Huggingface trainer iterabledataset. In general, an IterableDataset is idea...

Huggingface trainer iterabledataset. In general, an IterableDataset is ideal for big datasets (think hundreds of GBs!) due to its lazy behavior and speed advantages, while a Dataset is great for everything else. Dataset, optional) – The test dataset to use. Dataset. AsyncGRPOTrainer implements the same GRPO algorithm but decouples rollout generation from training. Seems like this is the way to do so: trainer. To get examples from an iterable dataset, you have to iterate over it using a for loop for example. In many cases, it is as easy as dragging and dropping your data files into a dataset repository on the Hub. Model parameters are automatically extracted and estimated from the config. in my case, it takes about an hour to resume training before it “skips” first n batdhes. data. Any Dec 12, 2023 · @amyeroberts thanks, my current solution is check condition of multiple or one gpu I found train_dataloader. train(resume_from_checkpoint=True) To clarify, I instantiated the trainer with a new object of Seq2SeqTrainer. i’m Jun 29, 2023 · Learn how to access the datasets on Hugging Face Hub and how you can load them remotely using DuckDB and the Datasets library Mar 10, 2012 · Expected behavior I use iterable_dataset in datasets to load data streamingly. IterableDataset 的 stream=True，例如。 Train transformer language models with reinforcement learning. Aug 23, 2024 · Motivation Have a project for distributed training on Trainium with DDP that requires use of HuggingFace's IterableDataset (when streaming=True in load. dataset object is not same: 1 gpu:is IterableDataset (datasets. 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. cc @stas00 @sgugger Oct 27, 2020 · #7858 breaks IterableDataset with __len__ in Trainer #8087 Closed cccntu opened this issue on Oct 27, 2020 · 3 comments · Fixed by #8095 Contributor Oct 2, 2023 · System Info I am using a Huggingface implementation of IterableDataset with the set_epoch method with the standard Trainer class. However, during training the _epoch attribute of the dataset is nev Note that if it’s a torch. A Dataset provides fast random access to the rows, and memory-mapping so that loading even large datasets only uses a relatively small amount of device memory. I know there is a datasets. IterableDataset. utils. May 26, 2023 · I have successfully trained a Whisper model using Seq2SeqTrainer for 40k steps. IterableDataset, not the classic map-style datasets. IterableDatasetShard) Mar 7, 2025 · None yet Development Code with agent mode 🏊 [SFT] Compatibility with padding free and iterable dataset huggingface/trl Participants Feb 7, 2023 · hello, i’m having trouble starting from a checkpoint fast because it seems like transformers is running the complete data processing pipeline despite not making use of the first n batches. Tensor objects out of our datasets, and how to use a PyTorch DataLoader and a Hugging Face Dataset with the best performance. I split it into smaller ones so that I can process them by using less RAM. Dataset format By default, datasets return regular python objects: integers, floats, strings, lists, etc. Note that if it’s a torch. Whichever type of dataset you choose to use or create depends on the size of the dataset. I have used datasets. However, I’ve seen that Huggingface do not allow iteration through multiple dataset. However various types of interrupts may occur during the training, which requires resume. Save your dataset by providing the name of the dataset repository on Hugging Face you wish to save it to to push_to_hub (). 3 days ago · Prepare NLP training data as a pipeline, not as a one-time cleanup task. Feb 23, 2025 · Indeed, GRPO doesn't support IterableDataset. We’re on a journey to advance and democratize artificial intelligence through open source and open science. HuggingFace Datasets库是自然语言处理领域广泛使用的数据处理工具，其中IterableDataset是一种高效处理大数据集的重要组件。在最新版本2. Parameters eval_dataset (torch. This doesn't make much sense as evaluation could easily be done using the iterable dataset. shuffle (). To get PyTorch tensors instead, you can set the Will use no sampler if test_dataset is a torch. split_dataset_by_node to distribute the dataset. 🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools - huggingface/datasets This document is a quick introduction to using datasets with PyTorch, with a particular focus on how to get torch. Generator for the randomization that must be identical on all processes (and the Trainer will manually set the seed of this Aug 23, 2024 · Motivation Have a project for distributed training on Trainium with DDP that requires use of HuggingFace's IterableDataset (when streaming=True in load. In the Hugging Face ecosystem, the dataset you train on usually ends up as one of a few shapes: plain text for language modeling, text + label for classification, token-level labels for NER and similar tasks, or messages / prompt - completion records for chat-style fine There are two types of dataset objects, a regular Dataset and then an IterableDataset . However, I'm encountering a number of issues. Therefore iterable datasets are mostly useful for iterative jobs like training a model Processing data in batches ¶ datasets. At each epoch, it does shuffle the dataset and it also groups the samples of roughly the same length size. In the case when using iterable dataset with streaming (which I suspect most do), when resuming huggingface runs over the batches (skips batches), until it reaches the appropriate place to continue the training Apr 13, 2023 · Splitting dataset into Train, Test and Validation using HuggingFace Datasets functions Asked 2 years, 11 months ago Modified 2 years, 10 months ago Viewed 23k times. To get the best speed performance, make sure your dataset doesn’t have an indices mapping. However, I have noticed that this distribution results in different processes having different amounts of data to train on Will use no sampler if test_dataset is a torch. dataset = load_data… TRL supports the Direct Preference Optimization (DPO) Trainer for training language models, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. The abstract from the paper is the following: While large-scale unsupervised language models Oct 27, 2022 · I am trying to train a transformer (Salesforce codet5-small) using the huggingface trainer method and on a hugging face Dataset (namely, "eth_py150_open"). To get PyTorch tensors instead, you can set the Sep 2, 2024 · According to the documentation, I should be able to use the batch method on an iterabledataset. For example, if you're using DeepSpeed, consider utilizing their memory Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face support - NVIDIA-NeMo/Automodel Feb 4, 2026 · Workaround A — Prompt/Completion dataset (completion-only loss) + disable assistant masks TRL supports prompt–completion training; by default it computes loss only on completion tokens. Manning, Chelsea Finn. Dec 15, 2020 · The trainer seems to support passing iterable datasets as the train_dataset (see #5829) but misses to support the same for the eval_dataset. split_dataset_by_node() function which can distribute the shards across the nodes, but should I call it myself inside the The trainer also supports processed datasets (tokenized) as long as they contain an input_ids field. train())? There are two types of dataset objects, a regular Dataset and then an IterableDataset . Dec 14, 2022 · Right now the Trainer uses IterableDatasetShard to skip examples on each node and avoid ending up with duplicate data. amp for PyTorch. my pipeline is pretty straightforward: load data, augment if necessary, tokenize and return the sample. - huggingface/trl Jun 1, 2023 · Just curious- how do I create a train test split from a dataset that doesn’t have a length function? I don’t want to download & tokenize the whole dataset before I split it into training and testing. As a new user, you’re temporarily limited in the number of topics and posts you can create. 6B 模型，这是一个紧凑、多样化的多轮数据集，用于基准测试推理和泛化能力。 This document is a quick introduction to using datasets with PyTorch, with a particular focus on how to get torch. Dec 14, 2022 · As mentioned in #3423, when using PyTorch DDP the dataset ends up with duplicated data. I believe the GPU’s are getting different sizes of batches. Mar 17, 2025 · I’m trying to unify some training code across different mixtures of hf datasets, and it would be nice to make an IterableDataset an infinite length and combine them together with interleave_datasets (sort of like webdataset’s resample=True). Sep 27, 2024 · When attempting to fine-tune a model using the SFTTrainer with an IterableDataset, an error occurs because the SFTTrainer expects a dataset that supports random access (__getitem__). It implements as form of stream processing. But for really, really big datasets that won’t even fit on disk or in memory, an IterableDataset allows you to access and use the dataset without Paste your HuggingFace model config URL (ending in config. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. Some of its features are: large scale parallel data access through sharding high performance disk I/O due to purely sequential reads latency insensitive due to big fat Feb 23, 2025 · Indeed, GRPO doesn't support IterableDataset. I hope it could be clarified in documentation. You can define sampling probabilities for each of the original datasets to specify how to interleave the datasets. train() still calls len() on this Feb 5, 2023 · When using Trainer with 2 GPUs and a batch size of 256, Dataset returns a batch of size 512 (256 per GPU), while IterableDataset returns a batch size of 256 (256 total). However, after one epoch it hangs then crashes after a timeout. A background worker continuously streams completions from a vLLM server while the training loop consumes them, so generation and gradient updates overlap instead of alternating. Currently I want to handle a very big dataset. And I have the checkpoints saved during the training. You can find the Sampler definition here. eval_dataset (Dataset, IterableDataset or dict[str, Dataset | IterableDataset]) — Dataset to use for evaluation. # slower 🐢 >>> iterable_dataset = load_dataset("ethz/food101", streaming= True) to_iterable_dataset() 函数支持分片（sharding），当 IterableDataset 被实例化时。 这在使用大型数据集时非常有用，并且您希望对数据集进行混洗（shuffle）或使用 PyTorch DataLoader 实现快速并行加载。 Once your iterable dataset is ready, you can save it as a Hugging Face Dataset in Parquet format and reuse it later with load_dataset (). Follow up? Mar 7, 2021 · The Seq2SeqTrainer (as well as the standard Trainer) uses a PyTorch Sampler to shuffle the dataset. e. However, during training the _epoch attribute of the dataset is nev Know your dataset There are two types of dataset objects, a regular Dataset and then an IterableDataset . iter(batch_size=) but this cannot be used in combination Jun 10, 2022 · Hi I’m beginner to Huggingface. This is not efficient for vision or audio tasks since we waste I/O and CPU time reading and decoding files that are not used. 0中，用户报告了一个严重的回归问题：当从生成器创建IterableDataset并应用map、select等操作后，数据集无法多 Feb 5, 2023 · When using Trainer with 2 GPUs and a batch size of 256, Dataset returns a batch of size 512 (256 per GPU), while IterableDataset returns a batch size of 256 (256 total). We already check for the PyTorch worker_info for single node, but we should also check for torch. py Top File metadata and controls Code Blame 603 lines Know your dataset There are two types of dataset objects, a regular Dataset and then an IterableDataset . Any Oct 27, 2020 · #7858 breaks IterableDataset with __len__ in Trainer #8087 Closed cccntu opened this issue on Oct 27, 2020 · 3 comments · Fixed by #8095 Contributor Pytorch Distributed native training library for LLMs/VLMs with OOTB Hugging Face support - NVIDIA-NeMo/Automodel The webdataset library is an implementation of PyTorch IterableDataset (or a mock implementation thereof if you aren't using PyTorch). 20. Subclass and override this method if you want to inject some custom behavior. You can easily and rapidly create a dataset with 🤗 Datasets low-code approaches, reducing the time it takes to start training a model. And while instantiating that object, I provided a Oct 2, 2023 · Trainer errors out when concatenating different sequence length batches with distributed training and IterableDataset #26548 Closed ssharpe42 opened on Oct 2, 2023 · edited by ssharpe42 Jul 23, 2020 · 🐛 Bug Information While pre-training a Longformer model from scratch, the text is delivered through an IterableDataset object. By default, all the dataset columns are returned as Python objects. History History 603 lines (522 loc) · 30. Doing extensive googling I’ve come to believe that it’s the way I’m using split_dataset_by_node and shuffle. Now, I want to resume the training from the checkpoint. iterable_dataset. I wonder that is there a method to iterate through several datasets or load them without training from beginning. the dataset thus made is an IterableDataset. This document is a quick introduction to using datasets with PyTorch, with a particular focus on how to get torch. co credentials. Mar 21, 2024 · Hey, I am trying to train a custom model (which inherits from PreTrainedModel) with IterableDataset using the HuggingFace Trainer in a DDP setup and I have a couple of questions on how to do it best as well as some of my observations. import argparse import json import os import time from typing import Dict, Tuple, Union, Optional, Callable from pathlib import Path import numpy as np import torch import torch. skip but it takes a lot of time because it re-iterates on all the past data until it reaches the resuming point. shuffle(). Still, it is possible to shuffle an iterable dataset using datasets. But for really, really big datasets that won’t even fit on disk or in memory, an IterableDataset allows you to access and use # slower 🐢 >>> iterable_dataset = load_dataset("ethz/food101", streaming= True) to_iterable_dataset() 函数支持分片（sharding），当 IterableDataset 被实例化时。 这在使用大型数据集时非常有用，并且您希望对数据集进行混洗（shuffle）或使用 PyTorch DataLoader 实现快速并行加载。 Note The dataset that is returned is a datasets. Is it blocking for you? I am currently working on a refinement pipeline, so I need to dynamically change the training data after each epoch. 19. Generator for the randomization that must be identical on all processes (and the Trainer will manually set the seed of this generator Mar 21, 2024 · Hey, I am trying to train a custom model (which inherits from PreTrainedModel) with IterableDataset using the HuggingFace Trainer in a DDP setup and I have a couple of questions on how to do it best as well as some of my observations. Jun 5, 2024 · I've been trying to finetune a language model on a standard dataset, with streaming=True, i. py/load_dataset () from package datasets==2. Join the Hugging Face community There are two types of dataset objects, a Dataset and an IterableDataset. However, the following code gives an AttributeError: ‘IterableDataset’ object has no attribute ‘batch’. This is a fast approximate shuffling that works best if you have multiple shards and if you specify a buffer size that is big enough. TRL 支持用于训练语言模型的监督微调 (SFT) Trainer。 此训练后方法由 Younes Belkada 贡献。 快速入门 本示例演示了如何使用 TRL 中的 SFTTrainer 训练语言模型。我们将在 Capybara 数据集 上训练 Qwen 3 0. But for really, really big datasets that won’t even fit on disk or in memory, an IterableDataset allows you to access and use the dataset without Oct 31, 2024 · I’m using a IterableDataset from datasets library and passing it to HF Trainer, something like this: from datasets import load_dataset from transformers import Trainer, TrainingArguments ds = load_dataset ("my-dataset",… Jan 1, 2025 · You can login using your huggingface. data_loader. metrics import f1_score Note The dataset that is returned is a datasets. IterableDataset, a sequential sampler (adapted to distributed training if necessary) otherwise. nn as nn import transformers import yaml from datasets import ( Dataset, load_dataset, DatasetDict, IterableDatasetDict, IterableDataset, ) from sklearn. What can I do to avoid or speed up this process? Hi, Is there a parameters that controls whether or not the data get reshuffled before each epoch? And whether or not it is grouped by length? Thanks! Additionally, if the training is aborted and I’m restarting from a checkpoint - does the checkpoint have information about the shuffling order for this given epoch and which datapoints still haven’t gone through this epoch already? Thanks! How to use Huggingface Trainer streaming Datasets without wrapping it with torchdata's IterableWrapper? Mar 7, 2021 · The Seq2SeqTrainer (as well as the standard Trainer) uses a PyTorch Sampler to shuffle the dataset. Now you will tokenize and use your dataset with a framework such as PyTorch or TensorFlow. I don't think there is an easy fix. Refer to the Stream guide for an example of how to interleave IterableDataset objects. Sep 26, 2025 · Additional checks should include training metrics (does it improve training quality to mix the data like this), and behavior check in a DDP settings, we don't want to face any deadlock due to some GPU having more batches than other. (Hugging Face) You generate: We’re on a journey to advance and democratize artificial intelligence through open source and open science. dataset. Always verify your actual training GPU requirements. But you can bridge the gap between a Python object and your machine learning Oct 5, 2023 · Feature request Hi, could you add an implementation of a batched IterableDataset. The API mirrors GRPOTrainer — for full details on the GRPO method itself (advantage computation, KL estimation, loss We’re on a journey to advance and democratize artificial intelligence through open source and open science. Train with 🤗 Datasets ¶ So far, you loaded a dataset from the Hugging Face Hub and learned how to access the information stored inside the dataset. To get the very last example of the dataset, you first have to iterate on all the previous examples. Start with reading Oct 30, 2022 · How to use Huggingface Trainer streaming Datasets without wrapping it with torchdata's IterableWrapper? Ask Question Asked 3 years, 4 months ago Modified 3 years, 4 months ago Jun 7, 2022 · There seems to be not a single sample in your epoch_iterator, stopping training at step 0! This is expected if you're using an IterableDataset and set num_steps (5000000) higher than the number of available samples. Oct 30, 2022 · 如何使用Huggingface处理流数据集？ 怎样在Huggingface中避免使用torchdata的IterableWrapper？ Huggingface流数据集使用有哪些技巧？ 给一个带有 datasets. 0) Your contribution N/A. The code which is called by Trainer. Sep 23, 2023 · Training an LLM on a big dataset (depending on the project) takes considerable amount of time. It already support an option to do batch iteration via . To lift those restrictions, just spend time reading other posts (to be precise, enter 5 topics, read through 30 posts and spend a total of 10 minutes reading). py Top File metadata and controls Code Blame 603 lines 4 days ago · Explore 15 essential datasets for training and evaluating AI agents, including tool calling, web navigation, and coding benchmarks like SWE-bench and WebArena. Jul 4, 2021 · It brings unexpected behavior when training models with iterable dataset and num_train_epochs not set (model is only trained for 3 epochs). json), and enter experiment details. Both interleave_datasets () and concatenate_datasets () work with regular Dataset and IterableDataset objects. But for really, really big datasets that won’t even fit on disk or in memory, an IterableDataset allows you to access and use Oct 30, 2022 · Found the answer from Using IterableDataset with Trainer - `IterableDataset' has no len () By adding the with format to the iterable dataset, like this: 3 days ago · Prepare NLP training data as a pipeline, not as a one-time cleanup task. 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. This page will compare the differences between a Dataset and an IterableDataset to help you pick the right dataset object for you. Oct 30, 2022 · This can be resolved by wrapping the IterableDataset object with the IterableWrapper from torchdata library. 0中，用户报告了一个严重的回归问题：当从生成器创建IterableDataset并应用map、select等操作后，数据集无法多 Nov 16, 2023 · I am wondering why the default value of dispatch_batches is False using IterableDataset in distributional training, and why use DataLoaderDispatcher rather than DataLoaderShard? Jul 4, 2021 · It brings unexpected behavior when training models with iterable dataset and num_train_epochs not set (model is only trained for 3 epochs). I've tried to get around it by subclassing Dataset and having a Mutable Dataset that would randomly sample from my desired dataset, though this has not been working. Jul 25, 2023 · Stream a local parquet file to huggingface trainer with an Iterable Dataset Ask Question Asked 2 years, 7 months ago Modified 2 years, 7 months ago Aug 16, 2024 · I’m currently training a multi-gpu single node model. When I use the trainer to continue training, it will spend a lot of time processing the data. This forum is powered by Discourse and relies on a trust-level system. distributed as dist import torch. Sep 26, 2023 · Hi! You should be able to avoid this data duplication by using split_dataset_by_node as explained in IterableDataset returns duplicated data using PyTorch DDP · Issue #5360 · huggingface/datasets · GitHub. Jan 5, 2024 · Here is the code of how to iterate a huggingface dataset Jan 23, 2023 · the current shard idx and row position in the current shard the epoch number the rng state the shuffle buffer Right now you can already resume the data loading of an iterable dataset by using IterableDataset. Therefore iterable datasets are mostly useful for iterative jobs like training a model We’re on a journey to advance and democratize artificial intelligence through open source and open science. But this later point should be already handled by the iterator of the IterableDataset. IterableDataset ) multiple gpu: IterableDatasetShard (accelerate. distributed. Note: This is a general recommendation and may not be optimal for your specific environment. Nov 17, 2023 · I am using PyTorch DDP (Distributed Data Parallel) to train my model. In the Hugging Face ecosystem, the dataset you train on usually ends up as one of a few shapes: plain text for language modeling, text + label for classification, token-level labels for NER and similar tasks, or messages / prompt - completion records for chat-style fine History History 603 lines (522 loc) · 30. map() can also work with batches of examples (slices of the dataset). This is particularly interesting if you have a mapped function which can efficiently handle batches of inputs like the tokenizers of the fast HuggingFace tokenizers library. I have implemented an iterable dataset for training and now I cannot use the same implementation for evaluation. Oct 31, 2024 · I’m using a IterableDataset from datasets library and passing it to HF Trainer, something like this: from datasets import load_dataset from transformers import Trainer, TrainingArguments ds = load_dataset ("my-dataset",… Jan 27, 2024 · Cc @andrewkho in case you know a way to make the DataLoader stop or add extra samples automatically in case of distributed + unevenly divisible iterable dataset Is the issue of overhanging batches also relevant for map-style datasets? The DistributedSampler drops the last data by default to make the dataset evenly divisible. How do I feed a dataset in such a streaming mode to the SFTTrainer (and/or Trainer. I am using an IterableDataset in streaming mode. To get PyTorch tensors instead, you can set the HuggingFace Datasets库是自然语言处理领域广泛使用的数据处理工具，其中IterableDataset是一种高效处理大数据集的重要组件。在最新版本2. Oct 2, 2023 · System Info I am using a Huggingface implementation of IterableDataset with the set_epoch method with the standard Trainer class. 2 KB main trl / trl / experimental / async_grpo / async_grpo_trainer. Because the amount of data is very large, it cannot enter the training process for a long time. yfu dwsaf kupg mmkhk rujy ebmyvu aqiak mzopzwh fpzyslle qwme