Ask questionstrain_tacotron.py: Random CUBLAS_STATUS_INTERNAL_ERROR
Occasionally when training tacotron (
train_tacotron.py), CUDA throws an error and kills the training.
| Epoch: 167/1630 (15/45) | Loss: 0.3459 | 1.1 steps/s | Step: 284k | Traceback (most recent call last): File "train_tacotron.py", line 204, in <module> main() File "train_tacotron.py", line 100, in main tts_train_loop(paths, model, optimizer, train_set, lr, training_steps, attn_example) File "train_tacotron.py", line 144, in tts_train_loop loss.backward() File "C:\Python37\lib\site-packages\torch\tensor.py", line 227, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "C:\Python37\lib\site-packages\torch\autograd\__init__.py", line 138, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: CUDA error: CUBLAS_STATUS_INTERNAL_ERROR when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`
I don't know why this happens, it seems almost random. Sometimes it happens 12 hours after starting, sometimes it happens 15 minutes after starting.
Answer questions serg06
Nice, that's a good solution. And yeah, I found out this morning that it picks up from where it left off very well. How many epochs did you leave it to train for? I'm on 100k so far and will probably let it run until close to a million I guess.
I just followed in this guy's steps and fine-tuned the pre-trained model on my own data. I tried going up to 300k, but I found it starts getting worse after ~260k. I don't think I ever tried training it from scratch.
1 million epochs? Wow, that would take quite a while on my hardware. Can I ask what GPU you're using and how fast your training goes?