运行时出现存储空间不足问题

[ice-lynn]

运行训练代码时，总是报错OSError: [Errno 28] No space left on device，但实际上空间内存足够。这是因为什么原因呢

(vits) root@3a5036f18e77:~/VITS-fast-fine-tuning# export CUDA_VISIBLE_DEVICES=0 && python finetune_speaker_v2.py -m ./OUTPUT_MODEL --max_epochs "1000" --drop_speaker_embed True /root/miniconda3/envs/vits/lib/python3.8/site-packages/joblib/_multiprocessing_helpers.py:46: UserWarning: [Errno 28] No space left on device. joblib will operate in serial mode warnings.warn('%s. joblib will operate in serial mode' % (e,)) Traceback (most recent call last): File "finetune_speaker_v2.py", line 372, in main() File "finetune_speaker_v2.py", line 55, in main mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) File "/root/miniconda3/envs/vits/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 239, in spawn return start_processes(fn, args, nprocs, join, daemon, start_method='spawn') File "/root/miniconda3/envs/vits/lib/python3.8/site-packages/torch/multiprocessing/spawn.py", line 182, in start_processes error_queue = mp.SimpleQueue() File "/root/miniconda3/envs/vits/lib/python3.8/multiprocessing/context.py", line 113, in SimpleQueue return SimpleQueue(ctx=self.get_context()) File "/root/miniconda3/envs/vits/lib/python3.8/multiprocessing/queues.py", line 336, in init self._rlock = ctx.Lock() File "/root/miniconda3/envs/vits/lib/python3.8/multiprocessing/context.py", line 68, in Lock return Lock(ctx=self.get_context()) File "/root/miniconda3/envs/vits/lib/python3.8/multiprocessing/synchronize.py", line 162, in init SemLock.init(self, SEMAPHORE, 1, 1, ctx=ctx) File "/root/miniconda3/envs/vits/lib/python3.8/multiprocessing/synchronize.py", line 57, in init sl = self._semlock = _multiprocessing.SemLock( OSError: [Errno 28] No space left on device

空间内存： (vits) root@3a5036f18e77:~/VITS-fast-fine-tuning# df -lh Filesystem Size Used Avail Use% Mounted on overlay 879G 409G 426G 50% / tmpfs 64M 0 64M 0% /dev shm 64M 64M 0 100% /dev/shm /dev/sda2 879G 409G 426G 50% /etc/hosts tmpfs 32G 12K 32G 1% /proc/driver/nvidia tmpfs 6.3G 3.5M 6.3G 1% /run/nvidia-persistenced/socket udev 32G 0 32G 0% /dev/nvidia0

[anfogy]

试试这个

[hedane]

shm 64M 64M 0 100% /dev/shm

就是这个不够

[ice-lynn]

shm 64M 64M 0 100% /dev/shm

就是这个不够

你是对的，已经解决问题啦