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    • Huggingface trainer use gpu.

  • Huggingface trainer use gpu I have multiple gpu available to me. In other cases, or if you use PyTorch directly, you may need to move your models and data to the GPU to ensure computation is done on the accelerator and not on the CPU. Multi-GPU Training. Single GPU training works, but as soon as I go to multi GPU, everything fails and i cant figure out w… We can see that the model weights alone take up 1. By default it uses device:0. The key is to find the right balance between GPU memory utilization (data throughput/training time) and training GPU. 7GBs. A forward pass is performed on each GPU and their outputs are sent to GPU 0 to compute the loss. Check run_mlm. is_available() returns true) and did model. Jul 19, 2021 · GPU usage (averaged by minute) is a flat 0. property device¶ The device used by this process. HuggingFace提供了类似以下的training_args。当我使用HF trainer来训练我的模型时，默认使用cuda:0。 我查看了HuggingFace文档，但仍然不知道如何在使用HF trainer时指定要在哪个GPU上运行。 GPU. when I use Accelerate library, the GPU-Util is almost 100% Aug 20, 2020 · It starts training on multiple GPU’s if available. DeepSpeed, powered by Zero Redundancy Optimizer (ZeRO), is an optimization library for training and fitting very large models onto a GPU. Model fits onto a single GPU: Normal use; Model doesn’t fit onto a single GPU: ZeRO + Offload CPU and optionally NVMe; as above plus Memory Centric Tiling (see below for details) if the largest layer can’t fit into a single GPU; Largest Layer not fitting into a single GPU: ZeRO - Enable Memory Centric Tiling (MCT). here is DataParallel supports distributed training on a single machine with multiple GPUs. Where I should focus to implement multiple GPU training? Sep 18, 2020 · Yes, I’m using LineByLineTextDataset, which already pre-tokenizes the whole file at the very beginning. I know for sure this is very silly, but I’m a beginner and can’t understand what I’m doing wrong! Transformer version: 4. 1 8b in full precision on 4 gpus of 16 GB VRAM each. This extension can be implemented by setting the environment variable CUDA_VISIBLE_DEVICES appropriately before the training process begins. Feb 7, 2025 · # train_dpo. I am trying to train a T5 model using two gpus but for some reason the trainer only uses one? in my bash file i specified the number of GPUs i wanna use like this: #SBATCH --gres=gpu:2. To use ORTTrainer or ORTSeq2SeqTrainer, you need to install ONNX Runtime Training module and Optimum. Use this to continue training if output_dir points to a checkpoint directory. The Trainer module leverages a TrainingArguments dataclass in order to define the training specifics. launch --nproc-per-node=4 finetune_flan. 2. I feel like this is an unexpected act, expecting all GPUs would be busy during training. @philschmid @nielsr your help would be appreciated import os import torch import pandas as pd from datasets import load_dataset os. here is Oct 21, 2022 · Earlier it was mentioned you can start distributed code directly out of your Jupyter Notebook. The first method demonstrates distributed training with Trainer, and the second Jun 14, 2023 · After reading the documentation about the trainer https://huggingface. Jul 7, 2021 · Using huggingface trainer, all devices are involved in training. I'm training the run_lm_finetuning. Efficient Training on Multiple GPUs. I tried the following settings: Running the script with CUDA available (batch size 64) Running the script with CUDA available (batch size 1048576) Running the script with NO-CUDA (batch size 64 Aug 17, 2022 · HuggingFace Training using GPU. Is there a way to configure the Sep 18, 2020 · Yes, I’m using LineByLineTextDataset, which already pre-tokenizes the whole file at the very beginning. I Dec 1, 2022 · hi @AndreaSottana, sorry I am trying to fine tune got-neo because of the Cuda memory issue I need to use multiple GPU. Even reducing the eval_accumation_steps = 1 did not work. py from datasets import load_dataset from trl import DPOConfig, DPOTrainer from transformers import AutoModelForCausalLM, AutoTokenizer model Jul 13, 2021 · I am trying to set gpu device for HF trainer. The size is more than 8b. DataParallel supports distributed training on a single machine with multiple GPUs. The next layer to be executed is loaded onto the GPU while the current layer is still being executed. Trainer abstracts this process, allowing you to focus on the model, dataset, and training design choices. Modified 4 years, 2 months ago. 数据预加载在 cpu 上提前加载和准备数据批次，以确保 gpu 持续工作，减少 gpu 空闲并提高利用率。有两种方法可以预加载数据以确保 gpu 始终工作。 在 cpu 上分配固定内存以存储数据，并将其直接传输到 gpu。 增加 cpu 线程或工作进程的数量以更快地预加载数据。 The Trainer will work out of the box on multiple GPUs or TPUs and provides lots of options, like mixed-precision training (use fp16 = True in your training arguments). I am trying to fine-tune a language model using the Huggingface libraries, following their guide (with another model and different data, but I don't think this is the crucial point). Aug 10, 2023 · TLDR: Hi, I am trying to train a (lora/p-tune) PEFT model on Falcon 40b model using 3 A100s. And causing the evaluation to be slow. When I run the training, the number of steps equals Mar 10, 2010 · I don't think that you can do that with the current HuggingFace API since it only appears to be using a train_dataset and eval_dataset. The first method demonstrates distributed training with Trainer, and the second Jul 29, 2021 · I read many discussion，they tell me if I use trainer API, I can automatically use multi-gpu. 1: 1754: August 10, 2024 Hi, I am using huggingface run_clm. It allows Nov 10, 2020 · By the way, a couple of months after this, I suggest going for a different task to learn how TPUs work. , a test set) then you'd have to create a separate Trainer object because you would need to prepare your data with Accelerate again. But in my case, it is not true I run the pytorch version example run_mlm. So the easiest API is made hard by missing to mention this script, which I finally found in one of the forums This notebook showed how to perform distributed training from inside of a Jupyter Notebook. With the increasing sizes of modern models, it’s more important than ever to make sure GPUs are capable of efficiently handling and delivering the best possible performance. Feb 21, 2024 · Although this can increase the computational overhead, it significantly lowers the memory footprint, making it possible to train larger models or use larger batch sizes on a single GPU. chunk. 3 I got this Feb 25, 2024 · I would like to train some models to multiple GPUs. Sep 28, 2021 · The Trainer API does support TPUs. I have 8*A10 GPUs with 24GB each but when I try to train the model, it fails to even Jan 2, 2025 · Hello, I’m trying to use a torch_directml device (GPU) for fine-tuning with the Transformers. The model takes up about 32GB when loaded, so each graphic is taken up to about 8GB (8*4). ” However, our experiments have shown that even in a single GPU environment (e. I’m using dual 3060s, so I need to use We can see that the model weights alone take up 1. In this topic, I share the Trainer is an optimized training loop for Transformers models, making it easy to start training right away without manually writing your own training code. If you are using ZeRO, additionally adopt techniques from the Methods and tools for efficient training on a single GPU. , loading ‘sms_spam’ data set, tokening loading the model (“distilbert-base-uncased”), and Feb 9, 2021 · This may be a CPU, GPU, or TPU depending on your environment, but for this blog post we’ll focus primarily on TPU. The only operations that are happening before the input to GPU are the ones in the data collator - which in this case is applying dynamic masking for MLM task. Ask Question Asked 4 years, 2 months ago. output_dir (str, optional, defaults to "trainer_output") — The output directory where the model predictions and checkpoints will be written. According to deepspeed integration documentation , calling the script using the deepspeed launcher and adding the --deepspeed ds_config. I noticed that the model gets moved to the GPU, since the memory increases, but the utilization remains at 0% througout training. GPU. and in my code i added this: Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. Nov 20, 2022 · 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) HuggingFace Training using GPU. You can control which GPU’s to use using CUDA_VISIBLE_DEVICES environment variable i. 0× speedup. amp for PyTorch. json should implement Many frameworks automatically use the GPU if one is available. This is because DTensor is not supported for some of the operations: such as torch. In nvidia-smi and the W&B dashboard, I can see that both GPUs are being used. I am doing this on a Jupyter notebook inside VSCode. Even when I explicitly move the model to the DML device, it gets reverted to the CPU during training. 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? Oct 31, 2024 · This article explores how to fine-tune the BERT model on multiple GPU nodes using Hugging Face’s Trainer and Accelerate libraries, making the process easier and more efficient. 0%. sharded_ddp (bool, optional, defaults to False) – Use Sharded DDP training from FairScale (in distributed training only). 3 I got this Aug 4, 2024 · 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 I find the GPU-Util is low, but the cpu is full. Feb 7, 2024 · I am on the “Fine-tuning a model with the Trainer API” step of Chapter 3 of the NLP Course. However I couldn’t understand why multi-GPU’s training speed is more slower than single-GPU. Jan 31, 2024 · The script above runs fine in PP even when I train/save other modules in the LoRA config. e. It’s used in most of the example scripts. When I tried this step in the Colab notebook, it took several hours. However, I’ve noticed that the Trainer automatically switches to the CPU if neither a CUDA nor SMD device is available. GRPO Trainer. This is where the code blows up! It couldn’t even pass to trainer. data. 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). An up-to-date model is replicated from GPU 0 to the other GPUs. Jun 7, 2023 · HuggingFace offers training_args like below. This is the case for the Pipelines in 🤗 transformers, fastai and many others. This is an experimental feature. Does Dec 11, 2023 · Is there a way to explicitly disable the trainer from using the GPU? I see something about place_model_on_device on Trainer but it is unclear how to set it to False. Any ideas what could be happening here? Jan 24, 2024 · Hello, I am new to LLM fine-tuning. The first method demonstrates distributed training with Trainer, and the second We can see that the model weights alone take up 1. . Enhance training efficiency for RL with >single GPU sampling. compile, mixed precision training, and saving the model to the Hub. Motivation May 15, 2023 · Im new to the huggingface community and to ML and starting playing around with accelerate and followed the instruction set out in the tutorials. I tried using torch. To use it, you don't need to change anything in your training code; you can set everything using just accelerate config. Depending on your GPU and model size, it is possible to even train models with billions of parameters. But it is not using Sep 23, 2024 · cd examples python . Jul 6, 2024 · Hello folks, I have been trying to fine-tune Llama 3 with VeRA adapter on a quite small dataset, which is "mlabonne/guanaco-llama2-1k". python -m torch. I am using a machine with two GPUs (one node). Sep 24, 2021 · While training a LayoutLM V2 model with a QA head we noticed that the evaluation loop stops using the GPU and will take hours to complete a single loop. Aug 20, 2020 · My server has two GPUs,(index 0, index 1) and I want to train my model with GPU index 1. Many frameworks automatically use the GPU if one is available. I am working on a LoRA adaptation of a ProtT5 model. We will go over everything it supports in Chapter 10. How can I load one batch to multiple gpus? It seems like that I ‘must’ load more than one batch on one gpu. Could it be that data structures (tensors I assume) used in our own implementation with each estimation are filling up GPU space and this is overloading our GPU device, and somehow default implementation is using memory garbage collector better? This notebook showed how to perform distributed training from inside of a Jupyter Notebook. Case 3: Largest layer of your model does not fit onto a single GPU. pytorch summary fails with huggingface model. Oct 30, 2020 · Hi! I am pretty new to Hugging Face and I am struggling with next sentence prediction model. N/A Mar 16, 2025 · Hi, Im currently trying to setup multi gpu training using accelerate with the for training GRPO from the TRL library. Overview. It allows Trainer¶. This guide will show you two ways to use Accelerate with Transformers, using FSDP as the backend. 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 even tried manually setting trainer DataParallel supports distributed training on a single machine with multiple GPUs. py . I would like it to use a GPU device inside a Colab Notebook but I am not able to do it. We can see that the model weights alone take up 1. 10. I loaded the model with 4bit config, used paged_adam_8bit with Grad checkpointing. PEGASUS From pytorch to tensorflow. 4. Aug 4, 2023 · 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. Pick and choose from a wide range of training features in TrainingArguments such as gradient accumulation, mixed precision, and options for reporting and logging training metrics. Reproduction. What is wrong? How to use GPU with Transformers? The key is to find the right balance between GPU memory utilization (data throughput/training time) and training speed. Trainer (and thus SFTTrainer) supports multi-GPU training. Generator for the randomization that must be identical on all processes (and the Trainer will manually set the seed of this generator Trainer is powered by Accelerate under the hood, enabling loading big models and distributed training. py with model bert-base-chinese and my own train/valid dataset. My objective is to speed-up the training process by increasing the batch size, as indicated in the requirements of the model I’m Sep 24, 2020 · I have multiple GPUs available in my enviroment, but I am just trying to train on one GPU. When I use HF trainer to train my model, I found cuda:0 is used by default. However, if you desire to tweak your DeepSpeed related args from your Python script, we provide you the DeepSpeedPlugin. I typically enable this outside of the DeepSpeed config file, and set ' gradient_checkpointing ' to true in the TrainingArguments class for HuggingFace. In general, should the GPU utilization be 100% while using this script? Aug 21, 2023 · 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. It allows Feb 13, 2024 · I am pretty sure this question has been answered before, but I could not find it. This causes per_device_eval_batch_size to be only 1 or it goes OOM. Could it be that data structures (tensors I assume) used in our own implementation with each estimation are filling up GPU space and this is overloading our GPU device, and somehow default implementation is using memory garbage collector better? May 28, 2023 · 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. In that case is it safe to set the device anyway and then accelerate in HF's trainer will make sure the actual right GPU is set? Feb 3, 2023 · Multiple gpu training. DeepSpeed. But, for DDP, that results in OOM. 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. 🤗Transformers. Trainer from the Hugging Face library. I followed the procedure in the link: Why is eval Feb 6, 2024 · I am finetuning a DeBERTa-v3-large model on classification, using huggingface trainer. Jun 23, 2022 · Hi, I want to train Trainer scripts on single-node, multi-GPU setting. ⇨ Single GPU. Li, Y. environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3" from transformers import Trainer Jun 13, 2024 · How can i use SFTTrainer to leverage all GPUs automatically? If I add device_map=“auto” I get a Cuda out of memory exception. May 16, 2024 · Hello everyone, I adapted this tutorial into a single script as below. cuda. txt 2>&1 Mar 28, 2025 · Using Hugging Face with Optimum-AMD# Optimum-AMD is the interface between Hugging Face libraries and the ROCm software stack. I read, somewhere earlier, that Google Colab makes a GPU available for free. It handles multiple arguments, from batch sizes, learning rate, gradient accumulation and others, to the devices used. As I understand from the documentation and forum, if I wanted to utilze… Apr 29, 2023 · I am running the script attached below. 11. 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. data import DataLoader # Replace 'model_name' and 'max_seq_length' with your actual model name and max sequence length model_name = 'your_model_name' max_seq_length = your_max_seq_length # Load SentenceTransformer model model = SentenceTransformer(model_name) model Jun 19, 2023 · I’m using huggingFace Trainer code to train gpt-based large language model. Dec 23, 2020 · I also experience this when including my own compute_metrics implementation, and it gradually increases GPU memory occupation over time. Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. GPUs are the standard hardware for machine learning because they’re optimized for memory bandwidth and parallelism. Mar 23, 2022 · @sgugger Are there any samples of how Huggingface Transformer finetuning should be done using GPU please? May 5, 2022 · Hello, I’m having a problem in using CUDA with Trainer. This guide will show you the features available in Transformers and PyTorch for efficiently training a model on GPUs. This comes from Accelerate's notebook_launcher utility, which allows for starting multi-gpu training based on code inside of a Jupyter Notebook. Motivation. Apr 26, 2022 · Try again, but add the os. I understand that the shape of the loss reduction is the same. Tensor and do some of the distributed logic Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. 在实例化你的 Trainer 之前，创建一个 TrainingArguments，以便在训练期间访问所有定制点。 这个 API 支持在多个 GPU/TPU 上进行分布式训练，支持 NVIDIA Apex 的混合精度和 PyTorch 的原生 AMP。 Trainer 包含基本的训练循环，支持上述功能。 You could have noticed that there are local* strategies, which use the same layers as * strategy, but don’t use DTensor at all. py example, it can be easily adapted using the same ideas of my former notebook. I put my training configs in a SFTConfig and initiated a SFTTrainer object as my trainer. Therefore, sometimes we need to use the local* strategies, which use vanilla torch. The training commands are exactly the same on both machines. For example, the language modeling examples can be run on TPU. Jun 14, 2023 · After reading the documentation about the trainer https://huggingface. 7. py to train gptj-6b model with 8 gpu’s. 0. Note that on newer GPUs a model can sometimes take up more space since the weights are loaded in an optimized fashion that speeds up the usage of the model. from_pretrained('bert-base-uncased') model = BertForNextSentencePrediction. IterableDataset with some randomization and you are training in a distributed fashion, your iterable dataset should either use a internal attribute generator that is a torch. Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. Your contribution. I use the trainer in hugging face which I understand it will use multiple GPu . So. I have GPUs available ( cuda. Why is that? Hi, I’ve set CUDA_VISIBLE_DEVICES=0,1,2,3 and torch. Oct 31, 2024 · This article explores how to fine-tune the BERT model on multiple GPU nodes using Hugging Face’s Trainer and Accelerate libraries, making the process easier and more efficient. train() stage since the trainer object (initiated with SFTTrainer ) took so much RAM (not GPU RAM . This is my proposal: tokenizer = BertTokenizer. to(device). py with wiki-raw dataset. , Colab), incorporating DeepSpeed can lead to a 2. Is there any flag which I should set to enable GPU usage? Details. environ["MASTER_ADDR Start by setting up the environment. I then launched the training script on a single-GPU for comparison. device_count() . The exact number depends on the specific GPU you are using. /nlp_example. Feb 7, 2024 · 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. Let suppose that I use model from HF library, but I am using my own trainers,dataloader,collators etc. The use_stream parameter can be activated for CUDA devices that support asynchronous data transfer streams to reduce overall execution time compared to CPU offloading. Is there a way to do it? I have implemented a trainer method. 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. But when I run my Trainer, nvtop shows that only GPU 0 is computing anything. It overlaps data transfer and computation by using layer prefetching. but it didn’t worked for me. The script works correctly when I force it on a single GPU using CUDA_VISIBLE_DEVICE=0 or 1, but when I let it run on both of them it gets stuck here (the dataset is tokenized and cached, but it tokenizes it also when using 2 GPUs): 2/06/2024 15:52:35 - INFO GPU. EDIT: Oh, I see I can set use_cpu in TrainingArguments to False. The key is to find the right balance between GPU memory utilization (data throughput/training time) and training Aug 10, 2023 · TLDR: Hi, I am trying to train a (lora/p-tune) PEFT model on Falcon 40b model using 3 A100s. 単一のGPUでのトレーニングが遅すぎる場合や、モデルの重みが単一のGPUのメモリに収まらない場合、複数のGPUを使用したセットアップが必要となります。 🤗 Accelerate supports training on single/multiple GPUs using DeepSpeed. The Trainer will work out of the box on multiple GPUs or TPUs and provides lots of options, like mixed-precision training (use fp16 = True in your training arguments). The default GPU, GPU 0, reads a batch of data and sends a mini batch of it to the other GPUs. Mar 10, 2010 · I don't think that you can do that with the current HuggingFace API since it only appears to be using a train_dataset and eval_dataset. This concludes the introduction to fine-tuning using the Trainer API. While training using model-parallel, I noticed that gpu:0 is actively computing, while other GPUs set idle despite their VRAM are consumed. 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. TRL supports the GRPO Trainer for training language models, as described in the paper DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models by Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, Y. HuggingFace Trainer logging train data. I’ve read the Trainer and TrainingArguments documents, and I’ve tried the CUDA_VISIBLE_DEVICES thing already. e if CUDA_VISIBLE_DEVICES=1,2 then it’ll use the 1 and 2 cuda devices. Would you please help me how you use multiple GPU for fine tunning the We can see that the model weights alone take up 1. Jun 2, 2022 · The Transformers Trainer is only using 1 out of 4 possible GPUs. The issue i seem to be having is that i have used the accelerate config and set my machine to use my GPU, but after looking at the resource monitor my GPU usage is only at 7% i dont think my training is using my GPU at all, i have a 3090TI. train(). I believe that if you want to use a third dataset (i. Some key notes to remember: Make sure to save any code that use CUDA (or CUDA imports) for the function passed to notebook_launcher() Set the num_processes to be the number of devices used for training (such as number of GPUs, CPUs, TPUs, etc) We can see that the model weights alone take up 1. GPUs are commonly used to train deep learning models due to their high memory bandwidth and parallel processing capabilities. distributed. environ call before you import anything else. Before instantiating your Trainer / TFTrainer, create a TrainingArguments / TFTrainingArguments to access all the points of customization during training. Aug 17, 2023 · When doing fine-tuning with Hg trainer, training is fine but it failed during validation. 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. Jun 8, 2023 · How can I specify which GPU to use when using Huggingface Trainer 问题. As the number of GPU increases, the number of steps(x-axis) are much smaller. Oct 22, 2024 · I am trying to fine-tune llama on multiple GPU using trl library, and trying to achieve data-parallel and model-parallel both. I went through the HuggingFace Docs, but still don't know how to specify which GPU to run on when using HF trainer. To use ONNX Runtime for training, you need a machine with at least one NVIDIA or AMD GPU. pytorch summary fails with huggingface model II: Expected all tensors to be on the same device, but found at least two devices May 7, 2025 · The specific issue I am confused is that I want to use normal training single GPU without accelerate and sometimes I do want to use HF + accelerate. This performs fine-tuning training on the well-known BERT transformer model in its base configuration, using the GLUE MRPC dataset concerning whether or not a sentence is a paraphrase of another. Basically, I am learning how to train a Bert classifier from scratch to classify a set of e-mails as ‘spam’ or ‘not spam’ on Google colab using T4 GPU Most of the code is pretty basic and standard, i. problems : Trainer seems to use ddp after checking device and n_gpus method in TrainingArugments , and _setup_devices in TrainingArguments controls overall device setting. I although I have 4x Nvidia T4 GPUs Cuda is installed and my environment can see the available GPUs. If you create a model outside the trainer, make sure to not pass to the trainer any additional keyword arguments that are relative to from_pretrained() method. The script had worked fine on the tiny version of dataset that i used to verify if everything was working. I am trying to further pre-train a BERT model on domain specific documents using the automodelforMLM with a pytorch framework. py > log. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. The key is to find the right balance between GPU memory utilization (data throughput/training time) and training Feb 20, 2021 · HuggingFace Training using GPU. device_count() shows 4. How Can I fix the problem, and use GPU-Util is full. co/docs/transformers/main_classes/trainer#pytorch-fully-sharded-data-parallel and further on the Trainer is powered by Accelerate under the hood, enabling loading big models and distributed training. utils. I am trying to implement model parallelism as bf16/fp16 model wont fit on one GPU. The training seems to work fine, but it is not using my GPU. For comparison, when I ran the script above without other modules being saved, but varying the batch size up to 16, I got OOM with both the PP and DDP approaches. I am running the model on notebook. NLI is by no means a good example of this. I want to use a custom device. Pre-training or fine-tuning a language model is a good use case for this hardware. Do I need to launch HF with a torch launcher (torch. Note that if it’s a torch. 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. property eval_batch_size¶ The actual batch size for evaluation (may differ from per_gpu_eval_batch_size in distributed training Jan 31, 2020 · I'm training the run_lm_finetuning. import os os. Aug 9, 2023 · I’m trying to train a longformer as a classifier, and I’m currently using a test dataset to try to get this working. I am also using the Trainer class to handle the training. 3 GB of the GPU memory. from_pretrained('bert-base-uncased', return_dict=True) model. Wu, Daya Guo. Viewed 5k times Part of NLP Collective Feb 15, 2022 · Hello, I am having a similar issue where my model is not training on GPU even though it is specified. set_device(). The key is to find the right balance between GPU memory utilization (data throughput/training time) and training Aug 21, 2020 · I’m finetuning GPT2 on my corpus for text generation. To use it is as trivial as importing the launcher: from accelerate import notebook_launcher Mar 29, 2024 · trainer: @sgugger, @muellerzr and @pacman100. json should implement Mar 4, 2024 · from sentence_transformers import SentenceTransformer, losses from torch. At the point where the Trainer is invoked, the tutorial says, “This will start the fine-tuning (which should take a couple of minutes on a GPU)”. g. K. Mar 7, 2025 · Official Hugging Face Transformers documentation states that “if your model fits onto a single GPU and you have enough space to fit a small batch size, you don’t need to use DeepSpeed as it’ll only slow things down. but my results are very strange and very different than when I use 1 GPU. But then the device is being set to cuda Sep 28, 2020 · @sgugger I am using Trainer classes but not seeing any major speedup in training if I use a multi-GPU setup. Sep 12, 2023 · 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. to("cuda:0") prompt = "In Italy May 15, 2023 · Im new to the huggingface community and to ML and starting playing around with accelerate and followed the instruction set out in the tutorials. Is there a more convenient way to specify multiple GPUs in a single node for training (or any hacks that would work now)? Furthermore, there might need to be more detailed configurations for multi-node vLLM/GPRO training runs. Jan 20, 2021 · The dark blue line is using 4-GPUs, grey line is using 2-GPUs and sky blue line is using single-GPU. If you are not using ZeRO, you have to use TensorParallel (TP), because PipelineParallel (PP) alone won’t be sufficient to accommodate the large layer. overwrite_output_dir (bool, optional, defaults to False) — If True, overwrite the content of the output directory. For a deeper dive into using Hugging Face libraries on AMD accelerators and GPUs, refer to the Optimum-AMD page on Hugging Face for guidance on using Flash Attention 2, GPTQ quantization and the ONNX Runtime integration. 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. Configure your training with hyperparameters and options from TrainingArguments which supports many features such as distributed training, torch. It does not work. vaxs avdcnt okypnb ghac jpwlslm tdnda dcgk xuz xiue tfvhce