设置完后将在当前文件夹下生成一个multi_gpu.yaml的文件。内容大概长这样: compute_environment:LOCAL_MACHINEdebug:falsedistributed_type:MULTI_GPUdowncast_bf16:'no'enable_cpu_affinity:falsegpu_ids:0,1,2,3machine_rank:0main_training_function:mainmixed_precision:'no'num_machines:1num_processes:4rdzv_b...
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常简单(只需要在标准的pytorch训练代码中改动不几行代码就可以适应于cpu/单GPU/多GPU的DDP模式/TPU 等不...
main_training_function: main num_machines: 1 num_processes: 2 第三,配置第二个运行配置文件second_config.yaml compute_environment: LOCAL_MACHINE distributed_type: MULTI_GPU fp16: false machine_rank: 0 main_process_ip: null main_process_port: 20655 main_training_function: main num_machines: 1 ...
Hugging Face 最近发布的新库 Accelerate 解决了这个问题。 「Accelerate」提供了一个简单的 API,将与多 GPU 、 TPU 、 fp16 相关的样板代码抽离了出来,保持其余代码不变。PyTorch 用户无须使用不便控制和调整的抽象类或编写、维护样板代码,就可以直接上手多 GPU 或 TPU。 项目地址:https://github.com/huggingface...
Accelerate库使用样例:https://huggingface.co/docs/accelerate/usage_guides/training_zoo 目录 前言(废话) 数据集提取 关于Jupyter内核占用GPU的一些心得 分布式训练(附HuggingFace Accelerate简介) 模型推理(风格生成) 结语 前言(废话) 本专栏是对上一期视频的补充: ...
Pix2PixMulti-GPUMixed precisionEarly stoppingThe Journal of Supercomputing - Generative adversarial networks are gaining importance in problems such as image conversion, cross-domain translation and fast styling. However, the training of...doi:10.1007/s11227-022-04354-1Lupión, M....
distributed_type: MULTI_GPU downcast_bf16:'no' machine_rank: 0 main_training_function: main mixed_precision: fp16 gpu_ids: 1,3 num_machines: 1 num_processes: 2 rdzv_backend:static same_network:true tpu_env: [] tpu_use_cluster:false ...
it contains. By distributing experts across workers, expert parallelism addresses the high memory requirements of loading all experts on a single device and enables MoE training on a larger cluster. The following figure offers a simplified look at how expert parallelism wo...
([0] No distributed training, [1] multi-CPU, [2] multi-GPU, [3] TPU): 2 How many different machines will you use (use more than 1 for multi-node training)? [1]: 2 What is the rank of this machine (from 0 to the number of machines - 1 )? [0]: 0 What is the IP ...
interprocess communication through NVLink. ThecuFFTandcuBLASlibraries take advantage of NVLink for better multi-GPU scaling including problems where communication is a significant bottleneck today. The combination of Unified Memory and NVLink enables faster, easier data sharing between CPU and GPU code....