# Tag Info

9

This should make a difference, but how big is the difference heavily depends on your task. However generally speaking, a smaller batch size will have a lower speed if counted in sample/minutes, but have a higher speed in batch/minutes. If the batch size is too small the batch/minute will be very low and therefore decreasing training speed severely. However a ...

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This changes according to your data and complexity of your models. See following article by microsoft. Their conclusion is The results suggest that the throughput from GPU clusters is always better than CPU throughput for all models and frameworks proving that GPU is the economical choice for inference of deep learning models. ... It is ...

4

I think you should redirect your focus. Learn and play with ML, and only when compute becomes the main bottleneck for your learning, invest in hardware. I've recently engaged into a ML project for which I 've assembled a machine with gtx1080, installed gui-less ubuntu, configured ssh, drivers etc, and than spend 3 months on data collection. And the dataset ...

3

GPUs are able to execute a huge amount of similar and simple instructions (floating point operations like addition and multiplication) in parallel. In contrast to a CPU which is able to execute a few complex tasks sequentially very quick. Therefore GPUs are very good at doing vector & matrix operations. If you look at the operations performed inside a ...

3

No. Different batch sizes mean different gradients (check stochastic gradient descent concept you will get how loss calculated) are calculated in each step, and thus the gradient descent will likely end up in different places in parameter space. In addition, how this is actually parallelized might make a difference, including the order of operations and ...

2

I advice you to always use GPU over CPU for training your models. This is driven by the usage of deep learning methods on images and texts, where the data is very rich. You must have a GPU suited perfectly for training (e.g. NVIDIA 1080, NVIDIA Titan or higher versions), I wouldn't be surprised to find that your CPU was faster if you don't have a powerful ...

2

First off I would like to post this comprehensive blog which makes comparison between all kinds of NVIDIA GPU's. The most popular deep learning library TensorFlow by default uses 32 bit floating point precision. The choice is made as it helps in 2 causes: Lesser memory requirements Faster calculations 64 bit is only marginally better than 32 bit as very ...

2

The answer is it depends. Are you referring to GPU vs CPU when training, or running inference? When you say CPU is it an x86 CPU or ARM CPU? What algorithms are running? Let's look at image classification. Suppose you select a Convolutional Neural Network(CNN) as your algorithm. CNN's were created in the 1980s, but only really saw use when GPU's were used. ...

2

If you are just starting out with Deep Learning, then a laptop with GTX 1060 is enough. I am using a GTX 1060 myself and I find it adequate for many of my personal projects, training large datasets and participating in most (not all) Kaggle competitions as well. But you say that you want to do research work as well. In that case, you may want to contact ...

2

A lot matters when it comes to comparison of GPU's, I will give you a broad overview of the matter (it is not possible to go into exact details as huge number of factors are actually involved): Cores - Number of CUDA cores increases means the parallelism has increased, thus multiple calculations can be done in parallel but is of no significance if your ...

2

Your dataset class probably have a lot of preprocessing code. You should use a dataloader. It will prefetch data from the dataset when the GPU is processing. Also, you can process all the data beforehand and save to a file. Multiple GPU cannot scale as the GPU have to get all data to one GPU to calculate the loss. The performance of 4 GPU is around 3.5x. A ...

2

As the other links suggest, you basically have four options: "restrict your CNN". This means making your model smaller and simpler, possibly by inserting a pooling layer at the front, or reducing the total number of layers. From a memory perspective, this isn't likely to produce really large gains though. "stream your data in each epoch". By default, the ...

2

I can't anwser your question on how much computing power you might need, but you'll need atleast a smallgrid to run the biggest model just looking at the memory requirments (175B parameters so 700GB of memory). The biggest gpu has 48 GB of vram I've read that gtp-3 will come in eigth sizes, 125M to 175B parameters. So depending upon which one you run you'll ...

1

I think it is premature to answer your question as OpenAI has not made GPT-3 available yet other than via a web-based API. For more information see OpenAI API. From OpenAI will start selling its text-generation tech, and the first customers include Reddit, by James Vincent: Access to the GPT-3 API is invitation-only, and pricing is undecided. You can join ...

1

Seems like you have a CUDA version conflict. Remove the existing CUDA 10.2 and install CUDA 10.0 (going by your missing libraries, it requires a v10.0). You can find the archived releases here: https://developer.nvidia.com/cuda-toolkit-archive

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Here's a link to some benchmarks that should give you some insights. In my experience (I've used systems with both 1080s and V100s) I've found that as of about a year ago, a lot of the common tools couldn't use the V100s well. Until we started doing some manual optimization, the 1080s were comparable if not better on common tasks. Of course, once we put ...

1

It depends, if you ve to solve a "simple" problem which does not require CNN or stacked models without multidimensional data and not many multiplications, big long numbers then if you decide to use CNN / stacked architectures AND GPU it is like using a hammer to insert a needle.It will not only spend energy but the computations will do zero padding in memory,...

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Jetson Nano specs Price:$99: 128-core Maxwell GPU Quad-core ARM A57 @ 1.43 GHz 472 GFLOPS max compute Titan V(for example) Price:$2500 : 640 tensor cores 5120 cuda cores 110000 GFLOPS max compute Some simple maths gets us to a speed of 11,800 GFLOPS for 25 of these perfectly synced in parallel(not likely or gonna be fun). Vs ...

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Deep models are very tolerant to arithmetic underflow. You can hope for neglectable differences in prediction accuracy between FP32 and FP16 models. Check this paper for concrete results.

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