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Questions tagged [gpu]

Graphics processing units or GPUs are specialized hardware for the manipulation of images and calculation of local image properties.

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What is accelerated years in describing the amount of the training time?

As described in this article, it was written that GPT-3 took 405 V100 years to train in 2020. I'm a bit confused about this definition, does that mean the process was accelerated like using a V100 GPU ...
Marziye Hasanshahi's user avatar
-2 votes
1 answer
55 views

How to run our python scripts utilizing our device's GPU? [closed]

My laptop has NVIDIA GeForce GTX1650 GPU. I want to utilize this GPU to run my Python script. Any help in the form of code would be really helpful. I mean tried researching this so much but I couldn't ...
Escanor6's user avatar
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How does VRAM usage change when running prompts async through LLMs?

If my LLM uses X GB of VRAM when processing prompts sequentially, does it use 2X GB if I run 2 prompts at once using async support? e.g. using Langchains ...
codeananda's user avatar
1 vote
1 answer
724 views

Has anyone tried to use llama.cpp with NVLink?

Apparantly its possible to pool the memory of two 3090 using NVLink (although not with 4090). This would make it possible to run large LLM's on consumer hardware. https://huggingface.co/transformers/...
user2741831's user avatar
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Does splitting a model across multiple GPUs (using model parallelism) reduce model accuracy?

When training a model in PyTorch on multiple GPUs, it should ideally give the same results as using just one GPU. However, are there any specific things to be careful about during implementation?
willtryagain's user avatar
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19 views

How to Train Multiple Deep Learning Models on Multiple GPUs?

I have access to a GPU server with four gpus. Now I would like to train multiple models or folds of one model, one on each gpu. How can I schedule multiple trainings, so that a new training instance ...
e.Fro's user avatar
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1 vote
0 answers
80 views

Inference process and flow, and role of GPU, CPU, and RAM [closed]

This is a noob question. I load a HuggingFace transformer model into GPU and create a HuggingFace pipeline using that model. Then I run inference on the model using the pipeline. I would be glad to ...
ahron's user avatar
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1 vote
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Relation between Batch Size and Micro Batch Size

In distributed training of large models (pipeline parallelism), a mini batch of training samples is divided into n-micro batches. Each device performs forward and backward passes for a micro batch. ...
Rituraj Singh's user avatar
1 vote
1 answer
307 views

How are gaming GPUs different from GPUs for ML? [closed]

I have a seemingly innocent question. I was looking to buy a computer for deep learning development using large language models. When I searched online on consumer e-commerce websites, I could not ...
morpheus's user avatar
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5 votes
3 answers
4k views

For an LLM model, how can I estimate its memory requirements based on storage usage?

It is easy to see the amount of disk space consumed by an LLM model (downloaded from huggingface, for instance). Just go in the relevant directory and check the file sizes. How can I estimate the ...
ahron's user avatar
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How important is to fix this common Tensorflow warning?

I have seen some people with the same problem, this is the warning: ...
Mah Neh's user avatar
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2 votes
0 answers
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How does one deal with images that are too large to fit in the GPU memory for doing ML image analysis?

How does one deal with images that are too large to fit in the GPU memory for doing ML image analysis? I am interested in detecting small structures on images which are themselves many GB in size. ...
Luca's user avatar
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0 answers
630 views

Do tensor cores provide advantages for running Stable Diffusion or only for training?

If I am only interested in running Stable Diffusion, using pre-trained weights, to generate images, are there any advantages to using a GPU with more Tensor cores? Or will any CUDA-compatible GPU ...
sak's user avatar
  • 101
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1 answer
79 views

How to speed up my neural network?

I would like to train an LSTM-based variational autoencoder on a large dataset (37 million sentences). However, I have calculated that my training speed as of now is too slow (on Google Colab). I am ...
postnubilaphoebus's user avatar
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2 answers
4k views

How "exactly" are AI-accelerator chip ASICs built differently than GPUs as GPU seem to lead for many AI workloads on performance

There is a lot of discussion on google search about AI-custom-accelerators (like Intel's Gaudi) and GPUs. Almost all of them say generic things like, a) AI Accelerator chip is for specialized AI ...
Joe Black's user avatar
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1 answer
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Pretrain large model on single GPU [closed]

i want to pretrain some model on P100 which is provided by kaggle. Pretraining on 3 A100 is about 1.5 day. I have 2 questions: Can I put the same seed everywhere so that the results match, train the ...
kali_xyyali's user avatar
1 vote
1 answer
70 views

Why training the same model on the same data can be slower on better card?

Can someone explain why training CNN model (in my case DenseNet201) on the same data, and the same data processing pipeline can be slower on better GPU (RTX3090) than worse one (RTX3060), with the ...
GKozinski's user avatar
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0 votes
1 answer
35 views

What kind of neural network and GPU should I use to classify images into > 10 000 classes?

I am trying to developp an image classifier that would have more than 10 000 classes but I don't know what kind of neural network I should use ? Some Other questions arise from this one : How big ...
Louis Delporte's user avatar
-1 votes
1 answer
2k views

Choosing proper graphic card for deep learning AND gaming [closed]

Though the combination between gaming and deep learning might not sound "serious" enough still this is the case - on the one hand, I need a great GPU for my son to play games, and it will be ...
Igor's user avatar
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0 answers
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Tensorflow-gpu and multiprocessing

I have finished implementing an Asynchronous Advantage Actor-Critic (A3C) agent for TensorFlow (gpu). By using a single RMSprop optimizer with shared statistics. To do so, a central controller holds ...
Lyn Cassidy's user avatar
-1 votes
1 answer
818 views

Should I spend money on a machine-learning capable PC or just use Google CoLab? [closed]

Assuming I have internet access, should I spend money on a PC or just use Google Colab? I'll be doing deep-learning training. Google CoLab: https://colab.research.google.com/
Daniel Melo Avila's user avatar
0 votes
2 answers
284 views

Is it true that batch size of form $2^k$ gives better results?

I am confused among the following in selecting the batch size for my model. #1: powers of 2 I generally see that batch sizes are in powers of two: 32, 64, 128, 256. #2: maximum GPU Suppose my GPU ...
hanugm's user avatar
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2 votes
0 answers
365 views

How to train neural networks with multiprocessing?

I am trying to figure out how multiprocessing works in neural networks. In the example I've seen, the database is split into $x$ parts (depending on how many workers you have) and each worker is ...
Yedidya kfir's user avatar
1 vote
0 answers
53 views

How can traditional edge detection algorithms be implemented on a GPU?

How can edge detection algorithms, which are not based on deep learning, such as the canny edge detector, be implemented on a GPU? For example, how are non-edge pixels removed from an image once it ...
user11937382's user avatar
1 vote
2 answers
15k views

How much computing power does it cost to run GPT-3? [closed]

I know it cost around $4.3 million dollars to train, but how much computing power does it cost to run the finished program? IBM Watson chatbot AI only costs a few cents per chat message to use, ...
Simon Suh's user avatar
  • 121
2 votes
1 answer
148 views

How do GPUs faciliate the training of a Deep Learning Architecture?

I would love to know in detail, how exactly GPUs help, in technical terms, in training the deep learning models. To my understanding, GPUs help in performing independent tasks simultaneously to ...
Anubhav Sachan's user avatar
8 votes
2 answers
10k views

Effect of batch size and number of GPUs on model accuracy

I have a data set that was split using a fixed random seed and I am going to use 80% of the data for training and the rest for validation. Here are my GPU and batch size configurations use ...
bit_scientist's user avatar
0 votes
0 answers
27 views

What are the properties of a model that is well suited for for high performance real-time inference

What are general best practices or considerations in designing a model that is optimized for real-time inference?
matthias_buehlmann's user avatar
3 votes
0 answers
29 views

Good model and training algorithm to store texture data for fast gpu inference

Now, the following may sound silly, but I want to do it for my better understanding of performance and implementation of GPU inference for a set of deep learning problems. What I want to do is to ...
matthias_buehlmann's user avatar
4 votes
2 answers
808 views

How does a transformer leverage the GPU to be trained faster than RNNs?

How does a transformer leverage the GPU to be trained faster than RNNs? I understand the parameter space of the transformer might be significantly larger than that of the RNN. But why does the ...
YoYO Man's user avatar
  • 151
3 votes
1 answer
206 views

Training network with 4 GPUs performance is not exactly 4 times over one GPU why? [closed]

Training neural network with 4 GPUs using pyTorch, performance is not even 2 times (btw 1 & 2 times) compare to using one GPU. From Nvidia-smi we see GPU usage is for few milliseconds and next 5-...
Troy's user avatar
  • 83
3 votes
1 answer
3k views

How can I reduce the GPU memory usage with large images?

I am trying to train a CNN-LSTM model. The size of my images is 640x640. I have a GTX 1080 ti 11GB. I am using Keras with the TensorFlow backend. Here is the model. ...
Thiedent's user avatar
9 votes
3 answers
6k views

Is a GPU always faster than a CPU for training neural networks?

Currently, I am working on a few projects that use feedforward neural networks for regression and classification of simple tabular data. I have noticed that training a neural network using TensorFlow-...
GKozinski's user avatar
  • 1,260
2 votes
0 answers
27 views

In addition to matrix algebra, can GPU's also handle the various Kernel functions for Neural Networks?

I've read a number of articles on how GPUs can speed up matrix algebra calculations, but I'm wondering how calculations are performed when one uses various kernel functions in a neural network. If ...
Greg Thatcher's user avatar
1 vote
0 answers
48 views

Any good resources for learning programming GPU level operations? [closed]

I want to be able to improve my lower level device specific programming abilities to assist in future endeavors. Examples would be learning to write custom tensorflow operations in C++ optimized to ...
mshlis's user avatar
  • 2,359
1 vote
1 answer
526 views

Can I calculate the training performance of GPUs by comparing their specification? [closed]

I am currently using Nvidia GTX1050 with 640 CUDA cores and 2GB GDDR5 for Deep Neural Network training. I want to buy a new GPU for training, but I am not sure how much performance improvement I can ...
Lei Xun's user avatar
  • 43
1 vote
2 answers
6k views

Does fp32 & fp64 performance of GPU affect deep learning model training? [closed]

I am purchasing Titan RTX GPU. Everything seems fine with that except float32 & float64 performance which seems lower vis-a-vis some of its counter parts. I wanted to understand if single ...
Ruchit Dalwadi's user avatar
8 votes
2 answers
21k views

Can LSTM neural networks be sped up by a GPU?

I am training LSTM neural networks with Keras on a small mobile GPU. The speed on the GPU is slower than on the CPU. I found some articles that say that it is hard to train LSTMs (and, in general, ...
Dieshe's user avatar
  • 289