"train_batch_size":"auto", "train_micro_batch_size_per_gpu":"auto", "gradient_accumulation_steps": 10, "steps_per_print": 2000000 } 速度 未完待续 问题 Caught signal7 (Bus error: nonexistent physical address) 在使用单机多卡时,使用官方镜像:registry.cn-beijing.aliyuncs.com/acs/deepspeed:v...
"train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "zero_allow_untested_optimizer": true, "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": ...
"train_micro_batch_size_per_gpu": 10, "gradient_accumulation_steps": 1, "zero_optimization": { "stage": 2, "offload_optimizer": { "device": "cpu" }, "offload_param": { "device": "cpu" }, "stage3_gather_16bit_weights_on_model_save": false }, "steps_per_print": inf, "bf...
accelerate launch \ --config_file deepspeed_config.yaml \ train.py llm_config.yaml DeepSpeed 配置文件 ZeRO-0 ### ds_z0_config.json { "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "zero_al...
"train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "zero_allow_untested_optimizer": true, "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power":...
{"device":"cpu","pin_memory":True}},"gradient_accumulation_steps":1,"steps_per_print":2000,"train_batch_size":32,"train_micro_batch_size_per_gpu":4,"wall_clock_breakdown":False}# 初始化模型和分词器model=AutoModelForCausalLM.from_pretrained('gpt2')tokenizer=AutoTokenizer.from_pretrained...
--do_train \ --dataset_dir data \ --dataset <custom_dataset_name> \ --overwrite_cache \ --finetuning_type lora \ --output_dir <custom_output_path> \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ ...
Figure 1: Intel® Data Center GPU Max Series 1100 PCIe Card The PJRT plugin for Intel GPU is based on LLVM + SPIR-V IR code-gen technique. It integrates with optimizations in oneAPI-powered libraries, such as Intel® oneAPI Deep Neural Network Library (oneDNN) and Intel...
而如果在Cell中是运行脚本,如“!python xxx.py”,那么运行结束后,会像正常脚本一样释放占用的memory。这也是为什么,上面的预处理运行完后,GPU占用显存又恢复为0了。 而当我在使用Accelerate库做并行训练的时候,我发现notebook_launcher函数同样可以实现上面的效果,即通过notebook_launcher函数运行自定义的训练函数,不管...
"--batch-size", action="store", default=128, type=int, help="Size of mini batch.", ) # 优化器选择 parser.add_argument( "-opt", "--optimizer", action="store", default="SGD", type=str, choices=["Adam","SGD"], help="Optimizer used to train the model.", ...