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Questions tagged [hardware]

For questions related to data science and its overlap with computer hardware. This may include GPUs, processing times, cloud computing, TPUs, etc.

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Is RTX 2050 compatible with PyTorch? Is it even CUDA-capable?

The NVIDIA site does not list GTX 2050 as CUDA enabled, and does not list its compute capability. However, if you google "Is RTX 2050 cuda enabled", the first result you get is some NVIDIA ...
Daigaku no Baku's user avatar
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0 answers
48 views

Are LLMs floating FP16?

I am curious to experiment with a project like this below where one can use an open-source LLM and then retune it with their own data where in this repo its a PDF in a folder called ...
bbartling's user avatar
  • 403
-1 votes
2 answers
244 views

Laptop for machine learning jobs

I am buying a new laptop for data science and web development jobs. Which combination is better: i9 (12900H) & NVIDIA T600 4GB or i7 (12800H) & NVIDIA RTX1000A 4GB? Both run on a DELL ...
Shad81's user avatar
  • 1
2 votes
2 answers
722 views

Which desktop hardware is best for DL?

I will be building my home Deep Learning workstation. Right now, I'm digging for some time about the best HW to use for home conditions. The workstation will be used for my work as a developer, but I ...
Jaroslav Štreit's user avatar
0 votes
0 answers
117 views

Which hardware setup is better for workstation/server to run R & RStudio on Linux

I have looked up for specs of computers to run R & RStudio and found that Linux (Ubuntu) based machine should be the choice. I talked to a supplier that won a tender at my university for computer ...
mschmidt's user avatar
0 votes
1 answer
561 views

How to find the number of operation ( multiplication or addition etc) required given a Keras model?

I want to implement an FPGA code or hardware code of a Keras model. As a first step, I want to find the number of mathematical operations required to evaluate a predicted output given a model. The ...
Creator's user avatar
  • 103
1 vote
1 answer
879 views

How do NVIDIA GPU restrictions affect AI computational frameworks?

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 ...
Thomas's user avatar
  • 119
1 vote
0 answers
7 views

Hardware datapaths for weights and operands

A paper, Survey and Benchmarking of Machine Learning Accelerators, mentions Conversely, pooling, dropout, softmax, and recurrent/skip connection layers are not computationally intensive since these ...
kiriloff's user avatar
  • 143
1 vote
1 answer
234 views

How many video streams can single GPU handle for object detection

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 ...
viator's user avatar
  • 111
2 votes
1 answer
139 views

How do data types influence hardware (CPU / GPU / TPU) performance?

I am currently dealing with a relatively big data set, for which I have some memory usage concerns. I am dealing with most of the different data types : floats, integers, Booleans, characters strings ...
Lucas Morin's user avatar
  • 2,244
1 vote
1 answer
349 views

Is it true more CPU core is better for deep learning?

I just started to learn the deep learning in my free time. I was hoping to buy a laptop where I want to implement some small(alexnet) to medium(GoogleNet) networks maybe something bigger. I searched ...
Fazla Rabbi Mashrur's user avatar
2 votes
0 answers
33 views

Will GPU optimized model run on TPU?

There is a project which contains models in DLC format (Snapdragon Neural Processing Engine - SNPE) that I guess are optimized for the Qualcomm Snapdragon 820 chipset (see second link). The project ...
Kozuch's user avatar
  • 121
0 votes
1 answer
124 views

Why GPU doesn't utilise System memory?

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 ...
neel g's user avatar
  • 227
0 votes
1 answer
40 views

Performance gain of GPU when learning DNNs

Currently, I learn deep neural networks on my CPU (i7-6700K) using TensorFlow without AVX2 enabled. The networks need about 3 weeks to be learned. Therefore, I am searching for a (cheap) way to speed ...
Xafer's user avatar
  • 51
1 vote
0 answers
11 views

Which PC hardware would you recommend to invest in for movement ecology studies in R (x,y,t data analysis)? [closed]

I mean all possible work with tagging data: GIS, tagging data pre-processing, visualisation, different types of modelling, simulations and modern analysis. I think it will be about 40 tagged animals ...
Sultan's user avatar
  • 11
5 votes
1 answer
850 views

Reducing the GPU memory usage when the model is already small enough

I trained a model and froze it into a PB (protocol buffer) file and a directory of some variables, and the total size is about 31M. We deployed it using a GPU card and followed this answer and set the ...
Lerner Zhang's user avatar
2 votes
1 answer
66 views

How to estimate machine learning training speed on different hardware

I need to put together a proposal for buying a better computer for machine learning. Is there any good way to estimate the general training speeds of computer hardware? Basicly, I want to be able to ...
Matthew L's user avatar
2 votes
1 answer
35 views

General equation for getting an idea of the scale of a machine learning project

I'm writing an application for a project where we intend to teach a model to predict one aspect of an environment (traffic safety) using a database with 10 images (about 300x300px and, say, 256 colors)...
Henrik's user avatar
  • 123
0 votes
0 answers
66 views

hardware configuration for data science

I would like to know what type of computer configuration to use to perform datascience on medium to low quantity of data (< 100 000 matrix line x 50 parameters). I have read some blog on people ...
lelorrain7's user avatar
2 votes
0 answers
33 views

Will crypto currency mining hardware work fine for machine learning?

I've found that because of recession in crypto mining business a lot of mining rigs are available on sale in pretty reasonable prices. For around $1000 we can buy used machine. Will rig like this work ...
Łukasz W.'s user avatar
0 votes
1 answer
2k views

Surface Pro 6 vs Macbook Pro for Professional Data Science Practice [closed]

[I strongly agree this is totally very opinionated question, thus narrators feel free to vote to close it if you feel it is right, but I find endless pros and cons on the Internet, I've decided to ask ...
TwinPenguins's user avatar
  • 4,279
1 vote
0 answers
132 views

Hardware for deep learning [closed]

Disclaimer: I am very new to using stackexchange so bear with me. I am trying to gain experience using neural networks and would like to do a few projects involving them. Right now I am trying to ...
valentinocc's user avatar
1 vote
1 answer
277 views

What will the required time to process 500GB of images using NVIDIA GEFORCE 930M GPU

I have an image dataset of size 500GiB, and my system specs are NVIDIA GEFORCE 930M, 12GB of RAM and Intel Core i5. I have the following questions: Is it possible such a large dataset to be used in ...
Tomonso Ejang's user avatar
1 vote
0 answers
40 views

What video card is necessary for resnet34 64 batches?(How much memory) [closed]

I have an old nvidia gt240 and I was considering buying gt 730. However, despite being 7 years older, the gt240 is faster. Do I need at least 4g memory? Also, what card would you recommend?
Goking's user avatar
  • 23
5 votes
1 answer
781 views

GPU performance is about 50% slower than benchmarks

Running this benchmark I get 50% slower performance than the author on practically all deep learning sub problems (SINGLE precision and on TRAINING only): I tested this on a GeForce 1080 GTX Ti and ...
Muppet's user avatar
  • 797
0 votes
1 answer
46 views

Machine Learning methods suited for CPU

I have a large number of x86_64 cores available to me, but no GPUs or TPUs. Which Machine Learning techniques are suited for execution on a CPU? I would imagine more "statistical learning" techniques ...
OregonTrail's user avatar
1 vote
1 answer
613 views

How do I choose a laptop if I'm interested in learning and applying data science? [closed]

I just got started with learning data science, and I was wondering what type of laptop I'd need to buy. I understand that this might come probably a little too early -- but my current laptop ...
WorldGov's user avatar
  • 133
0 votes
1 answer
8k views

Do Data Scientists prefer MACS? [closed]

I see many Data Science (DS) tutorials done on MACS, and many DS blogs recommend MACS as the best developing platform, thus the quote "Data Science is statistics on a Mac" came more than once into my ...
Riddle-Master's user avatar
3 votes
1 answer
2k views

Performance of four GTX 1080 Ti's versus one Tesla V100 for Deep Neural Network training

While this may be slightly off-topic, this question does pertain to data science and machine learning. I want to train a VGG16 model on Imagenet from scratch. For this purpose, I am looking into ...
arao6's user avatar
  • 141
1 vote
0 answers
31 views

Cloud computing with country-specific region for Switzerland

Are there any cloud computing services that allow for processing and storing data exclusively in Switzerland? Are there any that have machine-learning-specific functionality? Most do not seem to have ...
Bobby's user avatar
  • 114
2 votes
1 answer
35 views

Are there any reports on Hardware errors affecting experiments?

I recently wrote this as a list of what I have seen / can think of as problem sources that make it hard to reproduce (replicate?) an experiment. I think I have seen most of them, except for hardware ...
Martin Thoma's user avatar
  • 18.9k