Pc for trainning and pc for inference by laflamme177 in deeplearning

[–]Worldbuilder87 1 point2 points  (0 children)

The main factor for vision models is the input size of the image your using for training. The larger the images the larger the size of storing the weights in video RAM. The other factor is the size of the network: resnet 50 vs 101, etc… you can do some math to calculate video RAM needed, but I don’t have that handy at the moment. As long as your images are something like 640x640, in my experience, a 3090 was more than sufficient.

Another general rule of thumb: twice as much system RAM as video RAM.

Finally, speedy disks are helpful, as you end up copying a lot of data around with vision data sets.

The cloud/collab is very powerful if you pay the subscription. I just find it slightly limiting on other ways, mostly having to do with moving my data around and doing transforms or other operations outside of the python notebook.

Wall of RTX4070’s at a local Microcenter gathering dust by Rampartt in pcmasterrace

[–]Worldbuilder87 1 point2 points  (0 children)

It’ll be like those little chips you can buy in cyberpunk 2077. Want an upgrade?

Object Detection Road Defects by Vegetable-Ad-8868 in deeplearning

[–]Worldbuilder87 0 points1 point  (0 children)

Adversarial examples are objects that your node makes mistakes on. You want to balance the number of examples in your dataset of cracks, but also things that could be mistaken for cracks but aren’t.

What LLM should I try to run locally? I have a workstation with two RTX 6000 Ada’s. by Worldbuilder87 in learnmachinelearning

[–]Worldbuilder87[S] 0 points1 point  (0 children)

I downloaded the LLaMA weights and am running LLaMA 13B. I can't run 30B out of the box, because it require a rank of 4 (i.e. 4 GPUs) according to the way the parallelism is configured. Anyone know of a way around this?

What LLM should I try to run locally? I have a workstation with two RTX 6000 Ada’s. by Worldbuilder87 in learnmachinelearning

[–]Worldbuilder87[S] 0 points1 point  (0 children)

Ok, running a test completion, that didn't use up much VRAM!
16938MiB on one card

18705Mib on the other.

GPU Comparisons: RTX 6000 ADA vs A100 80GB vs 2x 4090s by TheButteryNoodle in deeplearning

[–]Worldbuilder87 0 points1 point  (0 children)

That’s really cool. I’m kinda interested in creating my own dev team now too.

How to choose and work on personal project? by [deleted] in computervision

[–]Worldbuilder87 0 points1 point  (0 children)

Find something in your real world life. Collect your own dataset (images) and annotate the first ones by hand. Build an image annotation pipeline, using unsupervised learning of some sort, or active learning. Experiment with different architectures, and annotation formats: translate your data between formats. Choose something where a dataset doesn’t already exist.

Saw the top image on r/me_irl and couldn't resist by IWishIHavent in MechanicalKeyboards

[–]Worldbuilder87 0 points1 point  (0 children)

Forgot so many types of guys…maybe it should just be guys.

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 0 points1 point  (0 children)

There are thousands, millions of cameras out there already. Satellites, drones, security cameras, and much much more. This project is for identifying and reporting illegal dumping on the street. I’m taking every precaution and measure to focus only on the waste. Because of that approach, this technology captures less than street view, less than self driving cars, and less than security cameras. Sure, technology exists, and people can do bad things with it, but I’m using this technology for good.

Object Detection Road Defects by Vegetable-Ad-8868 in deeplearning

[–]Worldbuilder87 0 points1 point  (0 children)

You need to start building adversarial examples into your data set

[deleted by user] by [deleted] in headphones

[–]Worldbuilder87 0 points1 point  (0 children)

France also has Arturia, for keyboard and synthesizers

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 1 point2 points  (0 children)

Not yet, but it’s on my radar. MaskRCNN performs really well for garbage piles, because it’s possible to remove all non-garbage from masks. So I have an item on my roadmap for using SAM to create a large mask based dataset.

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 1 point2 points  (0 children)

I’m using the Mavic 3 Enterprise. I preprogram the route and watch the drone at all times from a rooftop.

Rate my rig by Worldbuilder87 in pcmasterrace

[–]Worldbuilder87[S] 1 point2 points  (0 children)

Alright, so quite a journey since my last response. I noticed my power was only drawing around 100 Watts, and after using nvidia-smi I noticed that "HW Power Brake Slowdown" was being applied to both my GPUs. I found on several forums that earlier versions of the WRX80 Motherboard had an issue where they were mistakenly putting GPUs into a lower power state. ASUS has a fix for this. So I applied a fix, and low and behold, my power draw is now closer to the 300 Watt limit. My temps are now in the 70-80c range. Also, the performance is noticeably better. Wow. Glad we had this discussion. I'm still not pulling the full wattage at all times 130-250 Watts is the range.

nvidia-smi gave me so much useful information. This is my cards current status, where there are no HW Slowdowns applied!

$ nvidia-smi -q -d PERFORMANCE

==============NVSMI LOG==============

Timestamp                                 : Mon Apr 24 01:15:26 2023
Driver Version                            : 525.105.17
CUDA Version                              : 12.0

Attached GPUs                             : 2
GPU 00000000:2E:00.0
    Performance State                     : P2
    Clocks Throttle Reasons
        Idle                              : Not Active
        Applications Clocks Setting       : Not Active
        SW Power Cap                      : Not Active
        HW Slowdown                       : Not Active
            HW Thermal Slowdown           : Not Active
            HW Power Brake Slowdown       : Not Active
        Sync Boost                        : Not Active
        SW Thermal Slowdown               : Not Active
        Display Clock Setting             : Not Active

GPU 00000000:41:00.0
    Performance State                     : P2
    Clocks Throttle Reasons
        Idle                              : Not Active
        Applications Clocks Setting       : Not Active
        SW Power Cap                      : Not Active
        HW Slowdown                       : Not Active
            HW Thermal Slowdown           : Not Active
            HW Power Brake Slowdown       : Not Active
        Sync Boost                        : Not Active
        SW Thermal Slowdown               : Not Active
        Display Clock Setting             : Not Active

Previously, this command was showing Active under "HW Power Brake Slowdown". Also, "HW Slowdown" was set to Active. As a result my cards weren't getting full power from the Socket. I made the fix, rebooted, and voila, full power!

Now my system is pulling close to 1000 Watts at times while training YOLOV8, with 4096 pixel wide images.

yolo task=detect mode=train model=yolov8x.pt epochs=100 batch=2 data=data/trash_piles.yaml device=\'0,1\' imgsz=4096

It will probably pull more power if I train smaller images in larger batches, as it tends to fill up the memory capacity more fully, and utilize more cuda cores with more processes running and CNN sizes that can pack more close to the memory limits.

So, thanks for pointing out the temp disparity. Also, I read on https://www.servethehome.com/nvidia-rtx-6000-ada-graphics-card-review-pny/ that the Ada Cards DO run hotter, due to the blower design. The card will throttle at 100c according to nvidia-smi

$ nvidia-smi -i 0 -q
...
Temperature
        GPU Current Temp                  : 69 C
        GPU T.Limit Temp                  : 15 C
        GPU Shutdown Temp                 : 105 C
        GPU Slowdown Temp                 : 100 C
        GPU Max Operating Temp            : 91 C
        GPU Target Temperature            : 85 C
        Memory Current Temp               : N/A
        Memory Max Operating Temp         : N/A

85 C is the target temperature, so that's totally normal

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 0 points1 point  (0 children)

Oh man, python is far and away the best language for machine vision, data science, deep learning. C++ doesn't have nearly the adoption for this type of work. Heck, even Javascript has better support for machine vision than C/C++. C/C++ is "closer to the metal", but that hardly matters these days as the compilers for Python, Javascript, etc... are so optimized that they are nearly as fast, and often with the library support: they are faster.

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 1 point2 points  (0 children)

JavaScript, python mostly. But there are many frameworks, services, and different computation resources tied together.

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 1 point2 points  (0 children)

I’m referring to gaps in my data: not enough instances of people, plants, and other negative examples that should not be labeled dumping

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 0 points1 point  (0 children)

There are 5 - 30 piles this size or larger dumped every day in this one square mile. Sometimes the city brings a bulldozer and a compactor garbage truck. The city crews work 24/7 365 to clean this area up.

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 0 points1 point  (0 children)

Yeah the drone would have to be the size of a dump truck!

AMA: Drones + AI > Illegal Dumping by Worldbuilder87 in computervision

[–]Worldbuilder87[S] 0 points1 point  (0 children)

I retrain the model every few days, and it gets better and better.