Comments (6)
it says we compiled for one device but ran on another.CUDA_VISIBLE_DEVICES=0 fixes this problem for me.However,when i run this command CUDA_VISIBLE_DEVICES=0,1,2 python3 -m alpa.test_install, another error occured , there was a AssertionError,how can i fix that?
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你有几张卡,num_stages默认是2,你的num_devices不能整除num_satges就会出现这个问题,最好保证num_devices为偶数
from alpa.
你有几张卡,num_stages默认是2,你的num_devices不能整除num_satges就会出现这个问题,最好保证num_devices为偶数
我应该怎么修改num_devices的数量,当我尝试输入CUDA_VISIBLE_DEVICES=0,1 python3 -m alpa.test_install,还是会有相同的问题
from alpa.
你有几张卡,num_stages默认是2,你的num_devices不能整除num_satges就会出现这个问题,最好保证num_devices为偶数
我应该怎么修改num_devices的数量,当我尝试输入CUDA_VISIBLE_DEVICES=0,1 python3 -m alpa.test_install,还是会有相同的问题
我不知道怎么修改卡的数目,你有五张卡,并且卡的型号也不一样,你在a100上编译的,在3090上运行可能会出问题
from alpa.
你有几张卡,num_stages默认是2,你的num_devices不能整除num_satges就会出现这个问题,最好保证num_devices为偶数
我的服务器上有5张卡,如下图
我应该怎么修改num_devices的数量,当我尝试输入CUDA_VISIBLE_DEVICES=0,1 python3 -m alpa.test_install,还是会有相同的问题我不知道怎么修改卡的数目,你有五张卡,并且卡的型号也不一样,你在a100上编译的,在3090上运行可能会出问题
我已经解决了这个error,通过在启动ray的时候限制gpu数量ray start --head --num-gpus=2,然后当我继续运行CUDA_VISIBLE_DEVICES=0,1 python3 -m alpa.test_install 另一个error报错了,另外通过CUDA_VISIBLE_DEVICES=0,1可以限定运行的设备,让代码在同一种设备上运行
from alpa.
一个愚蠢的错误,一张显卡被其他用户占用了,换另外的显卡就能顺利执行,十分感谢
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Related Issues (20)
- Will alpa support jax 0.4.x and cuda 12.x?
- cupy package mismatches with CUDA version in the docs HOT 2
- Unable to use pipeline parallelism with multi-node meshes HOT 1
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- Question abuot licence / usage HOT 1
- Problem in building Alpa-modified Jaxlib. HOT 5
- IndexError: `InlinedVector::at(size_type) const` failed bounds check
- Check failed: operand_dim < ins->operand(0)->shape().rank() (2 vs. 2)Does not support this kind of Gather. HOT 2
- How to build debug-version Alpa-modified jaxlib HOT 3
- Unsupported parallel mode in shard-only auto perf test: load_solution
- How to use Alpa to serve BERT models
- Error about python3 -m alpa.test_install
- A question about file /alpa/benchmark/gen_serving_database.py
- Any solution to support llama2 finetune?
- Why did you choose ray instead of using torch distributed? HOT 2
- Ray spill out of disk error when using alpa to auto-parallelize llama HOT 2
- [Bug] Segment fault when using alpa to parallelize llama with jax 0.4.6 environment HOT 2
- How to profile Alpa models and get the trace HOT 1
- Check Installation failled HOT 1
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