Datasets:

foldl
/

torch-forum

title stringlengths 15 126	category stringclasses 3 values	posts list	answered bool 2 classes
About the Site Feedback category	Site Feedback	[ { "contents": "Discussion about this site, its organization, how it works, and how we can improve it.", "isAccepted": false, "likes": null, "poster": "system" }, { "contents": "Is it possible to have more categories other than those specified? Similar to tags on stackoverflow where people ca...	false
Pytorch is awesome!	null	[ { "contents": "Can’t wait for the open source release!", "isAccepted": false, "likes": 7, "poster": "ebetica" } ]	false
About the vision category	vision	[ { "contents": "Topics related to either pytorch/vision or vision research related topics", "isAccepted": false, "likes": 1, "poster": "smth" }, { "contents": "I created my model from scratch . I tried with batch_size 32 , it threw me this error. I tried with batch_size 1 , I got 320% accurac...	false
About the reinforcement-learning category	reinforcement-learning	[ { "contents": "A section to discuss RL implementations, research, problems", "isAccepted": false, "likes": 2, "poster": "smth" }, { "contents": "", "isAccepted": false, "likes": null, "poster": "lakehanne" }, { "contents": "I had the same problem, it only reached at 199 a...	false
Request: Tutorial for deploying on cloud based virtual machine	null	[ { "contents": "I don’t have a GPU computer. Is there a tutorial or best practices for using PyTorch on a cloud-based virtual machine with GPU capabilities?", "isAccepted": false, "likes": 1, "poster": "rcmckee" }, { "contents": "PyTorch does not NEED GPUs to function. It works great on CPUs ...	false
Convert/import Torch model to PyTorch	null	[ { "contents": "Hi, Great library! I’d like to ask if it is possible to import a trained Torch model to PyTorch… Thanks", "isAccepted": false, "likes": 4, "poster": "miliadis" }, { "contents": "As of today, you can deserialize Lua’s .t7 files into PyTorch containing Tensors, numbers, tables, ...	false
Roadmap for torch and pytorch	null	[ { "contents": "why did you choose to create this interface for python now, it did not seem a priority for the community (however I understand this will probably increase the adoption rate) ? as a torch user, I invested some time to learn how lua and torch operate, I guess it will operate the same way in the nea...	false
But what about Hugh Perkins?	null	[ { "contents": "", "isAccepted": false, "likes": null, "poster": "Jeremy_Godenz" }, { "contents": "", "isAccepted": false, "likes": null, "poster": "smth" } ]	false
CFFI for interfacing C functions	null	[ { "contents": "There are many benefits of CFFI, just to mention few: Abstract the Python version (CPython2, CPython3, PyPy). Better control over when and why the C compilation occurs, and more standard ways to write setuptools-based setup.py 3 files. Keep all the Python-related logic in Python so that you don’t...	false
Import/Export models	null	[ { "contents": "Meanwhile, do you have already any internal data structure to read the models definition?", "isAccepted": false, "likes": 2, "poster": "edgarriba" }, { "contents": "We’re open to discussion about formats for sharing parameters and model definitions between libraries. If there’...	false
Is new serialization format documented?	null	[ { "contents": "", "isAccepted": false, "likes": null, "poster": "jsenellart-systran" }, { "contents": "I can write up the specs, but they’re much more complicated now. Serialized files are actually tars containing 4 other files - one listing tensors, one listing storages, one with storage da...	false
Does it support Multi-GPU card on a single node?	null	[ { "contents": "Hi, Now I am using keras with a simplified interface of data parallisim sync version of parallelism. Now I wish to know if pytorch has similar function, seems like PyTorch is trying to advocate in “Speed”, so it had better support Multi-GPU just for a single node.", "isAccepted": false, "...	false
Import nn module written in Lua?	null	[ { "contents": "Thanks", "isAccepted": false, "likes": null, "poster": "zzz" }, { "contents": "", "isAccepted": false, "likes": 2, "poster": "smth" }, { "contents": "", "isAccepted": false, "likes": null, "poster": "Eugenio_Culurciello" }, { "contents":...	false
2017 and we still can’t support AMD hardware, why?	null	[ { "contents": "Not all of us own NV hardware. Some of us want to use these tools at home without having to make a new purchase or purchase some AWS time. Not all of us are research scientists with fat grants allowing us to buy the latest NV hardware. When will the vendor lock-in stop? Surely “free” tools should...	false
Package manager choice: conda vs. pip	null	[ { "contents": "", "isAccepted": false, "likes": null, "poster": "Atcold" }, { "contents": "This is because conda manages dependencies that are not just python dependencies. For example with conda, we get tighter control over which BLAS is installed.", "isAccepted": false, "likes": 3,...	false
How to do backward() for a net with multiple outputs?	null	[ { "contents": "e.g., in Torch 7 I have a net with the last module an nn.ConcatTable, then I make the gradOutputs a table of tensors and do the net:backward(inputs, gradOutputs) How to do similar things with pytorch? I tried to backward() for each output, but it complained that backward should not be called mult...	false
Why cant I see .grad of an intermediate variable?	null	[ { "contents": "Hi! I am loving this framework… Since Im a noob, I am probably not getting something, but I am wondering why I cant get the gradient of an intermediate variable with .grad? Here is an example of what I mean: <SCODE>xx = Variable(torch.randn(1,1), requires_grad = True)\nyy = 3xx\nzz = yy*2\nzz.b...	false
Input preparation	null	[ { "contents": "Hello all, For instance, the fallowing code results in error: <SCODE>(class model above)\n self.affine1(10, 100)\n(...)\n\nx = np.random.random(10)\nipt = torch.from_numpy(x)\nprobs = model(Variable(ipt))\n<ECODE> Then <SCODE>TypeError: addmm_ received an invalid combination of arguments - got...	false
Help core dumped problem!	null	[ { "contents": "", "isAccepted": false, "likes": 1, "poster": "Chun_Li" }, { "contents": "Thank you. It seems to be specific to AMD Athlon CPU. I will take a look.", "isAccepted": false, "likes": null, "poster": "smth" }, { "contents": "Thanks for the response. BTW, I forg...	false
Model.cuda() takes long time	null	[ { "contents": "Even for a small model, calling its cuda() method takes minutes to finish. Is it normal? If yes what it does behind the scene that takes so long? Thanks!", "isAccepted": false, "likes": 4, "poster": "ming" }, { "contents": "We’re aware that there are problems with binary packa...	false
How to transform Variable into numpy?	null	[ { "contents": "When I used pytorch, I met a problem that I can not transform Variable into numpy. When I try to use torch.Tensor or transform it into torch.FloatTensor, but it’s fail. So how can I to solve this problem?", "isAccepted": false, "likes": 6, "poster": "cumttang" }, { "contents":...	false
What’s the easiest way to clean gradients before saving a net?	null	[ { "contents": "<SCODE>torch.save(net.state_dict(), 'net_name.pth')\n<ECODE> right?", "isAccepted": false, "likes": null, "poster": "pengsun" }, { "contents": "<SCODE>torch.save(net.state_dict(), 'net_name.pth')\n<ECODE>", "isAccepted": false, "likes": null, "poster": "fmassa" }...	false
Load a saved model	null	[ { "contents": "and how do you forward with this model file?", "isAccepted": false, "likes": 1, "poster": "Eugenio_Culurciello" }, { "contents": "<SCODE>x = torch.rand(1, 3, 224, 224)\nxVar = torch.autograd.Variable(x)\nres18(xVar)\n\n--> \n\nVariable containing:\n-0.4374 -0.3994 -0.5249 ......	false
Copying nn.Modules without shared memory	null	[ { "contents": "", "isAccepted": false, "likes": 4, "poster": "lenny" }, { "contents": "no, what you need to do is to send them model to the new process, and then do: <SCODE>import copy\nmodel = copy.deepcopy(model)\n<ECODE> share_memory() only shares the memory ahead of time (in case you wan...	false
Why passing in tensor instead of Variable when backward?	null	[ { "contents": "Hi, I just realized Variable.grad field is also a Variable, it thus seems more natural to pass gradients as a Variable when doing backward (currently it must be a tensor). Any reason for such an interface?", "isAccepted": false, "likes": 1, "poster": "pengsun" }, { "contents":...	false
Why cant I change the padding, and strides in Conv2d?	null	[ { "contents": "Hi, perhaps I am missing something, but I cannot seem to figure out how to change the padding and stride amounts in the Conv2d method. Thanks", "isAccepted": false, "likes": null, "poster": "Kalamaya" }, { "contents": "You can pass them as arguments to the Module constructor e...	false
Asynchronous parameters updating?	reinforcement-learning	[ { "contents": "<SCODE>-- in main thread: shared parameters\nparams, _ = sharedNet:getParameters()\n\n-- in worker thread: its own gradParameters\ntNet = sharedNet:clone()\n_, gradParams = tNet:getParameters()\n\n-- in worker thread: stuff\n\n-- in worker thread: updating shared parameters with its own gradParam...	false
How to extract features of an image from a trained model	null	[ { "contents": "Hi all,", "isAccepted": false, "likes": 19, "poster": "big_tree" }, { "contents": "<SCODE>torchvision.models.resnet18(pretrained=True)\n<ECODE>", "isAccepted": false, "likes": 7, "poster": "apaszke" }, { "contents": "You can either reconstruct the classifie...	false
Broadcasting? Or alternative solutions	null	[ { "contents": "Hi, I really like the library so far. However, I was wondering if broadcasting is on the roadmap, and what would be your current suggestion for work-arounds? <SCODE>x = Variable(torch.Tensor([[1.0, 1.0], \n [2.0, 2.0], \n [3.0, 3.0], \n ...	false
Understanding how torch.nn.Module works	null	[ { "contents": "Essentially, I want to reproduce the results I get when I do it “manually:” <SCODE>from torch.autograd import Variable\nimport torch\n\n\nx = Variable(torch.Tensor([[1.0, 1.0], \n [1.0, 2.1], \n [1.0, 3.6], \n [1.0, 4.2...	false
Concatenate two tensor for each timestep	null	[ { "contents": "<SCODE>def forward(...):\n ...\n z = Variable(torch.zeros(y.size(0), y.size(1), 1024))\n for i in range(z.size(0)):\n z[i] = torch.cat((y[i], x), 1)\n\n z = self.decoderGRU(z, init_decoder_GRU)\n decoded = self.classLinear(z.view(z.size(0)*z.size(1), ...	false
Model zoo categories	null	[ { "contents": "", "isAccepted": false, "likes": null, "poster": "Eugenio_Culurciello" }, { "contents": "i’m not sure i understand your question", "isAccepted": false, "likes": null, "poster": "smth" }, { "contents": "the list of category names that correspond to each of t...	false
Are tables like concattable or paralleltable present in torch	null	[ { "contents": "Are there utilities like concattable, paralleltable in pytorch??", "isAccepted": false, "likes": 1, "poster": "gsp-27" }, { "contents": "No, but you can easily achieve that using autograd. e.g. for concat table: <SCODE>class MyModel(nn.Module):\n def __init__(self):\n ...	false
Non-legacy View module?	null	[ { "contents": "There doesn’t currently appear to be any non-legacy View module in pytorch’s torch.nn module. Any reason for this? While obviously not essential, it’s convenient when porting over existing Torch networks. Would be happy to submit a PR.", "isAccepted": false, "likes": 1, "poster": "leb...	false
“wheel not supported” for pip install	null	[ { "contents": "", "isAccepted": false, "likes": null, "poster": "eulerreich" }, { "contents": "Not sure if that’s a problem in your case, but I had a similar issue (or a similar message) when I accidentally tried to install a package into a wrong environment (a py 3.5 wheel into a 3.6 env). ...	false
JoinTable like operation when combining CNNs	null	[ { "contents": "<SCODE>class Net(nn.Module):\ndef __init__(self):\n super(Net, self).__init__()\n self.embed = nn.Embedding(vocab.size(), 300)\n #self.embed.weight = Parameter( torch.from_numpy(vocab.get_weights().astype(np.float32))) \n self.conv_3 = nn.Conv2d(1, 50, kernel_size=(3, 300),stri...	false
Inferring shape via flatten operator	null	[ { "contents": "Is there a flatten-like operator to calculate the shape of a layer output. An example would be transitioning from a conv layer to linear layer. In all the examples I’ve seen thus far this seems to be manually calculated, ex: <SCODE>class Net(nn.Module):\n def __init__(self):\n super(Net...	true
Simple L2 regularization?	null	[ { "contents": "Hi, does simple L2 / L1 regularization exist in pyTorch? I did not see anything like that in the losses. I guess the way we could do it is simply have the data_loss + reg_loss computed, (I guess using nn.MSEloss for the L2), but is an explicit way we can use it without doing it this way? Thanks",...	false
Problems with target arrays of int (int32) types in loss functions	null	[ { "contents": "(On a side note, would you recommend using doubles and longs over floats and ints performance-wise?) <SCODE>TypeError: FloatClassNLLCriterion_updateOutput \nreceived an invalid combination of arguments - \ngot (int, torch.FloatTensor, torch.IntTensor, torch.FloatTensor, bool, NoneType, torch.Floa...	false
Why use nn.Conv2D over nn.functional.conv2d?	null	[ { "contents": "Hi, I stumbled upon this question because I am trying to have control over how my convolutional weights are initialized. The API for both of those however seems different. For the former: torch.nn.functional.conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) For the latte...	false
Are GPU and CPU random seeds independent?	null	[ { "contents": "<SCODE>torch.manual_seed(args.seed)\nif args.cuda:\n torch.cuda.manual_seed(args.seed)\n<ECODE> torch.manual_seed(args.seed) sufficient?", "isAccepted": false, "likes": 3, "poster": "rasbt" }, { "contents": "It is sufficient for CPU determinism, but it won’t affect the GPU ...	false
Equivalent of np.reshape() in pyTorch?	null	[ { "contents": "", "isAccepted": false, "likes": 4, "poster": "Kalamaya" }, { "contents": "<SCODE>>>> import torch\n>>> t = torch.ones((2, 3, 4))\n>>> t.size()\ntorch.Size([2, 3, 4])\n>>> t.view(-1, 12).size()\ntorch.Size([2, 12])<ECODE>", "isAccepted": false, "likes": 12, "poster...	false
Visual watcher when training/evaluating or tensorboard equivalence?	null	[ { "contents": "Hi, As leveraging python ecosystem seems one of the main reasons for switching to pytorch from Torch 7, would you provide guidance to visually overseeing training/evaluating with 3rd party Python libraries that is hard to do in pure Torch 7 ? For example,", "isAccepted": false, "likes": n...	false
Variable Can not compare with other parameter?	null	[ { "contents": "When I try to find out some Variable result who is the biggest. However, I found that “Variable” can not compare with other parameters, such as ndarray or FloatTensor. Thus, is there any thing wrong with Variable, or what should I do?", "isAccepted": false, "likes": null, "poster": "c...	false
Parallelism via CPU	null	[ { "contents": "<SCODE>sess = tf.Session(config=tf.ConfigProto(\n intra_op_parallelism_threads=NUM_THREADS))<ECODE>", "isAccepted": false, "likes": null, "poster": "rasbt" }, { "contents": "Hi,", "isAccepted": false, "likes": 2, "poster": "fmassa" }, { "contents": "Sounds...	false
Cannot install via pip on LINUX?	null	[ { "contents": "I am getting the following error when I try to install on my 64-bit Ubuntu 14.04 LTS system. The command I run is: And the error I get is: After that it says: For additional context, I have made a virtual environment (called pytorch) first, and then within that environment, I tried to run the ins...	false
Weight initilzation	null	[ { "contents": "How I can initialize variables, say Xavier initialization?", "isAccepted": false, "likes": 23, "poster": "Hamid" }, { "contents": "", "isAccepted": false, "likes": 6, "poster": "Atcold" }, { "contents": "", "isAccepted": false, "likes": 12, "pos...	false
Cuda() failed with “The NVIDIA driver on your system is too old”	null	[ { "contents": "", "isAccepted": false, "likes": null, "poster": "rohjunha" }, { "contents": "That actually sounds like a docker issue. When PyTorch runs, we check with the CUDA driver API if the driver is sufficient, and we report back what it says… Are you using NVIDIA-docker? because you n...	false
High memory usage while training	null	[ { "contents": "<SCODE>class testNet(nn.Module):\n def __init__(self):\n super(testNet, self).__init__()\n self.rnn = nn.RNN(input_size=200, hidden_size=1000, num_layers=1)\n self.linear = nn.Linear(1000, 100)\n\n def forward(self, x, init):\n x = self.rnn(x, init)[0]\n y...	false
Train Neural network problem in reinforcement learning	null	[ { "contents": "<SCODE>self.value_optimizer.zero_grad()\npreValue = self.value_net(state).data\nnextValue = self.value_net(next_state).data\nexpectedValue = (self.gamma * nextValue) + reward\n\npreValue = Variable(preValue)\nexpectedValue = Variable(expectedValue)\nloss = F.smooth_l1_loss(preValue, expectedValue...	false
How to install PyTorch such that can use GPUs	null	[ { "contents": "EDIT: I use net.cuda() and this seems to work. However when I try to run my script I get this error: (If I do not try to run net.cuda() my script works find on the CPU).", "isAccepted": false, "likes": null, "poster": "Kalamaya" }, { "contents": "", "isAccepted": false, ...	false
Serving Model Trained with PyTorch	null	[ { "contents": "Tensorflow has Tensorflow Serving. I know pytorch is a framework in its early stages, but how do people serve models trained with pytorch. Must it be from Python? I’m specifically looking to serve from C++.", "isAccepted": false, "likes": 4, "poster": "abc" }, { "contents": "W...	false
Check `gradInput`s	null	[ { "contents": "<SCODE>net = Net()\nh = net(x)\nJ = loss(h, y)\nJ.backward()\n<ECODE>", "isAccepted": false, "likes": null, "poster": "Atcold" }, { "contents": "", "isAccepted": false, "likes": 1, "poster": "apaszke" }, { "contents": "", "isAccepted": false, "likes...	false
Sharing optimizer between processes	null	[ { "contents": "I am wondering if it is possible to share optimizer between different threads. To be specific, when optimizer.step() is applied, modified state of the optimizer should be available for all processes.", "isAccepted": false, "likes": null, "poster": "delgado" }, { "contents": "i...	false
RuntimeError: expected a Variable argument, but got function	null	[ { "contents": "Hello, I get this weird error while running my code. <SCODE>class Residual(nn.Module):\n\tdef __init__(self,dropout, shape, negative_slope, BNflag = False):\n\t\tsuper(Residual, self).__init__()\n\t\tself.dropout = dropout\n\t\tself.linear1 = nn.Linear(shape[0],shape[1])\n\t\tself.linear2 = nn.Li...	false
How to check if Model is on cuda	null	[ { "contents": "", "isAccepted": true, "likes": 11, "poster": "napsternxg" }, { "contents": "As replied on the github issues, an easy way is: <SCODE>next(model.parameters()).is_cuda # returns a boolean\n<ECODE>", "isAccepted": true, "likes": 56, "poster": "smth" }, { "cont...	true
Dropout functional API, advantages/disadvantages?	null	[ { "contents": "I saw in one of the examples that the functional API was used to implement dropout for a conv layer but not for the fully connected layer. Was wondering if this has a specific reason? I pasted the code below <SCODE>class Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__(...	false
RNN for sequence prediction	null	[ { "contents": "Hello, Previously I used keras for CNN and so I am a newbie on both PyTorch and RNN. In keras you can write a script for an RNN for sequence prediction like, <SCODE>in_out_neurons = 1\nhidden_neurons = 300\n\nmodel = Sequential() \nmodel.add(LSTM(hidden_neurons, batch_input_shape=(None, length_o...	false
How to get the graph of your DNN drawn?	null	[ { "contents": "Hi all, I am wondering if there is an easy plug-and-play way in order for me to have a nice visualization of my DNN that I have designed? Im after something simple to be honest, just something where I can see the various layers connected to each other to get a sense of what I have coded. Thanks",...	false
Why is input used as var name in examples and pytorch convention!	null	[ { "contents": "A discussion is needed for adopting common python specific convention to pytorch.", "isAccepted": false, "likes": null, "poster": "napsternxg" }, { "contents": "I know that many people will consider that a “bad practice”, but I seriously don’t think that giving up readability ...	false
Data set loader questions	null	[ { "contents": "I have downloaded and split the CIFAR 10 data using the given torch.datasets api, now I want to separate out training and validate data, say I want out 50000 sample 49000 as training example and 1000 as validation examples, how do I achieve this?? Also say I keep the batch size as 64, then in the...	false
Using previous output values as part of a loss function	reinforcement-learning	[ { "contents": "I am trying to implement TRPO, and I need the gradient of the network parameters w.r.t. the KL divergence between the current action distribution and the action distribution after the parameters are changed. What is the best way to implement this? How do I make sure action_distribution0 doesn’t g...	false
Proper way to do gradient clipping?	null	[ { "contents": "Is there a proper way to do gradient clipping, for example, with Adam? It seems like that the value of Variable.data.grad should be manipulated (clipped) before calling optimizer.step() method. I think the value of Variable.data.grad can be modified in-place to do gradient clipping. Is it safe to...	false
Text classification unexpectedly slow	null	[ { "contents": "I wrote a simple demo for short text classification but the speed is unexpectedly slow. When I tried to find out where the bottleneck is, it turns out to be intractable. At first, the bottleneck is this line: However, after commenting out the above line, it slows down at these lines in get_batch(...	false
Usage of Numpy and gradients	null	[ { "contents": "Hi, I was wondering if gradients are automatically computed if Numpy is in the Forward() function of nn.module, i.e, torch tensors are converted to numpy arrays, a numpy op is applied and then we convert it back to torch tensors. Is there any implication of doing so? Thanks!", "isAccepted": f...	false
Matrix-vector multiply (handling batched data)	null	[ { "contents": "<SCODE># (batch x inp)\nv = torch.randn(5, 15)\n# (inp x output)\nM = torch.randn(15, 20)\n<ECODE> Compute: <SCODE># (batch x output)\nout = torch.Tensor(5, 20)\nfor i, batch_v in enumerate(v):\n out[i] = (batch_v * M).t()\n<ECODE>", "isAccepted": false, "likes": null, "poster": "e...	false
Second order derivatives?	null	[ { "contents": "and I need the gradients of the network parameters w.r.t (flat_grad * x) In the process of flattening the gradients, I had to convert everything into a numpy array, which broke the backprop chain. How can I solve this problem?", "isAccepted": false, "likes": null, "poster": "yoonholee...	false
Implementing Squeezenet	null	[ { "contents": "Hi, I am trying to implement Squeezenet and train it on Cifar10 data, I have got the code ready but there seems to be some problem, my training set accuracy never increases though the loss function graph makes sense. <SCODE>class fire(nn.Module):\ndef __init__(self, inplanes, squeeze_planes, expa...	false
Different results with same input	null	[ { "contents": "Hi, I have this output : EDIT : I have the same problem if I do two forward pass with a batch of 1", "isAccepted": false, "likes": null, "poster": "maxgreat" }, { "contents": "", "isAccepted": false, "likes": 3, "poster": "apaszke" } ]	false
Titan X Maxwell (old) issues	null	[ { "contents": "I can confirm we can run Torch7 on those machines with no issues. Also on the new Pascal Titan X, we do not have this problem. Maybe an issues with driver? Anyone else has the same issue?", "isAccepted": false, "likes": null, "poster": "Eugenio_Culurciello" }, { "contents": "W...	false
Example on how to use batch-norm?	null	[ { "contents": "TLDR: What exact size should I give the batch_norm layer here if I want to apply it to a CNN? output? In what format? I have a two-fold question: Is this the correct intended usage? Maybe an example of the syntax for it’s usage with a CNN? Thanks.", "isAccepted": false, "likes": 4, "p...	false
Adding a scalar?	null	[ { "contents": "Dumb question, but how do I make a scalar Variable? I’d like to add a trainable parameter to a vector, but I keep getting size-mismatch problems. <SCODE># Works, but I can't make the 2 a \n# parameter, so I can't do gradient descent on it\nVariable(torch.from_numpy(np.ones(5))) + 2 \n<ECODE> Than...	true
List of nn.Module in a nn.Module	null	[ { "contents": "<SCODE>class testNet(nn.Module):\n def __init__(self, input_dim, hidden_dim, step=1):\n super(testNet, self).__init__()\n self.linear = nn.Linear(100, 100) #dummy module\n self.linear_combines1 = []\n self.linear_combines2 = []\n for i in range(step):\n ...	false
When will the conda package be updated?	null	[ { "contents": "I found some features like upsampling_nearest are in the github but not in the conda package. Is there a timeline when the conda package will be updated?", "isAccepted": false, "likes": null, "poster": "junonia" }, { "contents": "the conda package was updated yesterday eveni...	false
Read data from the GPU?	null	[ { "contents": "Thanks!", "isAccepted": false, "likes": null, "poster": "Kalamaya" }, { "contents": "", "isAccepted": false, "likes": 1, "poster": "tomsercu" } ]	false
Understanding graphs and state	null	[ { "contents": "There are two questions remaining - the second question is more important. 1) No guarantee that second backward will fail? <SCODE>x = Variable(torch.ones(2,3), requires_grad=True)\ny = x.mean(dim=1).squeeze() + 3 # size (2,)\nz = y.pow(2).mean() # size 1\ny.backward(torch.ones(2))\nz.backward() #...	false
Help clarifying repackage_hidden in word_language_model	null	[ { "contents": "<SCODE>def repackage_hidden(h):\n \"\"\"Wraps hidden states in new Variables, to detach them from their history.\"\"\"\n if type(h) == Variable:\n return Variable(h.data)\n else:\n return tuple(repackage_hidden(v) for v in h)\n<ECODE> I dont think I fully understand what th...	false
CUDA out of memory error	null	[ { "contents": "I ran my net with a large minibatch on the GPU with problem, and then ctrl-c out of it. However when I try to re-run my script I get: RuntimeError: cuda runtime error (2) : out of memory at /home/soumith/local/builder/wheel/pytorch-src/torch/lib/THC/generic/THCStorage.cu:66 Nothing changed and it...	false

End of preview. Expand in Data Studio

Dataset Card for "torch-forum"

Dataset structure

{
    title:str
    category:str,
    posts:List[{
                poster:str,
                contents:str,
                likes:int,
                isAccepted:bool
               }]
}

Downloads last month: 19

Size of downloaded dataset files:

734 kB

Size of the auto-converted Parquet files:

734 kB

Number of rows:

706