. GPU usage (averaged by minute) is a flat 0. Alternatively, use 🤗 Accelerate to gain full control over the training loop. I am running the model 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). However, the trainer only train the model for 40 steps. DataParallel for one node multi-gpu training. distributed. The basic code structure is similar to the DPO example provided under the llama model in the trl library. train() on my Trainer and it begins training, my GPU usage fluctuates from 0% to around 55%. It also includes Databricks-specific recommendations for loading data from the lakehouse and logging models to MLflow, which enables you to use and govern your models on Azure Databricks. However, in the course, it says it should Hello everyone, I adapted this tutorial into a single script as below. There’s one thing to take into account when training on TPUs: Note: On TPU, you should use the flag --pad_to_max_length in conjunction with the --line_by_line flag to make sure all your batches have the same length. 2. For a more detailed description of our APIs, check out our API_GUIDE, and for performance best practices, take a look at our TROUBLESHOOTING guide. 1. It does not work. I’m trying to train a transformer model on a GPU using transformers. py, which from what I understand, uses all 8 GPUs. I am using the transformer’s trainer API to train a BART model on server. 92 GiB already allocated; 206. Feature request. ; 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 The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. 5: 3479: It seems that the hugging face implementation still uses nn. In this section we have a look at a few tricks to reduce the memory footprint and speed up training for 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. 78 GiB total capacity; 13. The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. but my results are very strange and very different than when I use 1 GPU. Hugging Face Forums How to use specified GPUs with Views Activity; Using 3 GPUs for training with Trainer() of transformers. And I checked it for myself in training log. ; 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 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. I know for sure this is very silly, but I’m a beginner and can’t understand what I’m doing wrong! Transformer version: 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. The training script that I use is similar to the run_summarization script. Is there any configuration to use the GPU with the Trainer API? If I I wrote a simple trainer code as follows: from typing import List from tokenizers import ( decoders HuggingFace Trainer is not using GPU. You can take a look at the scripts for The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. It looks like the default fault setting local_rank=-1 will turn off distributed training However, I’m a bit confused on their latest version of the code If local_rank =-1 , then I imagine that n_gpu would be one, but its being set to torch. Start your TrainingJob by calling fit on a Hugging Face Estimator. What is wrong? How to use GPU with Transformers? My server has two GPUs,(index 0, index 1) and I want to train my model with GPU index 1. 5: 13071: October 16, 2024 Home ; Categories Training large transformer models efficiently requires an accelerator such as a GPU or TPU. I’m using huggingFace Trainer code to train gpt-based large language model. Cuda is installed and my environment can see the 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. For generic PyTorch / XLA examples, run the following Colab Notebooks we offer with Parameters . Modified 9 suggested heuristic: num_params / num_gpus = params/gpu, multiply by precision in bytes to know GBs used gpu_ids: null machine_rank: 0 main_process_ip: null main_process_port: null megatron_lm_config: { } # My company has a 6-gpu server. py, which I think means Trainer uses DP? Hi, there. In this guide, you’ll learn how to use FlashAttention-2 (a more memory-efficient attention mechanism), BetterTransformer (a PyTorch native fastpath execution), and bitsandbytes to quantize your What are the differences and if Trainer can do multiple GPU work, why need Accelerate? Accelerate use only for custom code? (add or remove something) Loading. When i put only one GPU, the training goes on it, but as soon as i put 2 or 3, the training is done on the fi Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. You just need to copy your code to Kaggle, and enable the accelerator(multiple GPUs or single GPU) from the Notebook options. ; padding_index (int, optional, defaults to -100) — The padding Im training using the trainer class on a multi gpu setup. Methods and tools for efficient training on a single GPU. The API supports distributed training on multiple GPUs/TPUs, Apparently, my laptop does not have a Nvidia GPU: running sudo lspci -v | less reveals that my VGA controller is an Intel TigerLake-LP GT2 (Iris Xe Graphics). Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. train(), as it will run very slowly on a CPU. I would like it to use a GPU device inside a Colab Notebook but I am not able to do it. However, I am not able to find which distribution strategy this Train with PyTorch Trainer. Trainer goes hand-in-hand with the TrainingArguments class, which offers a wide range of options to customize how a model is trained. Find the 🤗 Accelerate example further down in this guide. indeed I didn’t change anything in my code for using 1 GPU and let trainer use all gpu but results are very strange. Dataset. You can fine-tune many more NLP models for a wide range of tasks, and the AutoModel classes for Natural Language Processing provide a great foundation. The pytorch examples for DDP states that this should at least be faster:. 69 MiB free; 9. 7GBs. How can I achieve that? I assume that the machine you were using had access to a GPU. The input training data can be a: To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to speed up GPU inference. Here is an example of mine, I have been tested Trainer with Multiple GPUs or Single 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. The hf trainer will automatically use the GPU if it is available. py) Can you tell me what algorithm it uses? DP or DDP? And will the fsdp argument (from TrainingArguments) work correctly in this case? Trainer¶. from transformers import AutoModelForCausalLM model = AutoModelForCausalLM. Is that also the case when using the trainer class? In the case of warmup steps: should the same be applied? i. This is the same for GPUs 1 and 2. This makes it easier to start training faster without manually writing your The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. 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 be that both get used during training and it would be about 2x as fast as single-GPU training. If Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. If you are running a TrainingJob locally, define instance_type='local' or instance_type='local_gpu' for GPU usage. I although I have 4x Nvidia T4 GPUs Cuda is installed and my environment can see the In this article. This extension can be implemented by setting the environment variable CUDA_VISIBLE_DEVICES appropriately before the training process begins. You only need to pass it the necessary pieces for training (model, tokenizer, dataset, evaluation function, training hyperparameters, etc. Based on what I see in the task manager, it looks like The evaluation for question-answering is pretty long as the post-processing (going from the predictions of the models to spans of texts in the contexts) is not on the GPU and is rather long. My training script sees all the available GPUs through torch. Dive into the API Reference for more details on the classes and According to the following question, the trainer will handle multiple GPU work. To keep up with the larger sizes of modern models or to run these large models on existing and older hardware, there are several optimizations you can use to speed up GPU inference. device_count() shows 4. Multiple training with huggingface transformers will give exactly the same result except for the first time. train(). Specify your input training data in fit. Before instantiating your Trainer / TFTrainer, create a TrainingArguments / TFTrainingArguments to access all the points of customization during training. Greater flexibility in specifying Once you’ve done all the data preprocessing work in the last section, you have just a few steps left to define the Trainer. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex The Trainer class is optimized for 🤗 Transformers models and can have surprising behaviors when you use it on other models. Below are some notes to help you use this module, or follow the demos on Google @mapama247 many thanks for your reply. There are several techniques to achieve parallism such as data, tensor, or pipeline parallism. I although I have 4x Nvidia T4 GPUs. I loaded the model with 4bit config, used paged_adam_8bit with Grad checkpointing. I try to train RoBERTa from scratch. Next steps#. KeyError: 337 when training a hugging face model using pytorch. Unfortunately, as I am If you do multi-GPU, any-machine, you can use key dataset lost during training using the Hugging Face Trainer. This kind of problem is not present when training models using the whole PyTorch pipeline, but I would love to understand where I am getting it wrong to use also this powerful class. And causing the evaluation to be slow. The GPU space is enough, however, the training process only runs on CPU instead of GPU. Hi! As @BramVanroy pointed out, our Trainer class uses GPUs by default (if they are available from PyTorch), so you don’t need to manually send the model to GPU. My current machine has 8 gpu cards and I only want to use some of them. Beginners. I usually use Colab and Kaggle for my general training and exploration. Hi! I am pretty new to Hugging Face and I am struggling with next sentence prediction model. 00 MiB (GPU 0; 10. Together, these two huggingface accelerate could be helpful in moving the model to GPU before it's fully loaded in CPU, so it worked when GPU memory > model size > CPU memory by using device_map = 'cuda'!pip install accelerate then use. with_format("torch") to return PyTorch tensors when indexed. py. This guide demonstrates practical techniques that you can use to increase the efficiency of your model’s training by optimizing memory utilization, speeding up the training, or both. syfsmjy qrrtpb vwfzh emhkj kcj slxbvz crbq hood irtpetu hdcaaw