After hours of search and restarting the whole system to reset the gpu like 20 times

I finally found that its very easy to fix just by replacing it with `nn.BCEWithLogitsLoss()`

After hours of search and restarting the whole system to reset the gpu like 20 times

I finally found that its very easy to fix just by replacing it with `nn.BCEWithLogitsLoss()`

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are you just replacing `m.crit = F.binary_cross_entropy`

with `m.crit = nn.BCEWithLogitsLoss()`

before predicting?

Yes, that is because with `F.binary_cross_entropy`

, the output of the model (i.e. final layer) does not have a `Sigmoid()`

layer, hence why the GPU crash, most likely due to the negative predictions in the ouput (as @hiromi pointed this out in the Cuda runtime error (59) post).

WIth `nn.BCEWithLogitsLoss()`

the `Sigmoid()`

layer is added before computing the binary cross entropy loss (see doc nn.BCEWithLogitsLoss())

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When I had `F.binary_cross_entropy`

I did use `Sigmoid()`

. But that could have been a problem for others I guess.

Same here. According to the docs, the `nn.BCEWithLogitsLoss()`

“is more numerically stable than using a plain `Sigmoid`

followed by a `BCELoss`

as, by combining the operations into one layer, we take advantage of the log-sum-exp trick for numerical stability”

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Cool insight!

Could you tell me please where you are replacing this line? I am trying to apply the notebook from Lesson 1 to a new data set and it keeps crashing. I am just following the steps and get `cuda runtime error (59)`

. My photos are all ok, I have no idea why this is happening.

Lesson 1 doesn’t use `F.binary_cross_entropy`

. It uses `F.nll_loss`

. You can check what the criterion is by printing `learn.crit`

. You have a different problem.

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I switched to `nn.BCEWithLogitsLoss()`

and checked with `m.crit`

that it switched over. Still getting `cuda runtime rror (59)`

when I try to run `m.predict(True)`

. the model dataloader is as follows (it’s essentially the rossman one):

`md = ColumnarModelData.from_data_frame(PATH, val_idx, df, y.astype(float32), cat_flds=cur_cats, bs=128, test_df=df_test)`

and the learner:

`m = md.get_learner(emb_szs, len(df.columns)-len(cur_cats), 0.04, 1, [1000,500], [0.001,0.01], y_range=y_range)`

I have `y_range`

set to a tuple of (0,1), not sure if that’s correct or if that is the problem or what the heck is going on.

Maybe your batch size is too large?

Also what is the exact error message?

I had errors regarding float datatype. I replaced **np.float32** with **np.float** in many places and got rid of all errors. Maybe this trick helps you as well

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Batch size is fine, I have plenty of memory left so I don’t think it’s that. The exact error is:

RuntimeError Traceback (most recent call last)

in ()

----> 1 pred_test=m.predict(True)

~/fastai/courses/dl1/fastai/learner.py in predict(self, is_test, use_swa)

355 dl = self.data.test_dl if is_test else self.data.val_dl

356 m = self.swa_model if use_swa else self.model

–> 357 return predict(m, dl)

358

359 def predict_with_targs(self, is_test=False, use_swa=False):

~/fastai/courses/dl1/fastai/model.py in predict(m, dl)

227

228 def predict(m, dl):

–> 229 preda,_ = predict_with_targs_(m, dl)

230 return np.concatenate(preda)

231

~/fastai/courses/dl1/fastai/model.py in predict_with_targs_(m, dl)

239 if hasattr(m, ‘reset’): m.reset()

240 res = []

–> 241 for *x,y in iter(dl): res.append([get_prediction(to_np(m(*VV(x)))),to_np(y)])

242 return zip(*res)

243

~/miniconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in **call**(self, *input, **kwargs)

355 result = self._slow_forward(*input, **kwargs)

356 else:

–> 357 result = self.forward(*input, **kwargs)

358 for hook in self._forward_hooks.values():

359 hook_result = hook(self, input, result)

~/fastai/courses/dl1/fastai/column_data.py in forward(self, x_cat, x_cont)

112 def forward(self, x_cat, x_cont):

113 if self.n_emb != 0:

–> 114 x = [e(x_cat[:,i]) for i,e in enumerate(self.embs)]

115 x = torch.cat(x, 1)

116 x = self.emb_drop(x)

~/fastai/courses/dl1/fastai/column_data.py in (.0)

112 def forward(self, x_cat, x_cont):

113 if self.n_emb != 0:

–> 114 x = [e(x_cat[:,i]) for i,e in enumerate(self.embs)]

115 x = torch.cat(x, 1)

116 x = self.emb_drop(x)

~/miniconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in **call**(self, *input, **kwargs)

355 result = self._slow_forward(*input, **kwargs)

356 else:

–> 357 result = self.forward(*input, **kwargs)

358 for hook in self._forward_hooks.values():

359 hook_result = hook(self, input, result)

~/miniconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/sparse.py in forward(self, input)

101 input, self.weight,

102 padding_idx, self.max_norm, self.norm_type,

–> 103 self.scale_grad_by_freq, self.sparse

104 )

105

~/miniconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/_functions/thnn/sparse.py in forward(cls, ctx, indices, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)

45

46 if not indices.is_contiguous():

—> 47 ctx._indices = indices.contiguous()

48 indices = ctx._indices

49 else:

RuntimeError: cuda runtime error (59) : device-side assert triggered at /opt/conda/conda-bld/pytorch_1518244421288/work/torch/lib/THC/THCTensorCopy.cu:204

So at this point I’m pretty sure it has something to do with indices (of something) and then somehow not being contiguous.

what is your pytorch version? 0.3.1 works best, 0.4 should also, maybe but more recent ones not so sure…

I have a similar problem (see here: Structured Learner) but so far I could not find a good hint on what is wrong.

I checked my categorical variables and found a mistake (index was not set) that I corrected.

Also reducing the bs did not help (I also thought maybe I’m out of gpu ram and pytorch was not able to load it properly).

I use `is_multi=False`

because I want to do 1/0 classification.

With this setup my final layer looks like this:

A.) output of `learn.model`

:

```
(outp): Linear(in_features=500, out_features=1, bias=True)
(emb_drop): Dropout(p=0.4)
(drops): ModuleList(
(0): Dropout(p=0.001)
(1): Dropout(p=0.01)
```

B.) output of `learn.crit`

:

`<function torch.nn.functional.nll_loss(input, target, weight=None, size_average=True, ignore_index=-100, reduce=True)>`

Has somebody an idea where I should look for the error?

One thing I found out about " device-side assert triggered " is to look at the console where you started jupyter notebook, there will be a much more detailed message, e.g.:

In this example, the messages are Indicating that some tensors are of incompatible size (correction: actually, the error message says that the values inside the tensor should be between 0.0 and 1.0). Since it also includes the shapes of the tensors, it makes it easier to track them down in your code.

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Hello @gai,

thank you for the hint!

Now I have the full error message:

```
THCudaCheck FAIL file=/opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/THC/THCTensorCopy.cu line=204 error=59 : device-side assert triggered
/opt/conda/conda-bld/pytorch_1525909934016/work/aten/src/THC/THCTensorIndex.cu:360: void indexSelectLargeIndex(TensorInfo<T, IndexType>, TensorInfo<T, IndexType>, TensorInfo<long, IndexType>, int, int, IndexType, IndexType, long) [with T = float, IndexType = unsigned int, DstDim = 1, SrcDim = 1, IdxDim = -2, IndexIsMajor = true]: block: [0,0,0], thread: [46,0,0] Assertion `srcIndex < srcSelectDimSize` failed.
```

This seems very similar to the error in this post Structured Learner which was not solved yet.

From the error description it looks like a dimension mismatch. I guess I have to check my df setup again.

I will keep digging deeper!