import os os.environ["CUDA_VISIBLE_DEVICES"] = "1" # or "0,1" for multiple GPUs Run Code Online (Sandbox Code Playgroud) 这样,无论您的计算机上有多少个 GPU,Hugging Face Trainer 都只能查看和使用您指定的 GPU。归档时间: 1年,3 月前 查看次数: 3442 次 最近记录: 1年,3 月前 相关...
我使用了以下Python脚本之一(例如:run_clm.py),其中trainer.train()在那里:https://github.com/...
the full code of trainer HF:https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/trainer.py#L846 Bounty: Does one need to load the model to GPU before calling train when using accelerate? The specific issue I am confused is that I want to use nor...
In your training loop, you call optimizer.step() directly after computing the loss, with no gradient accumulation. Default Trainer uses gradient accumulation (1 gradient accumulation step by default), this causes gradient to be accumulated over multiple batches before model weights update; this i...
I am trying to fine-tune Llama 2 7B with QLoRA on 2 GPUs. 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...
Hi just providing some additional insights from solving this problem. For me, I encountered this exact same issue when training an adapted version of Llama-7B on multiple GPUs using Huggingface Transformers Trainer. The training epochs were fine and the error (deadlock then times out) only happen...
Fix Trainer for Datasets that don't have dict items by @sgugger in #17239 Handle copyright in add-new-model-like by @sgugger in #17218 fix --gpus option for docker by @ydshieh in #17235 Update self-push workflow by @ydshieh in #17177 ...
Trainer._load_from_checkpoint - support loading multiple Peft adapters by @claralp inhttps://github.com/huggingface/transformers/pull/30505 Trainer - add cache clearing and the option for batched eval metrics computation by @FoamoftheSea inhttps://github.com/huggingface/transformers/pull/28769 ...
ONNX Runtime (optimized for GPUs) Habana Before you begin, make sure you have all the necessary libraries installed : pip install --upgrade --upgrade-strategy eager optimum[habana] - from transformers import Trainer, TrainingArguments+ from optimum.habana import GaudiTrainer, GaudiTrainingArguments#...
It contains 115488 samples with multiple rows for each patient or case. Out of 115488 rows, only 33913 (29.364%) have RLE annotations for the class. This number denotes the total number of annotations available. The total number of images with corresponding annotations is 16590. These 16590 im...