I want to train a network with mBART model in google colab , but I got the message of RuntimeError: CUDA out of memory. Tried to allocate 886.00 MiB (GPU 0; 15.90 GiB total capacity; 13.32 GiB already allocated; 809.75 MiB free; 14.30 GiB reserved in total by PyTorch) I subscribed with GPU in colab. I tried to use 128 or 64 for The maximum total input sequence length. Kindly, What can I do to fix the problem?

i’m fine tuning the wav2vec-xlsr model. i’ve created a virtual env for that and i’ve installed cuda 11.0 and tensorflow-gpu==2.5.0 but it gives the following error : ValueError: Mixed precision training with AMP or APEX (--fp16 or --bf16) and half precision evaluation (--fp16_full_eval or --bf16_full_eval) can only be used on CUDA devices. i want to fine tune the model on GPU ANY HELP ?

I cannot find the visual studio 2019 version and every time I try to install CUDA 11.2.2 on my laptop, It warns me about not that I haven't installed Visual Studio. I've tried installing the C++ add-ons (Mobile and Desktop development for C++) but it still warns me about the same thing. Please suggest me a way! P.S I'm trying to install CUDA for tensorflow. Thanks in advance for your help!

I have a user with two GPU's; the first one is AMD which can't run CUDA, and the second one is a cuda-capable NVIDIA GPU. I am using the code model.half().to("cuda:0"). I'm not sure if the invocation successfully used the GPU, nor am I able to test it because I don't have any spare computer with more than 1 GPU lying around. In this case, does "cuda:0" mean the first device which can run CUDA, so it would've worked even …

I've managed to build Tensorflow 2.3 from source, following these instructions: https://towardsdatascience.com/how-to-compile-tensorflow-2-3-with-cuda-11-1-8cbecffcb8d3 But, when I install obtained pip package in new conda environment, and import tensorflow, I get following error: Could not load dynamic library 'libcudart.so.11.1'; dlerror: libcudart.so.11.1: cannot open shared object file: No such file or directory I've managed to use GPU support with CUDA 11.1 for Tensorflow 2.5 nightly, without creating soft links between libs (I get Successfully opened dynamic library libcudart.so.11.0 message). Any help appreciated.

I have been trying to understand the convolution lowering operation shown in the cuDNN paper. I was able to understand most of it by reading through and mapping various parameters to the image below. However, I am unable to understand how the original input data (NCHW) was converted into the Dm matrix shown in red. The ordering of the elements of the Dm matrix does not make sense. Can someone please explain this?

I am working with a GTX3070, which only has 8GB of GPU RAM. When I am running using trainer.train(), I run fine with a maximum batch size of 7 (6 if running in Jupiter notebook). However, when I attempt to run in a hyperparameter search with ray, I get CUDA out of memory every single time. I am wondering why this could be the case. Here is my code. Sorry if it’s a little long. It’s based off the following …

As a data scientist, I am always looking for ways to improve my workflows. I am familiar with Intel's sklearn acceleration module, Intelex. While this has sped up my algorithms between 3-70x based on the algorithm, it is only applicable to 22 sklearn algorithms/functions. I have seen cuML which accelerates on a CUDA GPU, but again there a only a handful of algorithms that are accelerated. Are there any other libraries that can accelerate sklearn or be the same function …

I installed spacy 3 in a venv and tried to execute: spacy.require_gpu() Then I got this as output: >>> spacy.require_gpu() Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/user/.virtualenvs/spacy3/lib/python3.8/site-packages/thinc/util.py", line 187, in require_gpu raise ValueError("GPU is not accessible. Was the library installed correctly?") ValueError: GPU is not accessible. Was the library installed correctly? How can I get rid of this? Im using: nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 450.119.04 Driver Version: 450.119.04 CUDA Version: 11.0 | |-------------------------------+----------------------+----------------------+ | …

I am using Tensorflow to perform inference on a dataset on Ubuntu. While it reports a cuda out-of-memory error, the nvidia-smi tool still shows that GPU is used, as shown below: My code is predicting one example at a time, so no batch used. I am using GPU 0 so the the first 47% is the one my code is using. The error message is below: INFO:tensorflow:Restoring parameters from /plu/../../model-files/model.ckpt-2683000 2021-09-09 07:49:24.230623: I tensorflow/stream_executor/cuda/cuda_driver.cc:831] failed to allocate 15.75G (16914055168 bytes) …

I want to install PyTorch and for that I visited PyTorch official website, and they give me a command to install it with Cuda: pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio===0.9.0 -f https://download.pytorch.org/whl/torch_stable.html The version of Cuda, they want me to install for PyTorch, is 11.1. But I already have Cuda install in my computer which is Cuda 11.2 (for TensorFlow 2.5.0). My question is if I install PyTorch with that command they gave me, will it remove Cuda 11.2 ? If …

I have created a GCP VM with Tesla K80 GPU attached to it. I have installed Nvidia 465 drivers for Ubuntu 20.04 along with Cuda 11. I am trying to use tensorflow on the GCP machine and each time when the training starts the machine crashes after few epochs. Here is the log 216/216 [==============================] - ETA: 0s - loss: 2.5774 - accuracy: 0.2203 216/216 [==============================] - 173s 800ms/step - loss: 2.5774 - accuracy: 0.2203 - val_loss: 47.4114 - val_accuracy: …

New CUDA 11.x versions add support for TF32 format, other new features for newer cards (RTX30xx, A100 etc). Is it worth upgrading to CUDA 11.x if you have GTX 1050 or RTX 2080 (having tensor cores)? Could it be that new features only add computational overhead (at least in the size of installation file, they do), and an older GPU won't be able to use the new features?

I am trying to write a neural network that will train on plays by Shakespeare and then write its own passages. I am using pytorch. For some reason, my GPU immediately runs out of memory. Note I am not running it on my own GPU; I am running it using the free GPU acceleration from Google Colab. I've tried running a different notebook using the GPU and it works, so I know it's not because I ran into some GPU …

I am confused about CUDA compatibility. I am studying deep learning and looking for a laptop to buy. One laptop has GTX 1650ti and another has GTX 1650. Will both be able to use GPU for model training, or only second one? I checked for gpu compatibility. On the nvidia website only gtx 1650 is mentioned. But on some other forums I read that both can work.

Training an LSTM using the exact code and dataset on two different machines with different components yields different results in terms of training time. However, for my case, the results were the opposite of what was expected. Is there reasoning for this? Perhaps I'm not making full use of the second machine. Both machines are running identical versions of CUDA 10.1, cuDNN 7.6.5.32, Python 3.8 along with relevant modules installed a few days ago at the same time (tensorflow, tensorflow-gpu,keras,scikit-learn,numpy,pandas,finnhub-python). …

I have noticed that more often when training huge Deep Learning models on consumer GPUs (like GTX 1050ti) The network often doesn't work. The reason is that the GPU just doesn't have enough memory to train the said network. This problem has solutions like using the CPU's cache memory for storage of things that are not being actively being used by GPU. So my question is - Is there any way to train models on CUDA with memory drawn from …

I'm using Keras (tf backend) to train a neural net; I'm accelerating with GPUs using the multi gpu options in Keras. For some reason, the program seems to heavily load one of the cards and the others only lightly. See the output from nvidia-smi below. +-----------------------------------------------------------------------------+ | NVIDIA-SMI 418.40.04 Driver Version: 418.40.04 CUDA Version: 10.1 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| …