Currently, I am trying to fine tune the Korean Llama model(13B) on a private dataset through DeepSpeed and Flash Attention 2, TRL SFTTrainer. I am using 2 * A100 80G GPUs for the fine-tuning, however, I could not conduct the fine-tuning.. I can't find out the problem and any solu...
per_gpu_train_batch_size=32, save_steps=10_000, save_total_limit=2, ) trainer = Trainer( model=model, args=training_args, data_collator=data_collator, train_dataset=dataset, prediction_loss_only=True, ) 开始训练 trainer.train() 保存最终的模型(model+tokenizer+config) trainer.save_model(voc...
初始化accelerate对象accelerator = Accelerator() 调用prepare方法对model、dataloader、optimizer、lr_schedluer进行预处理 删除掉代码中关于gpu的操作,比如.cuda()、.to(device)等,让accelerate自行判断硬件设备的分配 将loss.backbard()替换为accelerate.backward(loss) 当使用超过1片GPU进行分布式训练时,在主进程中使用...
from colossalai.zero.shard_utils import TensorShardStrategyzero = dict(model_config=dict(shard_strategy=TensorShardStrategy(), tensor_placement_policy="auto"), optimizer_config=dict(gpu_margin_mem_ratio=0.8))第二步,是在配置文件准备好后,插入几行代码来启动新功能。首先,通过一行代码,使用...
solver_mode: GPU 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 训练 执行如下命令开始对bvlc_reference_caffenet.caffemodel 来进行微调训练 sudo ./build/tools/caffe train --solver ./models/bvlc_reference_caffenet/my_solver.prototxt --weights ./models/bvlc_reference_caffenet/bvlc...
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 normal training single GPU without accelerate and sometimes I do want to use HF + accelerate. In that case is it safe ...
engine,train_dataloader,eval_dataloader,lr_scheduler=colossalai.initialize(model=model,optimizer=optimizer,criterion=criterion,train_dataloader=train_dataloader,test_dataloader=eval_dataloader,lr_scheduler=lr_scheduler) 1. 2. 3. 4. 5. 6. 还是得靠GPU+CPU异构 而能够让用户实现如上“傻瓜式”操作的关键,...
docker run --rm -it -v `pwd`/model/GFPGANCleanv1-NoCE-C2.pth:/GFPGAN.pth -v `pwd`/data:/data soulteary/docker-gfpgan 当命令执行完毕之后,在data目录中,会多出一个result.html文件,里面记录了模型处理前后的图片结果。使用浏览器直接打开,可以看到类似下面的结果: ...
zero=dict(model_config=dict(shard_strategy=TensorShardStrategy(),tensor_placement_policy="auto"),optimizer_config=dict(gpu_margin_mem_ratio=0.8)) 第二步,是在配置文件准备好后,插入几行代码来启动新功能。 首先,通过一行代码,...
(model, (dummy_input, ), 'model.onnx'),将这些对象导出为ONNX格式,在这个接口最重要的两个参数分别为 torch.nn.Module 模型对象 model,和一组模拟的输入数据 dummy_input,由于 PyTorch 是支持动态的 input shape,输入没有固定的 shape,因此我们需要根据实际情况,找到每个模型的 input shape,然后再创建模拟...