例如,假设你有4个GPU,train_micro_batch_size_per_gpu为32,gradient_accumulation_steps为4。那么,train_batch_size将是 32 * 4 * 4 = 512。这意味着,虽然每个GPU在每个迭代中只处理32个样本,但在执行一次参数更新之前,系统总共处理了512个样本。 4.train_batch_size: 这个参数表示整个训练批量的大小,通常是...
1、train_batch_size[int] 有效训练批量大小。这是导致模型更新一步的数据样本量。train_batch_size由单个 GPU 在一次前向/后向传递中处理的批量大小(又称为train_micro_batch_size_per_gpu)、梯度累积步骤(又称为gradient_accumulation_steps)和 GPU 数量共同决定。如果同时提供了train_micro_batch_size_per_gpu...
AssertionError: Check batch related parameters. train_batch_size is not equal to micro_batch_per_gpu * gradient_acc_step * world_size 9 != 1 * 3 * 1 To Reproduce Steps to reproduce the behavior: Run the following script on a Ray cluster with 3 nodes, each hosting 1 NVIDIA GPU A100 ...
"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...
config 基础配置 为了简化理解,配置为简单的 pp=2 dp=1 mp=0 上述配置可以在 DeepSpeedExamples/pipeline_parallelism/ds_config.json 进行配置,其中 micro batch num=train_batch_size/train_micro_batch_size_per_gpu=2. # DeepSpeedExamples/pipeline_parallelism/ds_config.json { "train_batch_size" : 256,...
"train_micro_batch_size_per_gpu":2 } Author markWJJ commented May 18, 2023 现在是做deepspeed 这是config Author markWJJ commented May 18, 2023 就改了 batch_size 和max_seq_len:1024 Owner ssbuild commented May 18, 2023 就改了 batch_size 和max_seq_len:1024 你这个标题属实没看懂,建...
"steps_per_print":2000, "train_batch_size":"auto", "train_micro_batch_size_per_gpu":"auto", "wall_clock_breakdown":false } 现在,该训练脚本上场了。我们根据Fine Tune FLAN-T5准备了一个run_seq2seq_deepspeed.py训练脚本,它支持我们配置 deepspeed 和其他超参数,包括google/flan-t5-xxl的模型 ID...
"auto","gradient_clipping":"auto","train_batch_size":"auto","train_micro_batch_size_per_gpu...
{"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,"initial_scale_power":16,"loss_scale_window":1000,"hysteresis":2,"min_...
We demonstrate simultaneous memory and compute efficiency by scaling the size of the model and observing linear growth, both in terms of the size of the model and the throughput of the training. In every configuration, we can train approximately 1.4 billion parameters per GPU, which is the ...