How to solve the error reported by installing pytorch on the Raspberry Pi? - pytorch

ERROR Image
At the beginning, the Raspberry Pi environment was 3.9.2 (armv7l).
After searching the Internet, pytorch found that it only supports 3.8; after installing the conda environment, install pytorch under the (py36) python version 3.6.6; but as shown in the picture,
it can't Install.
Is there anyone on the forum who has encountered this problem and solved it?
Thanks.
At the beginning, when installing pytorch according to the official process, it will report that /home/pi/pytorch/third_party/eigen lacks the extraction file and cannot be installed.
After finding and installing the pytorch.whl file on the Internet and reporting an error, try to install the conda environment; but in conda Environment can't be installed.
Off topic, my second question is if I go to train mobilenet SSD v2 after success, can I directly use the lens for visual recognition?
Before using the public mobilenet to detect, the number of frames was only about 4.4. Later, because I felt that the FPS was too low, I wanted to train mibilenet by myself.
How about someone who has used Raspberry Pi to use FPS after training? If the FPS is too low, is it better to use NVIDIA Jetson Nano for training and detection?

Related

How to run Pytorch on Macbook pro (M1) GPU?

I tried to train a model using PyTorch on my Macbook pro. It uses the new generation apple M1 CPU. However, PyTorch couldn't recognize my GPUs.
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
Does anyone know any solution?
I have updated all the libraries to the latest versions.
PyTorch added support for M1 GPU as of 2022-05-18 in the Nightly version. Read more about it in their blog post.
Simply install nightly:
conda install pytorch -c pytorch-nightly --force-reinstall
Update: It's available in the stable version:
Conda:conda install pytorch torchvision torchaudio -c pytorch
pip: pip3 install torch torchvision torchaudio
To use (source):
mps_device = torch.device("mps")
# Create a Tensor directly on the mps device
x = torch.ones(5, device=mps_device)
# Or
x = torch.ones(5, device="mps")
# Any operation happens on the GPU
y = x * 2
# Move your model to mps just like any other device
model = YourFavoriteNet()
model.to(mps_device)
# Now every call runs on the GPU
pred = model(x)
It looks like PyTorch support for the M1 GPU is in the works, but is not yet complete.
From #soumith on GitHub:
So, here's an update.
We plan to get the M1 GPU supported. #albanD, #ezyang and a few core-devs have been looking into it. I can't confirm/deny the involvement of any other folks right now.
So, what we have so far is that we had a prototype that was just about okay. We took the wrong approach (more graph-matching-ish), and the user-experience wasn't great -- some operations were really fast, some were really slow, there wasn't a smooth experience overall. One had to guess-work which of their workflows would be fast.
So, we're completely re-writing it using a new approach, which I think is a lot closer to your good ole PyTorch, but it is going to take some time. I don't think we're going to hit a public alpha in the next ~4 months.
We will open up development of this backend as soon as we can.
That post:
https://github.com/pytorch/pytorch/issues/47702#issuecomment-965625139
TL;DR: a public beta is at least 4 months out.
For those who couldn't install using conda like me use pip as following:-
Requirement:
Any Macbook with apple silicon chip
macOS version 12.3+
Installation:
pip3 install --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cpu
Update:
No need of nightly version. Pytorch version 1.12 now supports GPU acceleration in apple silicon. Simply install using following command:-
pip3 install torch torchvision torchaudio
You may follow other instructions for using pytorch in apple silicon and getting your benchmark.
Usage:
Make sure you use mps as your device as following:
device = torch.device('mps')
# Send you tensor to GPU
my_tensor = my_tensor.to(device)
Benchmarking (on M1 Max, 10-core CPU, 24-core GPU):
Without using GPU
Using GPU (5x faster)

Can I use high version torch and low version cuda?

I'm setting up my Conda environment with a remote GPU to use Pytorch.
The GPU I use is only NVIDIA-SMI 396.54, so I can only use cuda version 9.2
However, I need to use a higher version torch to be able to use some attributes.
I tried
conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=9.2
But this results in
print(torch.version.cuda)>> None
torch.cuda.is_available() >> False
There are two things I would check.
You may have unintentionally installed the pytorch cpu version or had it in your environment first, before running the above command. Even if you install the gpu version of Pytorch, if you already have the cpu version of pytorch then torch.cuda.is_available() will return False. Therefore I suggest checking out this link:
Forum on why Pytorch is CPU version even after installing cudatoolkit version
Although, I am pretty sure the above thing is your problem, I suggest looking at this second thing.
For understanding how to download previous version of Pytorch refer to this link. https://pytorch.org/get-started/previous-versions/
After looking at this, I suggest starting a new conda env and running your conda install command first.
Sarthak Jain

TrainDeepLearningModel tool not responding in arcgis pro

Platform: Precision 5820, 32G, rtx4000; Win 10 Pro, Arcgis Pro 2.6 concurrent license;
Issue:
I installed the deep learning tools following the guidelines provided here:
deeplearninginstallation
tersorflow was not found after installation so I manually installed the 2.1.0 version. I now have arcgis 1.8.2, pro 2.6, fastai 1.0.60, python 3.6.12, pytorch 1.4.0, tensorflow-gpu 2.1.0; environment check in arcgis pro python seemed fine.
However, after I select toolbox-image analyst-deeplearning-traindeeplearningmodel, the program seems to go into a hang, with most buttons disabled/unresponsive, this would continue until I force terminate the program. I also ran into "tool not licensed" twice, which was gone after I restarted the program; and a "name 'CallBackHandler' is not defined" once, which was also gone after I restarted.
I tried runing the command from the arcgis pro python prompt:
TrainDeepLearningModel(r"**", r"**", 40, "RETINANET", 16, "# #", None, "RESNET50", None, 10, "STOP_TRAINING", "FREEZE_MODEL")
executing the command would also send the program into a hang similar to the previous one. Monitor shows that ram and GPU usage haven't changed much, so I left the program running for an hour before forcibly terminating it.
I'd greatly appreciate it if anyone can tell me what the issues are here. I'll post any other env parameters if anyone requires. Cheers.
I got the tool up and running now by running conda install -c pytorch -c fastai fastai=1.0.54 pytorch=1.1.0 torchvision scikit-image and removing all the conflicting specifications in the cloned arcgispro-py3 env that I had. Now I still don't understand what went wrong. Presumably one or more packages in the env was conflicting. But seeing as I'm not a python expert, I couldn't identify the exact issue.
Before this I tried the versions stated here deeplearning install guide, but wasn't able to get pass tensorflow-gpu because python kept checking conflications. Now I actually don't have tensorflow-gpu in the env. I have tensorflow 2.1.0, keras-applications 1.0.8/base 2.3.1/preprocesing 1.1.0 (no keras-gpu), scikit-image 0.17.2, pillow 6.2.1, fastai 1.0.54, pytorch 1.1.0, libtiff 4.0.10. Some are different from what the guideline provided.
Thing is when I ran the process, CPU usage was up and GPU wasn't despite the fact that I specified GPU as the processing core. But I have much more pressing things to do right now like getting the analysis finished. So I'll probably tweek the env around a little after I'm done with this bit and see what happens. Meanwhile, anyone's input is still welcome.

How to install pytorch on windows subsystem for linux

my windows10 has subsystem for linux of 14.04. I tried to install pytorch on the preinstalled python2 but couldn't work.The error is: torch-0.2.0.post1-cp27-cp27m-manylinux1_x86_64.whl is not a supported wheel on this platform. I tried to install python3.6 then install pytorch with it, but still couldn't work.The error is missing module 'apt_pkg'. Anyone has idea on this?
According to this it should be working now via anaconda's package manager conda.
If you look in the whl filename, there are two bits to pay attention to. Firstly, the cp27 and secondly the x86_64 bit.
That first one tells you that you need python 2.7 which is the default for the WSL, so that's fine.
The second one tells you that the whl has been compiled for a 32 bit computer. If you have a 64 bit computer, it will fail.

keras Installation with already installed Tensorflow GPU version in windows 10

I have following environment in my windows 10 machine
Python : 3.6.0
Anaconda:4.3.1
Tensorflow:1.1.0
Screen Shots
OS:Windows 10-64bit
Now when I am trying to install keras into my system I am getting a huge list of errros.
Detailed Error Log
Now I have two questions here.
Can I install keras into my system when I already have tensorflow GPU version which was really hard to install?
If keras can be installed into my this system configuration then will my tensorflow GPU version work properly afterwards?

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