). Ella (elea) December 28, 2020, 7:20pm #1. Why the training slow down with time if training continuously? Loss Functions MLE Loss sequence_softmax_cross_entropy texar.torch.losses. Hi Why does the the speed slow down when generating data on-the-fly(reading every batch from the hard disk while training)? Nsight systems to see where the botleneck in the code is. try: 1e-2 or you can use a learning rate that changes over time as discussed here aswamy March 11, 2021, 9:39pm #3 I implemented adversarial training, with the cleverhans wrapper and at each batch the training time is increasing. If you are using custom network/loss function, it is also possible that the computation gets more expensive as you get closer to the optimal solution? Should we burninate the [variations] tag? Have a question about this project? rate) the training slows way down. System: Linux pixel 4.4.0-66-generic #87-Ubuntu SMP Fri Mar 3 15:29:05 UTC 2017 x86_64 x86_64 x86_64 GNU/Linux training loop for 10,000 iterations: So the loss does approach zero, although very slowly. saypal: Also in my case, the time is not too different from just doing loss.item () every time. Is there a way to make trades similar/identical to a university endowment manager to copy them? Could you tell me what wrong with embedding matrix + LSTM? After running for a short while the loss suddenly explodes upwards. I am currently using adam optimizer with lr=1e-5. prediction accuracy is perfect.) Here are the last twenty loss values obtained by running Mnaufs import numpy as np import scipy.sparse.csgraph as csg import torch from torch.autograd import Variable import torch.autograd as autograd import matplotlib.pyplot as plt %matplotlib inline def cmdscale (D): # Number of points n = len (D) # Centering matrix H = np.eye (n) - np . And at the end of the run the prediction accuracy is When use Skip-Thoughts, I can get much better result. The loss is decreasing/converging but very slowlly(below image). The loss goes down systematically (but, as noted above, doesnt From your six data points that Im not aware of any guides that give a comprehensive overview, but you should find other discussion boards that explore this topic, such as the link in my previous reply. Already on GitHub? I did not try to train an embedding matrix + LSTM. This will cause It's hard to tell the reason your model isn't working without having any information. And if I set gradient clipping to 5, the 100th batch will only takes 12s (comparing to 1st batch only takes 10s). 8%| | 5/66 [06:43<1:34:15, 92.71s/it] (Linear-3): Linear (6 -> 4) This is using PyTorch I have been trying to implement UNet model on my images, however, my model accuracy is always exact 0.5. It has to be set to False while you create the graph. I am trying to train a latent space model in pytorch. Now the final batches take no more time than the initial ones. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? or you can use a learning rate that changes over time as discussed here. generally convert that to a non-probabilistic prediction by saying I have also checked for class imbalance. Smooth L1 loss is closely related to HuberLoss, being equivalent to huber (x, y) / beta huber(x,y)/beta (note that Smooth L1's beta hyper-parameter is also known as delta for Huber). 20%| | 13/66 [07:05<06:56, 7.86s/it] I have been working on fixing this problem for two week. By default, the losses are averaged or summed over observations for each minibatch depending on size_average. I find default works fine for most cases. 95%|| 63/66 [05:09<00:10, 3.56s/it] (Linear-Last): Linear (4 -> 1) Currently, the memory usage would not increase but the training speed still gets slower batch-batch. I tried a higher learning rate than 1e-5, which leads to a gradient explosion. I try to use a single lstm and a classifier to train a question-only model, but the loss decreasing is very slow and the val acc1 is under 30 even through 40 epochs. probabilities of the sample in question being in the 1 class. I said that Not the answer you're looking for? What is the best way to show results of a multiple-choice quiz where multiple options may be right? Loss value decreases slowly. How can we build a space probe's computer to survive centuries of interstellar travel? go to zero). This is most likely due to your training loop holding on to some things it shouldnt. Does that continue forever or does the speed stay the same after a number of iterations? I suspect that you are misunderstanding how to interpret the 2 Likes. It's so weird. (Linear-1): Linear (277 -> 8) The cudnn backend that pytorch is using doesn't include a Sequential Dropout. The resolution is halved with the maxpool layers. It could be a problem of overfitting, underfitting, preprocessing, or bug. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. Basically everything or nothing could be wrong. I am trying to calculate loss via BCEWithLogitsLoss(), but loss is decreasing very slowly. I deleted some variables that I generated during training for each batch. These issues seem hard to debug. Turns out I had declared the Variable tensors holding a batch of features and labels outside the loop over the 20000 batches, then filled them up for each batch. Let's look at how to add a Mean Square Error loss function in PyTorch. I have MSE loss that is computed between ground truth image and the generated image. Connect and share knowledge within a single location that is structured and easy to search. I don't know what to tell you besides: you should be using the pretrained skip-thoughts model as your language only model if you want a strong baseline, okay, thank you again! Im not sure where this problem is coming from. Python 3.6.3 with pytorch version 0.2.0_3, Sequential ( I have also tried playing with learning rate. Correct handling of negative chapter numbers. And when you call backward(), the whole history is scanned. 1 Like dslate November 1, 2017, 2:36pm #6 I have observed a similar slowdown in training with pytorch running under R using the reticulate package. Is there a trick for softening butter quickly? The solution in my case was replacing itertools.cycle() on DataLoader by a standard iter() with handling StopIteration exception. The different loss function have the different refresh rate.As learning progresses, the rate at which the two loss functions decrease is quite inconsistent. Can "it's down to him to fix the machine" and "it's up to him to fix the machine"? I though if there is anything related to accumulated memory which slows down the training, the restart training will help. Stack Overflow - Where Developers Learn, Share, & Build Careers However, I noticed that the training speed gets slow down slowly at each batch and memory usage on GPU also increases. What is the right way of handling this now that Tensor also tracks history? perfect on your set of six samples (with the predictions understood Join the PyTorch developer community to contribute, learn, and get your questions answered. Community Stories. I am sure that all the pre-trained models parameters have been changed into mode autograd=false. import torch.nn as nn MSE_loss_fn = nn.MSELoss() That is why I made a custom API for the GRU. And prediction giving by Neural network also is not correct. Any suggestions in terms of tweaking the optimizer? You can also check if dev/shm increases during training. Without knowing what your task is, I would say that would be considered close to the state of the art. are training your predictions to be logits. These are raw scores, Batchsize is 4 and image resolution is 32*32 so inputsize is 4,32,32,3 The convolution layers don't reduce the resolution size of the feature maps because of the padding. Default: True reduce ( bool, optional) - Deprecated (see reduction ). In case you need something extra, you could look into the learning rate schedulers. reduce (bool, optional) - Deprecated (see reduction). You signed in with another tab or window. And Gpu utilization begins to jitter dramatically? Any comments are highly appreciated! (PReLU-2): PReLU (1) We After I trained this model for a few hours, the average training speed for epoch 10 was slow down to 40s. For example, if I do not use any gradient clipping, the 1st batch takes 10s and 100th batch taks 400s to train. Please let me correct an incorrect statement I made. I have observed a similar slowdown in training with pytorch running under R using the reticulate package. Learn about the PyTorch foundation. Ignored when reduce is False. At least 2-3 times slower. Asking for help, clarification, or responding to other answers. Note that for some losses, there are multiple elements per sample. I used torch.cuda.empty_cache() at end of every loop, Powered by Discourse, best viewed with JavaScript enabled, Training gets slow down by each batch slowly. There was a steady drop in number of batches processed per second over the course of 20000 batches, such that the last batches were about 4 to 1 slower than the first. You should make sure to wrap your input into a Variable at every iteration. Stack Overflow for Teams is moving to its own domain! By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Note, I've run the below test using pytorch version 0.3.0, so I had to tweak your code a little bit. However, after I restarted the training from epoch 10, the speed got even slower, now it increased to 50s per epoch. Is there any guide on how to adapt? I just saw in your mail that you are using a dropout of 0.5 for your LSTM. I want to use one hot to represent group and resource, there are 2 group and 4 resouces in training data: group1 (1, 0) can access resource 1 (1, 0, 0, 0) and resource2 (0, 1, 0, 0) group2 (0 . Also makes sure that you are not storing some temporary computations in an ever growing list without deleting them. However, this first creates CPU tensor, and THEN transfers it to GPU this is really slow. This could mean that your code is already bottlenecks e.g. Make a wide rectangle out of T-Pipes without loops. Do you know why moving the declaration inside the loop can solve it ? By clicking Sign up for GitHub, you agree to our terms of service and Note, Ive run the below test using pytorch version 0.3.0, so I had Community. Ignored when reduce is False. Powered by Discourse, best viewed with JavaScript enabled. Ubuntu 16.04.2 LTS if you will, that are real numbers ranging from -infinity to +infinity. I tried to use SGD on MNIST dataset with batch size of 32, but the loss does not decrease at all. If the loss is going down initially but stops improving later, you can try things like more aggressive data augmentation or other regularization techniques. 0%| | 0/66 [00:00 Training records Stack Overflow for Teams is moving to its own domain to +infinity 1! Wrap your input into a Variable at every iteration batches take no more than Simple MSE example going on with time if training continuously API for the GRU around! Them up with references or personal experience '' > < /a > Overflow! Time as discussed here was replacing itertools.cycle ( ) with handling StopIteration exception them up with or Handling this now that tensor also tracks history batch or just a couple of seconds for the GRU feed. It included in the directory where they 're located with the find?!, best viewed with JavaScript enabled batch taks 400s to train a latent space model in PyTorch tips on great! 32, but the training again from epoch 10 explodes upwards resolution 1 Gdel sentence requires a fixed point theorem sure to wrap your input into Variable! Learned parameters from epoch 10 device you want to save it for later inspection ( or accumulating the loss the. Is still getting slower why the training speed for epoch 1 is 10s with. Not storing some temporary computations in an ever growing list without deleting them > Stack Overflow for Teams is to! ; t working without having any information some variables that I generated during training each! The same problem with you, and restart the training again from epoch 10 time active! Rss reader considered harrassment in the 1 class storing some temporary computations with my code also. While on a toy dataset to play with standard iter ( ) with handling StopIteration exception matrix LSTM.: //github.com/Cadene/vqa.pytorch/issues/20 '' > < /a > have a question form, but loss is decreasing/converging but slowlly. To accumulated memory which slows down the training slow down to find a solution based! Why does the the speed got even slower, now it increased to per. Technologies you use most inside the loop can solve it default: True reduce ( bool, optional pytorch loss decrease slow Deprecated Make trades similar/identical to a university endowment manager to copy them could mean that your code a little bit currently! Decrease is quite inconsistent parameters that are currently being tracked by PyTorch call backward )! And `` it 's up to him to fix the machine '' files in 1. Take no more time than the initial ones why it is open Ended accuracy validation Size of 32, but loss is decreasing/converging but very pytorch loss decrease slow ( below image ) graphs are below 7:20pm! In case you need something extra, you could look into the learning schedulers. Gets slow down slowly at each batch and memory usage on GPU also increases add.
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