As you may know SpaCy is a great library for processing texts and building your own models for extracting and processing data. One of the
When I tried using en_core_web_trf model for getting entities from english texts, I came to sad outcome - model was very slow when working on CPU. So, I tried to get GPU work as powerhouse for this task.
But trying to enable GPU acceleration may be challenging - drivers, CUDA, pytorch, specific spacy installation...
After few failed attempts I found correct way for enablement of GPU in SpaCy. Hope it will help
Steps:
Install latest stable drivers for your GPU from nVidia website (download).
Install CUDA Toolkit from nVidia website (I used archive). For example, version 11.6 is stable and can be used without hesitation. Reboot.
Let's check correctness of installation
nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Fri_Dec_17_18:28:54_Pacific_Standard_Time_2021
Cuda compilation tools, release 11.6, V11.6.55z
Build cuda_11.6.r11.6/compiler.30794723_0
Install PyTorch from this download link. PyTorch will provide us backend for GPU support.
Check following options:
Build: Stable OS: Windows Package: pip Compute Platform:
Cuda 11.6
After checking list of these options you should be able to copy following string
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu116
Run this string in cmd with admin rights
Check if PyTorch is installed correctly
import torch
torch.cuda.is_available()
True
Install SpaCy from following webpage. Please check following options:
OS – Windows Platform – x86 package manager – pip Hardware
– GPU CUDA - 11.6
Using admin rights run following code in cmd:
pip install -U pip setuptools wheel
pip install -U “spacy[cuda116]”
python -m spacy download en_core_web_trf
Let's check that everything is working just fine:
spacy.require_gpu()
load_nlp = spacy.load("en_core_web_trf")
doc = load_nlp(text)
, where text - any text on English you want to process
Speaking about acceleration - SpaCy has it's own benchmarks
Related
are CUDA and CUDAtoolkit different?
I installed CUDA using this tutorial
I ran the sample too and it worked but to run tensorflow using gpu Do I need to install CUDAtoolkit and cudnn along with the already exisiting CUDA.
while running the trainning job I am getting this message
2023-01-01 00:03:41.818376: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] 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 https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
Tensorflow is very particular about the requirements and configurations for GPUs. You do need to install CUDNN and I believe CudaToolkit might be a requirement to install CUDNN.
I'd recommend following the official documentation to install tensorflow with gpu capabilities. You can see all of that here: https://www.tensorflow.org/install/pip
You can also look into Lambda Labs which has a pretty streamlined way of installing tensroflow GPU specifically on Ubuntu and adding the requirements to your apt repositories for automatic updating. https://lambdalabs.com/lambda-stack-deep-learning-software
I have anaconda3 installed on my windows10 system and I want to install pytorch using anaconda.
I was looking at the official website
https://pytorch.org/get-started/locally/
where I see only the versions 10.2 and 11.1 for cuda are written in the selector. Now, I know I have a cuda-capable gpu in my computer, and the version is NVIDIA GeForce GTX 1660 Ti.
I was also checking nvidia website for finding out the compute capability of my gpu so that I can select the appropriate one in the installation. Here's the link:
https://developer.nvidia.com/cuda-gpus#compute
As you see, the compute capability of all the versions is at most 8.7 ( Mine was 7.5 )
Does this mean I can't use this command for installing pytorch?
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge
Or can I use this command? and they won't cause a problem?
I have two version of CUDA installed on my Ubuntu 16.04 machine: 9.0 and 10.1.
They are located in /usr/local/cuda-9.0 and /usr/local/10.1 respectively.
If I install PyTorch 1.6.0 (which needs CUDA 10.1) via pip (pip install torch==1.6.0), it uses version 9.0 and thus detects no GPUs. I already changed my LD_LIBRARY_PATH to "/usr/local/cuda-10.1/lib64:/usr/local/cuda-10.1/cuda/extras/CUPTI/lib64" but PyTorch is still using CUDA 9.0.
How do I tell PyTorch to use CUDA 10.1?
Prebuilt wheels for torch built with different versions of CUDA are available at torch stable releases page. For example you can install torch v1.9.0 built with CUDA v11.1 like this:
pip install --upgrade torch==1.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
But not all the combinations are available.
I am running CNN on PyTorch. The torch.cuda.is_available() function returned false and no GPU is detected. However, I can run Keras model with GPU. Here is my system information:
OS: Ubuntu 18.04.3
Python 3.7.3 (Conda)
GPU: GTX1080Ti
Nvidia driver: 430.50
When I check nvidia-smi, the output said that the CUDA version is 10.1. However, the nvcc -V command tells me that it is CUDA 9.1.
I downloaded NVIDIA-Linux-x86_64-430.50.run from the official site and install it with command line. I installed CUDA 10.1 using these following command line recommended by the official site:
wget http://developer.download.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.243_418.87.00_linux.run
sudo sh cuda_10.1.243_418.87.00_linux.run
I installed PyTorch through pip install. What is wrong? Thanks in advance!
The default Pytorch 1.2 package depends on CUDA 10.0, but you have CUDA 9.1. The output of nvidia-smi just tells you the maximum CUDA version your GPU supports, nvcc gives the CUDA installed on your system. It seems that your installation of CUDA 10.1 was unsuccessful.
In addition to CUDA 10.0, Pytorch also supports CUDA 9.2 and I've found that the Pytorch package compiled for CUDA 10.0 also works with CUDA 10.1. So you can either upgrade your CUDA installation to 9.2 and install the Pytorch CUDA 9.2 package with
pip3 install torch==1.2.0+cu92 torchvision==0.4.0+cu92 -f https://download.pytorch.org/whl/torch_stable.html
Or get a working installation of CUDA 10.1. There are detailed Linux instructions here. (Note that you may have to remove previous installations of CUDA before installing a new one.)
FYI, this answer is a hack which could mess up your conda env, but may work more easily than installing a fresh env. A consistency-checking tool would be really helpful because of all the people having exactly this problem. Matching anaconda's CUDA version with the system driver and the actual hardware and the other system environment settings is challenging to say the least and almost an art.
I found that Anaconda improperly guesses the CUDA version to use frequently. So I have found the best way to fix this is to surgically uninstall and reinstall just pytorch with pip:
pip uninstall torch
pip install torch
Note that pip calls pytorch torch while conda calls it pytorch.
However, I also found that pip sometimes refuses to reinstall torch because it didn't get rid of the anaconda site package files. If that is the case you can very carefully remove them manually as:
rm -fr $HOME/miniconda3/envs/<ENV>/lib/python3.9/site-packages/torch/
rm -fr $HOME/miniconda3/envs/<ENV>/lib/python3.9/site-packages/torch-*.dist-info/
where should be replaced with your environment name and miniconda might be anaconda or something else depending on your installation.
Be very careful not to delete anything other than the torch files or you may mess something else up. Then you would be best served by installing yet another fresh environment.
After this pip install torch should work and torch.cuda.is_available() should return True. Unless there is another problem... YMMV.
Note that I recommend using miniconda because the full anaconda comes overloaded with packages and I find it quickly gets clogged and broken.
I have tried several solutions which hinted at what to do when the CUDA GPU is available and CUDA is installed but the Torch.cuda.is_available() returns False. They did help but only temporarily, meaning torch.cuda-is_available() reported True but after some time, it switched back to False. I use CUDA 9.0.176 and GTX 1080. What should I do to get the permanent effect?
I tried the following methods:
https://forums.fast.ai/t/torch-cuda-is-available-returns-false/16721/5
https://github.com/pytorch/pytorch/issues/15612
Note: When torch.cuda.is_available() works fine but then at some point switches to False, then I have to restart the computer and then it works again (for some time).
The reason for torch.cuda.is_available() resulting False is the incompatibility between the versions of pytorch and cudatoolkit.
As on Jun-2022, the current version of pytorch is compatible with cudatoolkit=11.3 whereas the current cuda toolkit version = 11.7. Source
Solution:
Uninstall Pytorch for a fresh installation. You cannot install an old version on top of a new version without force installation (using pip install --upgrade --force-reinstall <package_name>.
Run conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch to install pytorch.
Install CUDA 11.3 version from https://developer.nvidia.com/cuda-11.3.0-download-archive.
You are good to go.
Also with torch.cuda.is_available () had false.
But when installing the Nvidia driver to the most updated version 436.48, True is displayed. I previously updated Pytorch to 1.2.0.
I have windows 10 and Anaconda.
Install CUDA 9.1 using apt-get, following the instructions in this link:
https://cryptoandcoffee.com/mining-gems/cuda-9-0-install-ubuntu-16-04-apt-get/
Installed PyTorch using pip:
pip install torchvision ( this will install both torch and torchvision )
Rebooted
Now try it:
~$ python -c 'import torch; print torch.cuda.is_available()'
I saw this issue as well. The reason was the CUDA version used by Pytorch being out of sync with the installed Nvidia driver. As in Joe's answer, the solution was updating the Nvidia drivers. Some other important background info to be aware of:
Each release of CUDA requires a minimum Nvidia driver version (see here for a compatibility table).
You can check your Nvidia driver version with nvidia-smi.
Pytorch comes pre-packaged with a version of CUDA that may be different from the version you installed on your computer.
The CUDA version that you installed manually is the one shows up when you run nvidia-smi. Even if your driver version is compatible with this CUDA version, it may be incompatible with the Pytorch CUDA version.
You can get the Pytorch CUDA version by printing the torch.version.cuda variable in ipython or in a Python program. This is the version that determines the needed Nvidia driver version.