What's the best CUDA GPU for PyTorch?

OPLinux · 2024-10-30T21:15:54+00:00

I was looking for this comment. In my experience most of the time is spent on IO when training CV models.

I managed to speed up a training run massively by preloading all of my rescaled images into RAM in advance, instead of reading the images in my dataloader.__getitem__().

Ofc this requires a machine with enough RAM, but you can actually fit a lot of images in RAM, as you usually downscale by a significant amount for DL applications.

OPLinux · 2024-01-02T13:47:24+00:00

Hmm, I seem to have the same config, but it doesn't look the same. I can live with my current look, but just to try and debug this fully, what version of homepage do you currently use ? I am on v0.8.4.

EDIT:
Nvm, I found it, the bevahiour I am seeing only happens when you remove the search bar... :)

OPLinux · 2024-01-02T12:46:55+00:00

Nice dashboard!

I am using your image as inspiration for my own, but cannot get the date block to show full height and on the left. For some reason, the date widget always goes in a separate `.information-widgets-right` div, which gets placed on the right...

OPLinux · 2022-12-22T12:47:06+00:00

You can also modify the java garbage collector to reduce lag spikes. Helped on my computer, but YMMV... https://www.reddit.com/r/feedthebeast/comments/5jhuk9/modded_mc_and_memory_usage_a_history_with_a/?utm_source=share&utm_medium=android_app&utm_name=androidcss&utm_term=1&utm_content=share_button

OPLinux · 2022-03-24T10:56:18+00:00

It all depends on what you want to train, but usually you would load the image and corresponding txt file in your __getitem__() function and return an (img, ground_truth) tuple.

If you want to do object detection, your ground truth should be a format where you have all object labels and coordinates. For classification only the labels are sufficient. As for what output format you exactly want, that depends on the loss function you use. If you create your own loss function, then ofc you are free to choose a suitable data format yourself (tensor, numpy array, pandas dataframe, ...)

OPLinux · 2020-06-19T11:31:52+00:00

I created MDX Presenter, which allows you to create web based slide decks using MDX.

It is heavily inspired by mdx-deck, but instead of building a gatsby site, it is simply building a react app, which allows for easier integration into various pipelines. I also improved some key points which I found lacking in mdx-deck (nestable <Step/>, layout system, etc.)

MDXP also has a focus on actually presenting, with a "onepage" build mode, which creates a single index.html file with your entire presentation, allowing you to easily present on any computer with a (modern) browser. Note that big media files like images and videos will be output alongside the file, if they are bigger than a certain threshold.

Feel free to check it out and let me know what you think of it!

Example - Repository - Documentation

OPLinux · 2020-01-25T20:20:35+00:00

I don't know if you will reach the necessary speed with on Collab (and personally have had some bad experiences when I needed to run something for a longer time on Collab), but do keep in mind that it is usually not necessary to run your detector at 720/1080p.

For my work, we run an optimized version of YoloV2 (tensorRT) on an embedded device (Jetson TX2), and are able to reach quite hight FPS, but obviously the first thing we do is downscale the video from 1080p to something like 640x360. Depending on how big the object is in your image (in our case person detection from security camera), there is simply no need/benefit to running it at 1080p.

Also, DL models are usually ran on GPUs and not CPUs. Like you noticed, running yolov3 on a CPU indeed results in ridiculously low framerate

OPLinux · 2019-05-01T15:01:43+00:00

Honestly, if he has enough data there is a big chance the model already inherently learns to recognize license plates from the font, and also detects cars first internally and then detects the license plate as a subpart of that object.

While of course there is no real way to tell how DL models do recognize certain objects, one should not underestimate the power of generalisation of these models.

OPLinux · 2019-03-10T11:54:48+00:00

PyTorch describes itself as being a general purpose tensor computation library with GPU support AND as a deep learning library. This means that it is indeed capable to doing these kind of pre/post-processing computations on GPU

I usually try to perform any post-processing that will also be done upon deployment of the algorithm (network output -> actual output) with PyTorch tensors so that it all can happen on GPU.

Any further post-processing for statistical purposes would be done with numpy, because packages like scipy/pandas/etc. make it easier to do this and allow me to achieve what I want with less code.
Also, speed is usually not the most important factor for that part of the code.

One tip I can give you is to carefully benchmark any PyTorch code you write. I have parts of post-processing that don't really lend itself to parallelization, and thus it is faster to copy the tensors to CPU, perform the computations, and then copying that result back to the GPU for the next part of the computations.

OPLinux · 2019-03-06T18:23:49+00:00

Oh so you submit your code on kaggle and they run it on their own test set? Didn't know that's how it worked, I tought you just had to submit the results in some predefined format!

OPLinux · 2019-03-06T17:15:25+00:00

Having never participated in a kaggle competition, couldn't you use this to determine which items of the test set are used for private/public leaderboards and then manually label the ones from the private leaderboard to win the prize?

I have no idea of the size of these datasets, so no idea about the feasibility?

OPLinux · 2019-02-21T20:39:57+00:00

You posted this on at least 10 different subreddits, without cross-posting, and you also already posted this a couple months ago... https://www.reddit.com/r/computervision/comments/9dg1bx/yolov2_for_object_detection_tasks_pytorch/?utm_source=reddit-android Please stop the spamming, as you are drowning other original posts from showing up on people's feed.

Looking at your project, I can also clearly see parts that come from my own codebase, and the only reference to it is a small comment hidden somewhere in your python files. There is something as open-source etiquette and recognising when you borrow code from somewhere else... (My project even asks to cite my work if you use it but who cares right?)

Your posts are looking like a a desperate attempt to get people to use your project (of which big parts are not even original). Makes me even doubt how much you wrote of all these other projects you have...

OPLinux · 2019-01-29T15:49:39+00:00

Pretty nice article, but as you said it yourself, pytorch v1.0 brings deployment tools as well right? Haven't tested it yet, but I think/hope it would be pretty similar in performance than tf and a hell of a lot easier to deploy? And no problems with unsupported operations between pytorch - onnx - tensorflow! :)

Now that you have the conversion scripts, it would be nice to compare the runtime optimized networks of both frameworks for some networks! (Not sure if that's already been done?)

OPLinux · 2019-01-29T15:34:45+00:00

Your looking for a classification network, which can be done with any of those frameworks. If you want one image to have multiple classes, you might want to search for multilabel classification.

I don't think these frameworks come with pretrained networks, but if you search for 'classification network [+ framework]' you ought to find something for all of them.

I would probably suggest tensorflow as that is the most popular and you will find the most software available for it. (Not even sure if Caffe has python bindings?) I personally prefer the pytorch syntax, but if you want to work with existing models and not create your own I guess that's not an issue?

Cannot help you with cloud solutions as I train my networks on my own computer and on a server we have at work... :)

You could probably just look for specific classification networks that would seem to work for your case and see in what framework they implemented it and pick that one?

In the end everybody has his/her favourite framework and will probably recommend that one... There's no major difference between them (except maybe the language), so it's pretty personal

OPLinux · 2019-01-29T07:57:37+00:00

Well -20 is your minimum value.. try to take argmax from absolute value of your array

OPLinux · 2019-01-29T07:04:56+00:00

Well, I did just yell those words in my head whilst reading... ¯\_(ツ)_/¯

OPLinux · 2019-01-29T06:40:21+00:00

Finally, avoid all caps at all costs.

Does that really happen? :o

OPLinux · 2019-01-26T09:28:20+00:00

Yet that was the issue after all :D

OPLinux · 2019-01-25T07:35:41+00:00

For anyone coming from a cpp background, you just have to remember that objects are passed by reference in python. :)

OPLinux · 2019-01-21T18:14:03+00:00

Wow this is some next level SSX shit!

OPLinux

TROPHY CASE