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 have been stuck with a problem like this for a while now. I have an AWS setup with 500 GB of RAM and about 7 GPUs. Now the issue is that each time I try to run my Keras with TensorFlow as back-end code, it runs out of memory. I have found out the reason for this as well. The reason is that each GPU just has 12GB of memory, whereas my model needs more than that. So, how …

I have a colab pro+ subscription and like many, I'm finding that the GPU allowance is rather small compared to the price. While I wait for GPU access I was wondering if the TPU VM would be a substitute. It's running now and seems slower. I have not adjusted my code. Is there any point in this? To be honest, I'm not quite clear on the difference between a TPU and a GPU. I ran lscpu in the console and …

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'm working on an NLP task, using BERT, and I have a little doubt about GPU memory. I already made a model (using DistilBERT) since I had out-of-memory problems with tensorflow on a RTX3090 (24gb gpu's ram, but ~20.5gb usable) with BERT base model. To make it working, I limited my data to 1.1 milion of sentences in training set (truncating sentences at 128 words), and like 300k in validation, but using an high batch size (256). Now I have …

Is there a way to run complex list comprehensions like the following on GPU? [[x[index] if x[index]>len(x) else x[index]-1 for x in slice] if (len(slice)==1) else slice for slice,index in zip(slices,indices)] To what degree is it Possible? Do I have to convert it to some kind of numpy comprehension (if so what part is speciffically possible/necessary) The goal is performance optimization on large datalists/arrays.

I need to detect objects from multiple video streams at realtime (or close to it, like 10 FPS). How many GPUs do I need to detect objects using YOLOv3 or MobileNet for, say, 10 video streams? Is it possible to use CPU or something else? I don't need an exact number. I just need to understand scalability perspective and costs per single stream.

Can anyone recommend an alternative to the big 3 cloud computing alternatives? I know they're the best but I found them overly complicated because they cater to massive enterprises. The amount of set up required just to get an instance running is too much. I am looking for a multi GPU cloud offering which offers RAPIDS pre-installed. I see that Blazing SQL will have an offering soon, does anyone know of anything else that I could use in the mean …

I know that Caffe uses GEneral Matrix to Matrix Multiplication (GEMM) which is part of Basic Linear Algebra Subprograms (BLAS) library for performing convolution operations. Where a convolution is converted to matrix multiplication operation. I have referred below article. https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/ I want to understand how other deep learning frameworks like Theano, Tensorflow, Pytorch perform convolution operations. Do they use similar libraries in the backend. There might be some articles present on this topic. If someone can point me to those …

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´m trying to code a CAM or more simply a dictionary storing the pointer of the data accessible by a key. I try to do it with a GPU but all attempts have been inefficient compared on using System.Collections.Generic.Dictionary. Does anybody know how to implement this with CUDA to obtain a better performance than with a CPU?

ISSUE: while training Darknet with GE FORCE GTX 1650 using following: CUDA 11.0 cuDNN 8.0.5 OPENCV 4.5 Model starts training with config file details as below for [net] section: [net] # Testing #batch=1 #subdivisions=1 # Training batch=64 subdivisions=16 width=416 height=416 channels=3 momentum=0.949 decay=0.0005 angle=0 saturation = 1.5 exposure = 1.5 hue=.1 learning_rate=0.001 burn_in=1000 max_batches = 6000 policy=steps steps=4800,5400 scales=.1,.1 #cutmix=1 mosaic=1 #:104x104 54:52x52 85:26x26 104:13x13 for 416 When I change the batch from 64 to 32 (reducing it ) coupled …

I finally got a computer with 2 gpus, and tested out https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html and https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10_estimator and confirmed that both gpus are being utilized in each(The wattage increases to 160-180 on both, Memory is almost maxed out on both, and GPU-Util increased to about 45% on both at the same time). So I decided I would try out tensorflow's MirroredStrategy() on an exitsting neural net I had trained with one GPU in the past. What I don't understand is that the wattage …

I know this question is very vendor specific and as time passes it might change but I am wondering how NVIDIA available GPU cards nowadays (2022) are restricted in any way license wise or hardware wise to be used for training and interference? Is it prohibited to use these cards in production systems? For example there are several RTX 3060 Gaming cards available in shops. Is it allowed to use these for AI? Side question: Is there any CUDA restriction …

What are the pros/cons of using external GPUs (e.g., connected through thunderbolt) vs. internal GPUs for machine learning?

I am using Keras=2.3.1 with Tensorflow-gpu=2.0.0 backend. While I trained model on two RTX 2080 ti 11G GPUs, it allocates all data to '/gpu:0',and nothing changed with '/gpu:1'. Surely, the second GPU not used at all. However, every GPU could work if I selected only one GPU. Moreover, the two gpus can be run parallelly in Pytorch. Follow some instances, I try to run multi-gpus with these codes: Below is NVIDIA-SMI output when I run a multi-gpus model. and cuda …

Suppose I want to develop and train a big end-to-end deep learning model using Tensorflow (1.15, for legacy reasons). The objects are complex, with many types of features that can be extracted: vector of numeric features of fixed length, sequences, unordered sets, etc. Thus, the model will include many submodules to deal with various types of features. I have access to a server with several GPUs, so I want to distribute the model across them. What is the best way …

I have the following neural network: normalizer = preprocessing.Normalization() normalizer.adapt(np.array(trainX)) batch_size=32 learning_rate=1e-3 model = tf.keras.Sequential([ normalizer, layers.Dense(128, activation='elu', kernel_regularizer=regularizers.l2(0.01)), layers.Dropout(0.5), layers.Dense(128, activation='elu', kernel_regularizer=regularizers.l2(0.01)), layers.Dropout(0.5), layers.Dense(2), layers.Softmax()]) model.compile(optimizer = keras.optimizers.Adam(learning_rate=learning_rate), loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = False), metrics = ['accuracy']) fitted_model = model.fit(trainX, trainY, epochs=50, verbose=0, batch_size=batch_size) I would like to know how to build this neural network without using the compile function Also what would I need to change if I want to run it on gpu instead of cpu

I have the following code, for vertical federated learning where i use keras to create a simple 2 layer NN batch_size = 32 learning_rate = 1e-3 epochs = 50 optimizer = keras.optimizers.Adam(learning_rate = learning_rate) loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits = False) train_acc_metric = tf.keras.metrics.SparseCategoricalAccuracy() class Client(): def __init__(self, train, test, labelled): self.__trainX = train.copy() self.__testX = test.copy() self.labelled = labelled if (labelled): self.__trainY = self.__trainX.pop('credit_risk') self.__testY = self.__testX.pop('credit_risk') normalizer = preprocessing.Normalization() normalizer.adapt(np.array(self.__trainX.loc[common_train_id])) self.model = keras.Sequential([ normalizer, layers.Dense(128, activation='elu', kernel_regularizer=regularizers.l2(0.01)), layers.Dropout(0.5), layers.Dense(128, activation='elu', …

I am trying to run a keras model on vast.ai using multiple GPUs. For that I am using keras.utils.multi_gpu_model , however I keep having this error: if multi_GPU and n_GPUs > 1: model = multi_gpu_model(model) AttributeError: module 'tensorflow_core._api.v2.config' has no attribute 'experimental_list_devices') I am using this default docker : Official docker images for deep learning framework TensorFlow Successfully loaded tensorflow/tensorflow:nightly-gpu-py3 I have also checked the available GPUs and all the GPUs are detected correctly: Any ideas? cheers