Sfttrainer multi gpu training. Sep 28, 2023 · I have a multi-host environment w...
Sfttrainer multi gpu training. Sep 28, 2023 · I have a multi-host environment with the following GPU configuration: Host1: GPU1 Host2: GPU2 Host3: GPU3, GPU4 Host4: GPU5, GPU6 To make sure that all available GPUs are used for training I'm us 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. You need different hyperparameters for different types of training, e. Feb 1, 2024 · 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. A step-by-step guide to fine-tuning Llama 3. py, which I think means Trainer uses DP? 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. chat_templates import get_chat_template from unsloth import FastLanguageModel, is_bfloat16_supported Jun 7, 2025 · Relevant source files This document details the core distributed training implementation in the unsloth_multi_gpu system. 6 days ago · Highlights Trainers: Various fine-tuning methods are easily accessible via trainers like SFTTrainer, GRPOTrainer, DPOTrainer, RewardTrainer and more. Mar 22, 2023 · This is in contrary to this discussion on their forum that says "The Trainer class automatically handles multi-GPU training, you don’t have to do anything special. ". Feb 1, 2024 · 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. , single-GPU / multi-GPU training, full-parameter tuning, LoRA, or Q-LoRA. @philschmid @nielsr your help would be appreciated import os import torch import pandas as pd from datasets import load_dataset os. environ["MASTER_ADDR Jul 29, 2024 · import torch from trl import SFTTrainer from datasets import load_dataset from transformers import TrainingArguments, TextStreamer from unsloth. . 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. I'm running this with python train. 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 GPUs Designed for high-performance and distributed environments, SFTTrainer supports: Single-GPU, multi-GPU, and multi-node training Distributed Data Parallel (DDP) and Fully Sharded Data Parallel (FSDP) strategies RDMA over Converged Ethernet (RoCE) for optimized multi-node communication Quickstart ¶ For you to start finetuning quickly, we directly provide a shell script for you to run without paying attention to details. Jun 7, 2025 · This page details the training arguments, SFTTrainer setup, and training loop implementation in the unsloth_multi_gpu system. Oct 20, 2024 · AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging, ) from peft import ( LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model, ) import os, torch, wandb from datasets import load_dataset from trl import SFTTrainer, setup_chat_format Checking if torch get all the device, and it does. This config works for single-GPU training and for multi-GPU training with DeepSpeed (see Appendix for full command). So this is confusing as on one hand they're mentioning that there are things needed to be done to train on multiple GPUs, and also saying that the Trainer handles it automatically. Feb 16, 2025 · Multi-GPU Training using SFTTrainer 🤗Transformers sai-santhosh February 16, 2025, 5:17pm 1 Sep 28, 2023 · I have a multi-host environment with the following GPU configuration: Host1: GPU1 Host2: GPU2 Host3: GPU3, GPU4 Host4: GPU5, GPU6 To make sure that all available GPUs are used for training I'm us We’re on a journey to advance and democratize artificial intelligence through open source and open science. I although I have 4x Nvidia T4 GPUs Cuda is installed and my environment can see the available GPUs. It covers the configuration parameters, optimization settings, and execution flow for distributed supervised fine-tuning (SFT) of large language models. g. py, which I think means Trainer uses DP? Mar 22, 2023 · This is in contrary to this discussion on their forum that says "The Trainer class automatically handles multi-GPU training, you don’t have to do anything special. It covers how PyTorch Distributed Data Parallel (DDP) is used to coordinate LLM fine-tuning across multiple GPUs, including process initialization, model loading strategies, and training execution flow. Efficient and scalable: Leverages 🤗 Accelerate to scale from single GPU to multi-node clusters using methods like DDP and DeepSpeed. Apr 8, 2024 · Reproduction When running training using the transformers trainer, and setting device_map to auto, what is the default distributed training type that is used when the model is too large to fit on one GPU? (assume that I have not yet run accelerate config). 1 using SWIFT on multi-GPU RunPod infrastructure, with LoRA configuration, dataset preprocessing, and completion-only training. If you want to use Spectrum check the Appendix for more information. dzzvpoe69wuixht2gy44utexxm3vs56wwbvwbjjsesrfoqlmosh1jcsjej3pbwjjri3ode4qypax7loinlphemiwgaw2j6fhplpq6edk