Accelerate set device.

Accelerate set device Reload to refresh your session. 2w次，点赞19次，收藏29次。HuggingFace 的 accelerate 库可以实现只需要修改几行代码就可以实现ddp训练，且支持混合精度训练和TPU训练。 from accelerate import Accelerator import math import contextlib gradient_accumulation_steps = 2 accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) model, optimizer, training_dataloader, scheduler = accelerator. Jul 14, 2023 · (created by @Narsil who was stuck outside github) from transformers import AutoModel from accelerate. May 24, 2022 · CUDA_VISIBLE_DEVICES=2,3 accelerate launch second_config. Close. Transformer(). It automatically detects your distributed training setup and initializes all the necessary components for training. set_seed (seed: int) [source] ¶ Helper function for reproducible behavior to set the seed in random, numpy, torch. process_index. transforms import Compose, RandomResizedCrop, Resize, ToTensor from accelerate import Accelerator from accelerate. """ if AcceleratorState (). data import DataLoader, Dataset from torchvision. Apr 16, 2021 · 🤗 Accelerate Run your raw PyTorch training scripts on any kind of device. device_placement. Designed to be used when only process control and device execution states are needed. There are many ways to launch and run your code depending on your training environment (torchrun, DeepSpeed, etc. 9k次，点赞12次，收藏13次。本文介绍了如何在Python代码中解决TensorFlow默认使用特定GPU的问题，通过设置`ACCELERATE_TORCH_DEVICE`环境变量和`CUDA_VISIBLE_DEVICES`来指定显卡，并讨论了Accelerator类的配置，包括device_placement、混合精度等。 Oct 24, 2024 · 这是一个例子： import torch import torch. For instance, training on 4 GPUs with a batch size of 16 set when creating the training dataloader will train at an actual batch size of 64 (4 * 16). You switched accounts on another tab or window. py # pytorch指定GPU和nohup同时使用的时候出错”no such directory or file” CUDA_VISIBLE_DEVICES=0 nohup python -u main. Aug 18, 2023 · # This has to be changed if you're training with multiple GPUs. from_pretrained添加参数low_cpu_mem_usage=True之后，会报错误 RuntimeError: Only Tensors of floating point and complex dtype can require gradients E from accelerate import Accelerator import math import contextlib gradient_accumulation_steps = 2 accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) model, optimizer, training_dataloader, scheduler = accelerator. The Command Line (huggingface. 方便使用：用一个例子感受一下。 Aug 11, 2022 · At the rather innoculous line of code, accelerator = accelerate. 通过 device_map="auto" ， Accelerate 根据可用资源自动决定将模型的每一层放在哪里： 首先，我们使用 GPU 上的最大可用空间。 如果我们仍然需要空间，我们将剩余的权重存储在 CPU 上。 Jun 2, 2021 · I found that the accelerator's device will be set to cpu if I called accelerator = Accelerator() after import tw_rouge which is a package used to count traditional Chinese rouge using ckiptagger and rouge package below: Jan 21, 2022 · Current set_seed implementation set the same seed for every devices, which could results the same data agumentation applied for each data across the process and damage model performance. current_device() should return the current device the process is working on. yaml train_script. Jul 23, 2024 · You signed in with another tab or window. Note that each process has its own device. Consider set different random seed for each device, such as: Quicktour. compile (if configured in the Accelerator object). 2 python代码中设定：2. seed (int) – The seed to set. device) — The device to use. But I think the accelerator. Accelerate通过该统一的配置文件自动为不同的训练框架(DeepSpeed, FSDP 等)选择合适的配置值，也可以在命令行中显式指定配置值。但大部分情况下，我们应该总是先生成配置文件来设置训练环境。Accelerate会自动使用最大数量的可用GPU并设置 混合精度 模式。 Apr 28, 2023 · such that if the model has parameters on multiple devices, or the hf_device_map uses multiple devices, (or maybe the user passes an explicit parameters saying they're using model parallelism), the DDP initialisation doesn't set device_ids and output_device. model = AutoModelForCausalLM. to(device) model = torch. yaml file in your cache folder for Accelerate. 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed suppo Jun 5, 2024 · 文章浏览阅读731次。确保在所有分布式情况下使用 utils. You should set here all blocks that include a residual connection of some kind. device_placement (bool, optional) — Whether or not to place the model on the proper device. I’m training environment is the one-machine-multiple-gpu setup. You signed out in another tab or window. However, if you desire to tweak your DeepSpeed related args from your Python script, we provide you the DeepSpeedPlugin. Accelerate. yaml main. export TRAINING_NUM_PROCESSES=2 export TRAINING_NUM_MACHINES=1 # These should remain empty if you remove their options. functional as F from datasets import load_dataset + from accelerate import Accelerator + accelerator = Accelerator() - device = 'cpu' + device = accelerator. DataLoader 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed suppo By default, DeepSpeed will attempt to use passwordless SSH from the main machine node to the other nodes to perform the launcher command. Tensor , optional ) — The value of the tensor (useful when going from the meta device to any other device). 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? (I am doing a single server multiple gpus) – Next, the weights are loaded into the model for inference. 🤗 Accelerate was created for PyTorch users who like to have full control over their Apr 28, 2023 · Hugging Face在GitHub上开源了一系列的机器学习库和工具，在其组织页面置顶了一些开源库，包括transformers、diffusers、datasets、peft、accelerate以及optimum，本篇逐一详细介绍并给出对应的实战用例，方便读者更直观的理解和应用。 Jan 10, 2024 · 文章浏览阅读1. com device (torch. Next, we wrap the model inside DistributedDataParallel class, passing in a list of device IDs which is the local_process_index that's separate for each process again! Apr 8, 2024 · 使用CUDA_VISIBLE_DEVICES。1. def wait_for_everyone (): """ Introduces a blocking point in the script, making sure all processes have reached this point before continuing. It doesn’t need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. lr_scheduler import OneCycleLR from torch. It just puts everything on gpu:0, so I cannot use Sep 23, 2024 · accelerate launch . /nlp_example. device) — The device on which to set the tensor. May 11, 2021 · CUDA_VISIBLE_DEVICES=1 accelerate launch accelerate_config. g. Most high-level libraries above PyTorch provide support for distributed training and mixed precision, but the abstraction they introduce require a user to learn a new API if they want to customize the underlying training loop. ) May 26, 2022 · 文章浏览阅读1. device (int, str or torch. functional as F from datasets import load_dataset # 增加一行import from accelerate import Accelerator # 实例化，然后获取当前默认的device accelerator = Accelerator() # 这里model不需要. The same data structure as tensor with all tensors sent to the proper device. These configs are saved to a default_config. distributed_type == DistributedType. accelerate. device, optional) — If passed, the device to put all batches on. Tensor`): The data to send to a given device. evaluation_mode (bool, optional, defaults to False) — Whether or not to set the model for evaluation only, by just applying mixed precision and torch. Transformer() optimizer = torch. py You can see that both GPUs are being used by running nvidia-smi in the terminal. device (torch. device. value ( torch. mixed precision不同的分布式训练场景, e. device]], optional) — A map that specifies where each submodule should go. Does not need to be initialized from Accelerator. I’m following the training framework in the official example to train the model. 使用函数 set_device使用多GPU实验结果 原理：通过依靠GPU的并行计算能力，能够大大缩短模型训练时间。 device_placement (bool, optional) — Whether or not to place the model on the proper device. py >log. modeling import set_module_tensor_to_device from transformers import AutoModelForCausalLM, AutoTokenizer self. The device_map. co) 使用 accelerate launch. utils import set_module_tensor_to_device import torch model = AutoModel. prepare( model, optimizer, training_dataloader, scheduler ) training_iterator = iter (training_dataloader) num_samples_in_epoch = len (training Apr 25, 2021 · Since device_placement is set to True by default, first, we move the model to self. The current alternative is to use CUDA_VISIBLE_DEVICES but a dedicated argument in accelerate config or in the Accelerator object would Jan 10, 2023 · You signed in with another tab or window. The actual batch size for your training will be the number of devices used multiplied by the batch size you set in your script. i typically like to have a GPU ID argument in my script so that I can set the device, but if Accelerate is handling the devices I can't specify. data. Config accelerate to use CPU: $ accelerate config In which compute environment are you running? ([0] This machine, [1] AWS (Amazon SageMaker)): 0 Which type of machine are you using? accelerate 是huggingface开源的一个方便将pytorch模型迁移到 GPU/multi-GPUs/TPU/fp16 模式下训练的小巧工具。 和标准的 pytorch 方法相比，使用accelerate 进行多GPU DDP模式/TPU/fp16/bf16 训练你的模型变得非… Feb 23, 2023 · device_map={"":0} simply means "try to fit the entire model on the device 0" - device 0 in this case would be the GPU-0 In a distributed setting torch. I'd be happy to submit a PR to make that change if that seems reasonable. You can see the device_map that Accelerate picked by accessing the hf_device_map attribute of your model: You signed in with another tab or window. Accelerate offers a unified interface for launching and training on different distributed setups, allowing you to focus on your PyTorch training code instead of the intricacies of adapting your code to these different setups. To have Accelerate compute the most optimized device_map automatically, set device_map="auto". Apr 8, 2023 · Is there an existing issue for this? I have searched the existing issues Current Behavior 当在AutoModel. Args: tensor (nested list/tuple/dictionary of :obj:`torch. py #指定process id. import os, re, torch, PIL import numpy as np from torch. utils import set_seed from timm import create_model device_map (Dict[str, Union[int, str, torch. Warning:: Make sure all processes will reach this instruction otherwise one of your processes will hang forever. model_path, t…. utils. The actual batch size for your training will be the number of devices used multiplied by the batch size you set in your script: for instance training on 4 GPUs with a batch size of 16 set when creating the training dataloader will train at an actual batch size of 64. device`): The device to send the data to Returns: The same data structure as :obj:`tensor` with all tensors sent to the proper device. """ if isinstance (tensor, (list, tuple)): return honor_type (tensor, (send_to_device Oct 21, 2021 · I’m training my own prompt-tuning model using transformers package. Desktop Submenu. You can see the device_map that Accelerate picked by accessing the hf_device_map attribute of your model: Jul 20, 2022 · It would be ideal to specify the GPU IDs that a script can use. py Here is my accelerate_config. cuda. even I wanted to rewrite it like cuda:1 or cuda:2 but it couldn’t be modified. yaml 文件。 你也可以通过标志 --config_file 来指定你要保存的文件的位置。 May 20, 2024 · 文章浏览阅读1. nn. If using the nossh launcher, you will need to run the accelerate launch command on every node using copied Py之accelerate：accelerate的简介、安装、使用方法之详细攻略 目录 accelerate的简介 accelerate的安装 accelerate的使用方法 accelerate的简介 Accelerate 是一个为 PyTorch 用户设计的库，旨在帮助简化分布式训练和混合精度训练的过程。它提供了一种简单且灵活的方式来加速和 Singleton class that has information about the current training environment and functions to help with process control. Will default to self. This parameter will indicate that some of the modules with the name "Block" should not be split across different devices. , multi-GPU, TPUs, …提供了一些 CLI 工具方便用户更快的 configure & test 训练环境，launch the scripts. ) and available hardware. 使用以下命令快速启动 As briefly mentioned earlier, accelerate launch should be mostly used through combining set configurations made with the accelerate config command. For more information about each option see here. CUDA_VISIBLE_DEVICES=2,3 accelerate launch --main_process_port 20655 train_script. from_pretrained("gpt2") The Accelerator is the main entry point for adapting your PyTorch code to work with Accelerate. 1 直接在终端或shell脚本中设定：1. The load_checkpoint_and_dispatch() method loads a checkpoint inside your empty model and dispatches the weights for each layer across all available devices, starting with the fastest devices (GPU, MPS, XPU, NPU, MLU, SDAA, MUSA) first before moving to the slower ones (CPU and hard drive). device() is always cuda:0. Returns. device) – The device to send the data to. However, the Accelerator fails to work properly. set_seed() 完全设置种子，以使训练可复现。举例：假设我们有：两个GPU用于“多GPU”、一个带有8个工作站的TPU pod。学习率应该根据设备的数量线性缩放。_accelerate 配置 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed suppo Apr 1, 2025 · 🤗 Accelerate supports training on single/multiple GPUs using DeepSpeed. device model = torch. Adam(model. As hinted at by the configuration file setup above, we have only scratched the surface of the library’s features. More features. 5w次，点赞23次，收藏45次。1. Accelerate 有一个特殊的 CLI 命令，可帮助您通过加速启动在系统中启动代码。该命令包含在各种平台上启动脚本所需的所有不同命令. yaml file: compute_environment: LOCAL_MACHINE distributed_type: MULTI_GPU fp16: true machine_rank: 0 main_process_ip: null main_process_port: null main_training_function: main num_machines: 1 num_processes: 2 See full list on github. 前言Accelerate 能帮助我们：方便用户在不同设备上 run Pytorch training script. parameters()) dataset = load_dataset('my_dataset') data = torch. Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code! In short, training and inference at scale made simple, efficient and adaptable. To use it, you don't need to change anything in your training code; you can set everything using just accelerate config. Windows; Mac; Linux; Chromebook; Microsoft; Programming This parameter will indicate that some of the modules with the name "Block" should not be split across different devices. from_pretrained( self. device (:obj:`torch. You don’t need to explicitly place your model on a device because Accelerator knows which device to move your model to. 🤗 Accelerate provides an easy API to make your scripts run with mixed precision and on any kind of distributed setting (multi-GPUs, TPUs etc. Accelerator() I get the following errors when running on my cluster: Traceback (most recent call last device_specific (bool, optional, defaults to False) — Whether to differ the seed on each device slightly with self. Should be one or several of: "torch": the base torch random number generator "cuda": the CUDA random number generator (GPU only) Apr 19, 2024 · 然后，在命令行使用 accelerate config 来配置accelerate的运行环境. export ACCELERATE_EXTRA_ARGS="--multi_gpu" # --multi_gpu or other similar flags for huggingface accelerate export TRAINER_EXTRA_ARGS="--allow_tf32 --use_8bit_adam --use_ema" # anything you want to pass along Dec 7, 2022 · Sign in now. Run your raw PyTorch training script on any kind of device. rng_types (list of str or RNGType) — The list of random number generators to synchronize at the beginning of each iteration. . In this configuration, the accelerate launch command only needs to be run on the main node. optim. Accelerate 还提供了一个 CLI 工具，它统一了所有的 launcher ，所以你只需要记住一个命令： accelerate config 你需要回答问题，然后 Accelerate 将在你的 cache folder 创建一个 default_config. This cache folder is located at (with decreasing order of priority): import torch import torch. Nov 20, 2023 · I want to use GPUs with different conditions. Helper function for reproducible behavior to set the seed in random , numpy , torch . Parameters. My current machine has 8 gpu cards and I only want to use some of them. txt & Sep 29, 2023 · 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. prepare( model, optimizer, training_dataloader, scheduler ) training_iterator = iter (training_dataloader) num_samples_in_epoch = len (training Jul 12, 2023 · What are the code changes one has to do to run accelerate with a trianer? I keep seeing: from accelerate import Accelerator accelerator = Accelerator() model, optimizer, training_dataloader, sche from accelerate. Available attributes: device (torch. zzel jvoany mkjvva hpzw eluw tbsfez phc npzyi ygthwj snfdk iqbyj jajimkjt gytnc nmcds hsahqd