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TensorFlow and Keras GPU Support - CUDA GPU Setup
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- Опубликовано: 14 авг 2024
- In this episode, we'll discuss GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU!
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Dear deeplizard team, I love you so much for doing this videos! It took me three nights to install tensorflow with gpu support according to depency errors, missing libraries and all that stuff. I can't believe how you can manage to stay that calm while talking about this installation process for it caused me so much headache. Thanks for sharing your experience and keep going!
your expression literally make me smile at 3:59. your lectures are truly instructive
This tutorial deserves more appreciation. i followed each step and everthing worked flawlessly. Thank you so much for this.
note- if you are downloading cudnn ver 8.1.0 then there will be more than one file in the lib/bin folders. Copy and paste all of them into the other folder
Thank you! Several days of aggravation (linux, Win7, back to linux, try again in Win7 yada, yada...) and now it's working thanks to you.
Thanks! This saved me lots of time fumbling around in the dark to get GPU working. Hell, I was still importing from keras until after this video when you guys informed that keras is integrated into tensorflow and that I am importing from an outdated API. Big thanks.
between all the wrong versions installed of cuda and VS, this took hours but i got it in the end. THanks for the vid!!
For anyone who installed the latest visual studio and got an error message. I think the latest one nvidia cuda recognizes is 2019 or something. look it up online, but don't just go with the latest.
This channel has been my goto for a long time now. Great work guys!
Mild humor, charming speech covers all we need to know
Hello from Vietnam (you can spot a string "Nguyen" in my name), enjoy your trip! Thanks so much.
Your courses are tremendous, so much clearer, crisper and to the point than the tons of courses out there. Love the short length and the industry snippets. Would love to meet you guys one day
I was about to install cuda for TF on windows and then ran into this.
Thank you for making life easier👍👍
Awaiting more tutorials on this series.
Fantastic instruction video. Very clear and the timing was great. I was able to follow along and go through the steps with you. Thanks bunches.
This video is immensely useful. Great tips all around. If windows too check display adapters and version of windows to get that update: C:\ProgramData\Microsoft\Windows\Start Menu\Programs\Administrative Tools then check System Information for win version, and grab drivers. Well done and amazing video - thankyou!!
Just starting out with all this so this was very timely. Great vid! Looking forward to delving deeper into all your content
Most talented and beautiful lizard I ever saw! Thankyou for this amazing content!!!
Was working on a project involving style gan and was pulling my hair out over how to use GPU, very easy to follow tutorial, thanks!
Thank you, this was a huge help.
Thanks a lot ffor this video, I have a GPU but was unaware of how to use it for deep learning.
Thanks for this detailed CUDA setup checklist.
Thank you very much for this video! It's great to have a really beginner course for this! It helps noobies at this like me!
Thank you. Very helpful
AWESOME!!! It worked for me :)
Thank you, this video helped me
Nice ! I wait for a video about GAN for image data augmentation :)
Yes. I also want to see the videos coming out. The videos for NLP and RNN things.
thank you for making life easier
I have found it far easier to install tensorflow using the conda terminal in Anaconda. Conda can install tensorflow up to version2.1,1, When you install using conda it automatically installs cuda toolkit version 10.1.243 and cudnn version 7.6.5.This way you do not have to do all the downloading and changing of environment variables etc. If you want to install tensorflow version 2.2 first install 2.1 with cuda the install tensorflow version 2.2 with pip as in pip install tensorflow ==2.2.0. Toolkit version 10.1.243 and cuddd 7.65 are compatible with tensorflow 2.2.
Thanks for the tip!
Thank you. Very helpful.
This was extremely helpful, thank you.
As always, super cool video from super cool couple
The procedure is very long surprisingly!! I didn't had to do this for Pytorch.I could get GPU running for Pytorch within minutes.
Wow it worked 👍 It will save my cpu❤️
Thankyou so much cheers!
This helped me a lot!, thank youuuu
front camera view is better than side view
Amazing and helpful video, thank you very much!
Great tutorial! thank you so much
This is Love!
thanks for sharing!
You are always the best
Thanks
Thanks a lot
Thank you, this got it working for me!
please complete the video series on reinforcement learning. optimizing reinforcement learning
I did all of the steps and still Num GPUs are 0. would you help me with this?
ty
Is the Game-Ready Driver incompatible or inefficient with tensorflow? Or is your choosing the Studio Driver a matter of preference?
Bravo! I m so happy. Thank you :)
thank you only your process worked
Please help!
Before enabling my GPU, my CNN would train like normal but now that I've enabled my GPU and tensorflow recognises it, I can no longer train? I get an output stating that it's away to start epoch 1/3 but then immediately moves on to output that it has read the dll files succesfully and never actually trains. Can anyone help me please??
Thanks this's been really helpful!
great video!
I am having CPU and in google for my model it have its having CUDA computing capability. how to verify this in my control panel??
thank you for this!
So this means that i can only use tensorflow CPU? Because I'm on a laptop and it has i guess something like Intel 620 graphic cards. Please tell me about this I'm new to deep learning and very confused by the tensorflow CPU and GPU and all about the Nvidia and all.....
Thanks for this video
0:47 breathes!
First, thank you for your great job!!!
Second, after following carefully all the steps in the clip I get that the number of GPUs is 0.
Do you have any idea what might be the problem and how can I fix it?
Same issue here?
Maybe you forgot to install tensorflow-gpu
I was using a virtual env and was saying that my number of GPUs was 0. Issue was fixed after i installed tensorflow-gpu module and re running the code sample again.
You did, CUDA toolkit correct version? Cudnn correct version? Nvidia drivers correct version? Tensor flow correct version? Restart computer? There’s a table for tensorflow and all the other component versions that work together.
Really helpful, finally got it to to work! Thank you!
You can also try installing it from conda
from terminal:
conda create -n tf tensorflow-gpu
conda activate tf
python
>> import tensorflow as tf
>> print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
However this got me tensorflow gpu 2.1 and not 2.2 (which is the latest when I type this message) so I guess Conda is lagging behind a bit
You, sir, are the mvp!
Thanks for sharing your solution, Jerzy. I'm not aware if there are any differences with conda's tensorflow-gpu package, aside from it not coming with TensorFlow 2.2 at the moment, only 2.1. TensorFlow does not list the conda install on their website as an installation method.
what to do next? it shows 1 gpu available in the terminal but how to open a jupyter notebook from this gpu? when i open the notebook from this specific environment, it shows no gpu. Man im losing my patience for real
I really wish I understood all the details and lingo that you're using. I just need to figure out how to prevent my tensorflow convolutional neural network model from running out of memory. My model has about 2 million trainable parameters, and it also has an early stopping callback with a patience of 3. Everything runs fine when I call "fit" on my model. It ususally runs about 12 epochs before the early stopping callback gets activated. As soon as the callback kicks in, I get the out of memory error. I have been researching this issue for about three months, and I'm no closer to a solution path than when I started. If you could point me in the right direction towards a solution, I will be forever indebted to you.
Better than netflix
Seriously? SERIOUSLY?
I have been stuck on the DLL failing to load for almost 2 whole days. I have finally fixed it by trying random things and uninstalling and installing python a few times, and it randomly started to work. Apparently my recent update to Visual Studio updated the C++ thing and after re-installing python and tensorflow again it got fixed. At least, now that I hear what you said I think that was what fixed it.. If only I'd seen this video sooner..
same here
Hey can you say why its showing 2 variables cuda path and cuda path v12 seperately in environment variable? thanks
may i know how to revert this change? it completely messed up my ide
Thanks a lot miss
I'm trying to use Visual Studio 2019 instead of Jupyter. After hours of searching and trying different things, I _just_ can't figure out how to add the cudnn.lib file to Visual Studio (this is step 5 on the CUDNN installation guide you showed in the video).
at device manager in display adapters only
intel(R) UHD Graphics 620
is coming and nothing like geforce or titan
do i have to purchase geforce graphics card for GPU ??
i love you! Thank you really much for spreading this positive vibes combined with crazy usefull information !! I was thinking about this whole gpu installation for months now and finally did it with your help !!
I was just wondering if it is possible to gain access to the gpu from other than the base environment. Currently on my device it is only finding the gpu in base-env. Somehow it would be really nice if it could be fixed also for my special "dlproject" environment. Is it possible? Did I do something wrong maybe? Thank you very much in advance. I would love to hear from you!! Greetings from Germany
Again thanks for the awesome info. Now in 2021, are there plans to make it run on Apple's new M1 ARM chipset as well? or does it do that already?
How do you enable multi GPU for tensorflow? I have two RTX2070 SUPERs and it recognizes them when I do the print line you mentioned, but when I run training it only seems to be using one core.
Thanks for the fantastic video, I'm going to check out the rest of the videos on this channel now =)
thanks a lot!
Probably the hardest part of the ai/neural network subject
😅
Hey, I followed the steps and even got my program to recognize my GPU but when I try to OCR an image, I still get the "CUDA not available - defaulting to CPU. Note: This module is much faster with a GPU" warning. Any ideas? I'm using Tensorflow & Tensorflow GPU 2.9.0, CuDNN 8.1, and CUDA 11.2; which are all supposedly compatible.
You could use Conda (Anaconda/Miniconda) for setting up the environment. You won't have to go through the process of installing the CUDA Toolkit or Visual Studio or cuDNN, nor downloading any DLLs.
It is as simple as -- $ conda install tensorflow tensorflow-gpu -- on a fresh installation of miniconda/anaconda. Conda handles all of the dependencies.
Thanks for sharing your solution, Shubh. I'm not aware if there are any differences with conda's tensorflow-gpu package. I know that it does not come with TensorFlow 2.2.0 at the moment. Only 2.1.0. TensorFlow does not list the conda install on their website as an installation method.
Oh, okay. But I'm sure the package managers at conda will update to 2.2.0 soon. Anyway, just wanted to mention the awesome dependency solving ability of Conda.
@@existenceisillusion6528 Anaconda installs CUDAToolkit version 10.1.x currently. It might soon be updated to 10.2(Latest as of now).
So is it possible to use this "shortcut"?
After follong this tutorial I can only se XLA_GPU, and my code example does not run on GPU
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),
PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),
PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]
I have a GeForce RTX 3070
I installed CUDA 10.1 and cudnn-10.1-windows10-x64-v8.0.4.30
And use Tersorflow version 2.3.1
you're so beautiful Miss
I did everything as you say but still, it's not working. help 😥😥😥😥
Anyone run into the issue of the Jupyter kernel dying when trying to train a NN after these steps?
yep same here
At 12:59 i have same error and restart not work...
CUDA v10.1
set PATH as well.. any idea :)
This doesn't work while i want to use tf-nightly-gpu.
How to use GPU in tensorflow nightly build version?
Anaconda seemed WAAY easier, but it isn’t working in Jupyter even tho confirmed in my env & using gtx1080(well qualified)… not sure where to go from here.
Hey I am installing tensorflow 2.4.1 can u plz tell me which version of cuda and cudnn supported
Hi Mandy, thanks for the video. I followed all the steps. But tensorflow doesn't identity any GPUs. However it does identify a 'XLA_GPU' . Do you know if there is a difference between XLA_GPU and GPU. thanks
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),
PhysicalDevice(name='/physical_device:XLA_CPU:0', device_type='XLA_CPU'),
PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]
Running tf.test.is_built_with_cuda() returns 'True'
same here
Hey what about TensorRT? Should I download it and add it's location to the PATH? Very helpful video BTW!
what if the type of my driver is not there?, what should I do? Mine is GeForce 1080, but there's nothing like that in the list , should I keep downloading the 1660Ti?
it will definitely be there
This is a very nice easy intro years back, I did these on a Quad i7 MSI laptop. It went well, but my motherboard is reaching 100 deg.C for mesh grid simulation of a surface wave. I have since 2017 sold that laptop and acquired an ASUS ROG G751J laptop that runs on NVIDIA 970M wrote a functional CUDA cores test using CUDA FORTRAN, which also worked well. Some four years back, I ran Tensorflow code from Google, and playing with Python also went well. Now there is the latest NVIDIA Tensor AI Graphics Card. I want to buy that new GPU and use my old ROG G751J to run it like an eGPU or an external GPU. Hopefully, there is some edge connector PCIe Gen.4 somewhere!
I hope you talk about NVIDIA Tensorflow AI A100 GPU's.
Did all the exact same config getting this error
2020-08-13 02:21:14.464920: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-08-13 02:21:14.472264: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2020-08-13 02:21:16.283908: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library nvcuda.dll
2020-08-13 02:21:16.320173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 computeCapability: 6.1
coreClock: 1.493GHz coreCount: 5 deviceMemorySize: 4.00GiB deviceMemoryBandwidth: 104.43GiB/s
2020-08-13 02:21:16.331173: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
2020-08-13 02:21:16.337569: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cublas64_10.dll'; dlerror: cublas64_10.dll not found
2020-08-13 02:21:16.343346: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cufft64_10.dll'; dlerror: cufft64_10.dll not found
2020-08-13 02:21:16.348965: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'curand64_10.dll'; dlerror: curand64_10.dll not found
2020-08-13 02:21:16.356294: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2020-08-13 02:21:16.361823: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cusparse64_10.dll'; dlerror: cusparse64_10.dll not found
2020-08-13 02:21:16.367270: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'cudnn64_7.dll'; dlerror: cudnn64_7.dll not found
2020-08-13 02:21:16.375408: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1753] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
I had an issue while trying to import tf and worked around with os. For anyone working on this i hope this can save u from some headaches.
import os
os.add_dll_directory("C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.7/bin")
import tensorflow as tf
print("Num GPUs Available:", len(tf.config.experimental.list_physical_devices('GPU')))
skip to 3:40 if u want to save time
It will be easy to install through anaconda navigator right why to download and install so many things will there be any difference
hey guys ,. i am getting this error " Ignoring visible gpu device (device: 0, name: GeForce GT 540M, pci bus id: 0000:01:00.0, compute capability: 2.1) with Cuda compute capability 2.1. The minimum required Cuda capability is 3.5.
Num GPUs Available: 0"
can anyone explain what this means.. does this mean that i cant use my gpu for tensorflow?
Hi I found this really helpful ! However, I have one question, the downloaded folder contains more files (it seems like in your video, each of the three folders, bin, include and lib only contain 1 file). Should I copy everything from the downloaded folder to the corresponding directory? Thank you for your help
Yes, copy all :D
@@deeplizard Hi thank you so much! I cannot explain how much help I had gained from you and the reply is so quick!
Amazon video!
I'm currently training my mobilenet model in an environment with specification python 3.6, cuda 10.2, tensorflow 2.0.0
Additionally installing CuDNN is required? Will there be difference in the performance with or without CuDNN?
Thanks in advance.
Yes, cuDNN is required if you want to make use of the GPU while using TensorFlow/Keras.
what if is 11.x and not a specific value?
I downloaded CUDA 11.2 while the supported version is 11.0
should I reinstall it ?
a milion likes
What if we don't have a GPU? Can we follow the course with just a CPU?
Definitely! GPU is not required for the course.
If you have a Windows Laptop you can install Windows Subsystem for Linux which has Cuda and GPU support, they released it recently
I did all this and it was verified that TensorFlow can access my GPU, but I'm not seeing a speed difference. I recorded the time it took to train the network on the cpu and gpu, and they are the same 1 hour and 30 minutes. I thought the GPU would be much faster.
Hey The One - Have a look at this one: deeplizard.com/learn/video/6stDhEA0wFQ
There is a section that discusses this issue: "GPU Can Be Slower Than CPU"
What does that mean for Mac users?
bruh
Hi, thanks for the tutorial.
My display adapters only shows an "Intel(R) UHD Graphics 620", how do I go on to install a GPU?
I have tried DL on my CPU, and it takes TOO long..
Thanks.
u need cuda to run code faster, only nvidia video cards supports cuda, you cannot use gpu
@@user-pn7gq3gk1m thanks for the info.
i installed it many times and dont know why some of the times, i ran into many errors but solution i found is install anaconda make just one environment activate that for once and install tensorflow-gpu there by just writing conda install tensorflow-gpu
==version,it will install all dependencies and tool kits required then i assigned path of that environment as my main python so that i can use jupyter sublime pycharm all with that python (conda environment)
Thanks for sharing your solution, Muhammad. I'm not aware if there are any differences with conda's tensorflow-gpu package. I know that it does not come with TensorFlow 2.2.0 at the moment. Only 2.1.0. TensorFlow does not list the conda install on their website as an installation method.
@@deeplizard thats mine 😅
how to assign this path to the main python 3 path?
@@sanskartewatia4320 set all conda environment python path in environment variables.instead of main python
Hi Mandy,
I am trying to launch the notebook from my Linux docker container and at the same time have access to local folders where I keep my data and notebooks. Is there a way to do this?
Thanks,
Hossam
Yes, use the --volume parameter when you run your container. See the documentation here: docs.docker.com/engine/reference/run/#volume-shared-filesystems