HERRO is a highly-accurate, haplotype-aware, deep-learning tool for error correction of Nanopore R10.4.1 or R9.4.1 reads (read length of >= 10 kbps is recommended).
I am trying to herro with quite a big dataset (4 Gb plant genome, ~72x depth). I already did the AvA step, But now I am struggling in the inference step:
Error log
```raw
thread '' panicked at src/inference.rs:172:64:
called `Result::unwrap()` on an `Err` value: Torch("The following operation failed in the TorchScript interpreter.
Traceback of TorchScript, serialized code (most recent call last):
File \"code/__torch__/model.py\", line 31, in forward
target_positions: List[Tensor]) -> Tuple[Tensor, Tensor]:
embedding = self.embedding
bases_embeds = (embedding).forward(bases, )
~~~~~~~~~~~~~~~~~~ <--- HERE
_0 = [bases_embeds, torch.unsqueeze(qualities, -1)]
x = torch.cat(_0, -1)
File \"code/__torch__/torch/nn/modules/sparse.py\", line 18, in forward
_0 = __torch__.torch.nn.functional.embedding
weight = self.weight
_1 = _0(input, weight, 11, None, 2., False, False, )
~~ <--- HERE
return _1
File \"code/__torch__/torch/nn/functional.py\", line 37, in embedding
else:
input0 = input
_3 = torch.embedding(weight, input0, padding_idx0, scale_grad_by_freq, sparse)
~~~~~~~~~~~~~~~ <--- HERE
return _3
def batch_norm(input: Tensor,
Traceback of TorchScript, original code (most recent call last):
File \"/raid/scratch/stanojevicd/projects/haec-BigBird/model.py\", line 118, in forward
'''
# (batch_size, sequence_length, num_alignment_rows, bases_embedding_size)
bases_embeds = self.embedding(bases)
~~~~~~~~~~~~~~ <--- HERE
# concatenate base qualities to embedding vectors
File \"/home/stanojevicd/miniforge3/envs/haec/lib/python3.11/site-packages/torch/nn/modules/sparse.py\", line 162, in forward
def forward(self, input: Tensor) -> Tensor:
return F.embedding(
~~~~~~~~~~~ <--- HERE
input, self.weight, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
File \"/home/stanojevicd/miniforge3/envs/haec/lib/python3.11/site-packages/torch/nn/functional.py\", line 2233, in embedding
# remove once script supports set_grad_enabled
_no_grad_embedding_renorm_(weight, input, max_norm, norm_type)
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
~~~~~~~~~~~~~~~ <--- HERE
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
```
Given the final lines of the log, I thought it was a stochastic error, but I ran it again and got the same, so it seems consistent. Do you have any idea of what could be happening?
Hello there,
I am trying to
herrowith quite a big dataset (4 Gb plant genome, ~72x depth). I already did the AvA step, But now I am struggling in the inference step:The command I am using is:
But I am getting this error
Error log
```raw thread 'Given the final lines of the log, I thought it was a stochastic error, but I ran it again and got the same, so it seems consistent. Do you have any idea of what could be happening?
Thanks in advance.