I am installing CUDA from this link.
Though it is a CUDA SDK 9.2, when I check the version installed using nvcc --version, I get the following results:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17
I am new to CUDA and wanted to check if this is expected. Should I expect 9.2 as the CUDA version post installation?
FYI - GPU is GeForce GTX 1080 Ti
You need to follow the official guide step by step，click here to check if the toolkit is installed correctly. Also,the post-installation-actions must be taken into consideration,click here to get more info.
I had occured the same condition u mention above, In my case, I add this path to the PATH:
$ export PATH=/usr/local/cuda-9.2/bin${PATH:+:${PATH}}
The best solution should be to modify the corresponding profile file, like this:
vim /etc/profile
Add export PATH=/usr/local/cuda-9.2/bin${PATH:+:${PATH}} to the end of the file
reboot
Good Luck.
when asking for
nvidia-smi
it gives this error:
Failed to initialize NVML: GPU access blocked by the operating system
other information:
$ nvcc --verion
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Mon_Feb_16_22:59:02_CST_2015
Cuda compilation tools, release 7.0, V7.0.27
and also:
$ lspci | grep -i nvidia
01:00.0 VGA compatible controller: NVIDIA Corporation GF108M [GeForce GT 425M] (rev a1)
01:00.1 Audio device: NVIDIA Corporation GF108 High Definition Audio Controller (rev a1)
Having searched a lot in the internet I couldn't find a way to solve this problem.
when I use ipython notebook and want to run Caffe framework it gives this error:
Check failed: error == cudaSuccess (38 vs. 0) no CUDA-capable device is detected
I noticed that after CUDA installation restarting Ubuntu works, and now I see the GPU details output by nvidia-smi
If you believe that both CUDA and graphics driver are installed correctly, but you still cannot make your GPU to be detected, the problem might be in that you are using mobile Nvidia graphics on Optimus-enabled laptop on Linux.
You could either:
change your application to properly detect GPUs behind Optimus. See documentation here
or to run your application via Bumblebee (and primus)
WSL user here. Running nvidia-smi on either Windows and WSL failed. Reinstalling the Nvidia for WSL driver, on the Windows side, fixed the problem. The problem was created when installing CUDA Toolkit and CUDNN broke the Nvidia for WSL driver.
I had the same problem. It was happened because of installing a nvidia toolkit (I am not sure). According to this website (which has useful ideas)
I found that cuda driver version in the cuda installer and host was incompatible. (host : 367.57 , installer: 375.26 , At first I could not check the installer version because all the versions was 367.57, but when I reinstall cuda by run file, I found it)
So, I uninstalled cuda and nvidia completely and install cuda again by this help. At first in the installation process I got some errors which I found, nvidia has not completely gone. After uninstalling completely, I installed cuda and now I can run "sudo nvidia-smi" without problem.
I got the error failed to initialize NVML: Driver/Library version mismatch. And nvidia-smi failed to print any info. I tried to find if there were other versions of nvidia driver installed in my ubuntu. But I just found nvidia-driver-390. In the end, reboot helped me solve the problem.
I successfully installed caffe on my dual-boot laptop (GTX 860M, Windows 7 + Ubuntu 14.04.2). All the tests were successfully passed. When I restarted, however, the ubuntu got stuck on the opening screen (the one with ubuntu logo and five red dots). Don't know what to do with it.
Has anyone run into the same issue before? I reckon something is wrong with graphic card driver booting. I installed newest CUDA 7 Toolkit with nvidia drivers built inside. Since all tests were passed before I restarted, it seems that the driver would work once successfully booted.
the stuck screen is like this: http://i.stack.imgur.com/pRtEF.jpg
I had a similar issue when trying to install Caffe on my system. The steps below worked for me, but it has at least one known issue (documented below).
I'm not sure what precisely caused this problem, but it surely has something to do with the Nvidia Driver and Cuda Toolkit installation and is not caused by Caffe.
After completing the steps below, I've been able to successfully install Caffe on my system with the following tutorials and guides:
Official Install Guide
Github Install Guide
Update
Recently, I had the exact same problem trying to make Cuda 7.5 work on Ubuntu 14.04; this approach also solved that problem. Specs:
CPU: Intel Core i7-4700MQ (4x 2.40 GHz with Hyperthreading)
GPU: NVidia GT 940M
RAM: 8 GB
HDD: 52.7 GB (of which 6.7 GB used after installation)
INSTALL NVIDIA DRIVER AND CUDA ON UBUNTU 14.04
Source: ubuntuforums.org/showthread.php?t=2246526
!! Known Issues !!
After the system has been suspended (or hibernated, not confirmed), all applications using the Nvidia Driver and Cuda 6.5 Toolkit will freeze. When this happens, the command sudo shutdown -r now will print the reboot message but nothing will happen.
Executed and tested on a fresh 64-bit Ubuntu 14.04 install with the following hardware specifications:
CPU: Intel Core i5-2410m (2x 2.30 GHz with Hyperthreading)
GPU: NVidia GT 540M
RAM: 6 GB
HDD: 52.7 GB (of which 8.6 GB used after installation)
The following command was executed before installation:
sudo apt-get -y build-essential vim git llvm clang
The following steps resulted in a stable system with the latest Nvidia Driver and Cuda 6.5 Toolkit installed:
Remove all traces of previous/legacy Nvidia Drivers and Cuda Toolkits or perform a fresh Ubuntu 14.04 install.
Download the latest Nvidia Driver .run file for Ubuntu 14.04 and your system specifications to the ~/Downloads directory.
e.g.: NVIDIA-Linux-x86_64-346.35.run
Download the latest Cuda 6.5 Toolkit .run file for Ubuntu 14.04 and your system specifications to the ~/Downloads directory.
e.g.: cuda_6.5.14_linux_64.run
Blacklist the 'nouveau' Driver by appending the following lines to /etc/modprobe.d/blacklist.conf (nouveau is a free open-source driver for Nvidia cards, it is the default for Ubuntu 14.04):
blacklist nouveau
options nouveau modeset=0
Reboot the system, do NOT log in but drop to the terminal with CTRL+ALT+F1
Kill lightdm (replace 'lightdm' with your own Display Manager if you have changed it, lightdm is the default for Ubuntu 14.04):
sudo service lightdm stop
The next step is critical, make sure to check twice before continuing!
Run the Nvidia Driver installer with the --no-opengl-files option (the option prevents OpenGL files from being overwritten; without this option, Unity would not function properly and the screen would freeze after login):
sudo chmod +x ~/Downloads/NVIDIA-Linux-x68_64-346.35.run
sudo ~/Downloads/NVIDIA-Linux-x68_64-346.35.run --no-opengl-files
Accept the EULA and acknowledge all further warnings but deny to install anything extra.
Reboot and login to the desktop, verify with the 'Additional Drivers' (System Settings > Software & Updates > Additional Drivers) utility that the manually installed driver is in use.
Open a terminal and install the Cuda 6.5 Toolkit:
sudo chmod +x ~/Downloads/cuda_6.5.14_linux_64.run
sudo ~/Downloads/cuda_6.5.14_linux_64.run
Accept the EULA, do NOT install the driver, install the Toolkit and the Examples (if you want to), leave all default directories in place.
Add the Cuda 6.5 Toolkit environment variables by appending the following lines to ~/.bashrc:
# For 32-bit systems, append these:
export PATH=$PATH:/usr/local/cuda-6.5/bin
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib
# For 64-bit systems, append these:
export PATH=$PATH:/usr/local/cuda-6.5/bin
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64
The Nvidia Driver and Cuda 6.5 Toolkit should now be correctly installed.
Optional: confirm your Nvidia Driver and Cuda 6.5 Toolkit installation.
Confirm the Nvidia Driver installation by running the following command:
nvidia-smi
Confirm the Cuda Compiler installation by running the following command:
nvcc -V
Confirm everything works by building and running the optionally installed Cuda Examples: (build-essential is required to use 'make')
sudo apt-get install -y build-essential
cd ~/NVIDIA_CUDA-6.5_SAMPLES/1_Utilities/deviceQuery
make
./deviceQuery
cd ~/NVIDIA_CUDA-6.5_SAMPLES/1_Utilities/bandwidthTest
make
./bandwidthTest
This problem is not related to caffe.
The problem is that the nVidia driver that is installed from the ubuntu software center does not support your card.
Uninstall any nvidia package (sudo apt-get purge nvidia-*) and install the latest driver version from the nvidia website.
I recommend you to change the cuda 7.5 ubuntu 15.04 version. I try it on the ubuntu 14.04, it solves this problem. And when I install cuda 7.5 ubuntu 14.04 version on ubuntu 14.04 I countered the exactly problem.
I just upgraded from CUDA 4.2 to CUDA 5.0. Not surprisingly, the library that used to be named libcudart.so.4 is now called libcudart.so.5.0. After recompiling my code with nvcc 5.0, and attempting to running the code, I got this message:
./main: error while loading shared libraries: libcudart.so.4: cannot open shared object file: No such file or directory
Yeah, you stupid system, I know there's no libcudart.so.4. That's because it's now called libcudart.so.5.0. Why is it looking for libcudart.so.4 instead of libcudart.so.5.0, and how can I fix it?
What I've tried so far:
I've checked that all my paths are in order. These environment variables are set:
export PATH=$PATH:/usr/local/cuda/bin:/usr/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib:/usr/local/cuda/lib64:/lib
#note: /usr/local/cuda is symlinked to /usr/local/cuda-5.0
I've verified that libcudart.so.5.0 can be found in one of the LD_LIBRARY_PATH directories.
I recompiled my CUDA application with the the CUDA 5.0 version of nvcc. I successfully compiled and ran my application on an other machine with CUDA 4.2, and on an other machine with CUDA 4.0.
I confirmed that nvcc is really on version 5.0:
user#host$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2012 NVIDIA Corporation
Built on Fri_Sep_21_17:28:58_PDT_2012
Cuda compilation tools, release 5.0, V0.2.122
I'd like to get this question off the unanswered list, and I don't think #Jared Hoberock will mind, so I'm going to post his comment as an answer. If there's a concern and Jared or solvingPuzzles posts an answer, I'll delete mine (assuming it's not accepted -- I can't delete an accepted answere AFAIK).
nvcc seems to be statically linking against libcudart.a version 4.
Somewhere in your lib path, it seems that nvcc is finding an old libcudart.a, which needs to be removed.
For other readers, it's probably just sufficient to find all instances of libcudart.* on the system and delete any that don't match your desired CUDA version (assuming you're not trying to run a machine with multiple CUDA versions available -- in that case, the library paths for both compiling and running have to be managed appropriately)
Is there any quick command or script to check for the version of CUDA installed?
I found the manual of 4.0 under the installation directory but I'm not sure whether it is of the actual installed version or not.
As Jared mentions in a comment, from the command line:
nvcc --version
(or /usr/local/cuda/bin/nvcc --version) gives the CUDA compiler version (which matches the toolkit version).
From application code, you can query the runtime API version with
cudaRuntimeGetVersion()
or the driver API version with
cudaDriverGetVersion()
As Daniel points out, deviceQuery is an SDK sample app that queries the above, along with device capabilities.
As others note, you can also check the contents of the version.txt using (e.g., on Mac or Linux)
cat /usr/local/cuda/version.txt
However, if there is another version of the CUDA toolkit installed other than the one symlinked from /usr/local/cuda, this may report an inaccurate version if another version is earlier in your PATH than the above, so use with caution.
On Ubuntu Cuda V8:
$ cat /usr/local/cuda/version.txt
You can also get some insights into which CUDA versions are installed with:
$ ls -l /usr/local | grep cuda
which will give you something like this:
lrwxrwxrwx 1 root root 9 Mar 5 2020 cuda -> cuda-10.2
drwxr-xr-x 16 root root 4096 Mar 5 2020 cuda-10.2
drwxr-xr-x 16 root root 4096 Mar 5 2020 cuda-8.0.61
Given a sane PATH, the version cuda points to should be the active one (10.2 in this case).
NOTE: This only works if you are willing to assume CUDA is installed under /usr/local/cuda (which is true for the independent installer with the default location, but not true e.g. for distributions with CUDA integrated as a package). Ref: comment from #einpoklum.
[Edited answer. Thanks for everyone who corrected it]
If you run
nvidia-smi
You should find the CUDA Version highest CUDA version the installed driver supports on the top right corner of the comand's output. At least I found that output for CUDA version 10.0 e.g.,
For CUDA version:
nvcc --version
Or use,
nvidia-smi
For cuDNN version:
For Linux:
Use following to find path for cuDNN:
$ whereis cuda
cuda: /usr/local/cuda
Then use this to get version from header file,
$ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
For Windows,
Use following to find path for cuDNN:
C:\>where cudnn*
C:\Program Files\cuDNN7\cuda\bin\cudnn64_7.dll
Then use this to dump version from header file,
type "%PROGRAMFILES%\cuDNN7\cuda\include\cudnn.h" | findstr CUDNN_MAJOR
If you're getting two different versions for CUDA on Windows -
Different CUDA versions shown by nvcc and NVIDIA-smi
Use the following command to check CUDA installation by Conda:
conda list cudatoolkit
And the following command to check CUDNN version installed by conda:
conda list cudnn
If you want to install/update CUDA and CUDNN through CONDA, please use the following commands:
conda install -c anaconda cudatoolkit
conda install -c anaconda cudnn
Alternatively you can use following commands to check CUDA installation:
nvidia-smi
OR
nvcc --version
If you are using tensorflow-gpu through Anaconda package (You can verify this by simply opening Python in console and check if the default python shows Anaconda, Inc. when it starts, or you can run which python and check the location), then manually installing CUDA and CUDNN will most probably not work. You will have to update through conda instead.
If you want to install CUDA, CUDNN, or tensorflow-gpu manually, you can check out the instructions here https://www.tensorflow.org/install/gpu
Other respondents have already described which commands can be used to check the CUDA version. Here, I'll describe how to turn the output of those commands into an environment variable of the form "10.2", "11.0", etc.
To recap, you can use
nvcc --version
to find out the CUDA version.
I think this should be your first port of call.
If you have multiple versions of CUDA installed, this command should print out the version for the copy which is highest on your PATH.
The output looks like this:
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Thu_Jun_11_22:26:38_PDT_2020
Cuda compilation tools, release 11.0, V11.0.194
Build cuda_11.0_bu.TC445_37.28540450_0
We can pass this output through sed to pick out just the MAJOR.MINOR release version number.
CUDA_VERSION=$(nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p')
If nvcc isn't on your path, you should be able to run it by specifying the full path to the default location of nvcc instead.
/usr/local/cuda/bin/nvcc --version
The output of which is the same as above, and it can be parsed in the same way.
Alternatively, you can find the CUDA version from the version.txt file.
cat /usr/local/cuda/version.txt
The output of which
CUDA Version 10.1.243
can be parsed using sed to pick out just the MAJOR.MINOR release version number.
CUDA_VERSION=$(cat /usr/local/cuda/version.txt | sed 's/.* \([0-9]\+\.[0-9]\+\).*/\1/')
Note that sometimes the version.txt file refers to a different CUDA installation than the nvcc --version. In this scenario, the nvcc version should be the version you're actually using.
We can combine these three methods together in order to robustly get the CUDA version as follows:
if nvcc --version 2&> /dev/null; then
# Determine CUDA version using default nvcc binary
CUDA_VERSION=$(nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p');
elif /usr/local/cuda/bin/nvcc --version 2&> /dev/null; then
# Determine CUDA version using /usr/local/cuda/bin/nvcc binary
CUDA_VERSION=$(/usr/local/cuda/bin/nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p');
elif [ -f "/usr/local/cuda/version.txt" ]; then
# Determine CUDA version using /usr/local/cuda/version.txt file
CUDA_VERSION=$(cat /usr/local/cuda/version.txt | sed 's/.* \([0-9]\+\.[0-9]\+\).*/\1/')
else
CUDA_VERSION=""
fi
This environment variable is useful for downstream installations, such as when pip installing a copy of pytorch that was compiled for the correct CUDA version.
python -m pip install \
"torch==1.9.0+cu${CUDA_VERSION/./}" \
"torchvision==0.10.0+cu${CUDA_VERSION/./}" \
-f https://download.pytorch.org/whl/torch_stable.html
Similarly, you could install the CPU version of pytorch when CUDA is not installed.
if [ "$CUDA_VERSION" = "" ]; then
MOD="+cpu";
echo "Warning: Installing CPU-only version of pytorch"
else
MOD="+cu${CUDA_VERSION/./}";
echo "Installing pytorch with $MOD"
fi
python -m pip install \
"torch==1.9.0${MOD}" \
"torchvision==0.10.0${MOD}" \
-f https://download.pytorch.org/whl/torch_stable.html
But be careful with this because you can accidentally install a CPU-only version when you meant to have GPU support.
For example, if you run the install script on a server's login node which doesn't have GPUs and your jobs will be deployed onto nodes which do have GPUs. In this case, the login node will typically not have CUDA installed.
On Ubuntu :
Try
$ cat /usr/local/cuda/version.txt
or
$ cat /usr/local/cuda-8.0/version.txt
Sometimes the folder is named "Cuda-version".
If none of above works, try going to
$ /usr/local/
And find the correct name of your Cuda folder.
Output should be similar to:
CUDA Version 8.0.61
If you have installed CUDA SDK, you can run "deviceQuery" to see the version of CUDA
If you have PyTorch installed, you can simply run the following code in your IDE:
import torch
print(torch.version.cuda)
On Windows 10, I found nvidia-smi.exe in 'C:\Program Files\NVIDIA Corporation\NVSMI'; after cd into that folder (was not in the PATH in my case) and '.\nvidia-smi.exe' it showed
You might find CUDA-Z useful, here is a quote from their Site:
"This program was born as a parody of another Z-utilities such as CPU-Z and GPU-Z. CUDA-Z shows some basic information about CUDA-enabled GPUs and GPGPUs. It works with nVIDIA Geforce, Quadro and Tesla cards, ION chipsets."
http://cuda-z.sourceforge.net/
On the Support Tab there is the URL for the Source Code: http://sourceforge.net/p/cuda-z/code/ and the download is not actually an Installer but the Executable itself (no installation, so this is "quick").
This Utility provides lots of information and if you need to know how it was derived there is the Source to look at. There are other Utilities similar to this that you might search for.
One can get the cuda version by typing the following in the terminal:
$ nvcc -V
# below is the result
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Nov__3_21:07:56_CDT_2017
Cuda compilation tools, release 9.1, V9.1.85
Alternatively, one can manually check for the version by first finding out the installation directory using:
$ whereis -b cuda
cuda: /usr/local/cuda
And then cd into that directory and check for the CUDA version.
We have three ways to check Version:
In my case below is the output:-
Way 1:-
cat /usr/local/cuda/version.txt
Output:-
CUDA Version 10.1.243
Way2:-
nvcc --version
Output:-
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2017 NVIDIA Corporation
Built on Fri_Nov__3_21:07:56_CDT_2017
Cuda compilation tools, release 9.1, V9.1.85
Way3:-
/usr/local/cuda/bin/nvcc --version
Output:-
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
Way4:-
nvidia-smi
NVIDIA-SMI 450.36.06 Driver Version: 450.36.06 CUDA Version: 11.0
Outputs are not same. Don't know why it's happening.
First you should find where Cuda installed.
If it's a default installation like here the location should be:
for ubuntu:
/usr/local/cuda
in this folder you should have a file
version.txt
open this file with any text editor or run:
cat version.txt
from the folder
OR
cat /usr/local/cuda/version.txt
On Windows 11 with CUDA 11.6.1, this worked for me:
cat "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.6\version.json"
if nvcc --version is not working for you then use cat /usr/local/cuda/version.txt
After installing CUDA one can check the versions by: nvcc -V
I have installed both 5.0 and 5.5 so it gives
Cuda Compilation Tools,release 5.5,V5.5,0
This command works for both Windows and Ubuntu.
Apart from the ones mentioned above, your CUDA installations path (if not changed during setup) typically contains the version number
doing a which nvcc should give the path and that will give you the version
PS: This is a quick and dirty way, the above answers are more elegant and will result in the right version with considerable effort
If you are running on linux:
dpkg -l | grep cuda
If you have multiple CUDA installed, the one loaded in your system is CUDA associated with "nvcc". Therefore, "nvcc --version" shows what you want.
Open a terminal and run these commands:
cd /usr/local/cuda/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery
You can get the information of CUDA Driver version, CUDA Runtime Version, and also detailed information for GPU(s). An image example of the output from my end is as below.
You can find the image here.
i get /usr/local - no such file or directory. Though nvcc -V gives
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2016 NVIDIA Corporation
Built on Sun_Sep__4_22:14:01_CDT_2016
Cuda compilation tools, release 8.0, V8.0.44
Found mine after:
whereis cuda
at
cuda: /usr/lib/cuda /usr/include/cuda.h
with
nvcc --version
CUDA Version 9.1.85
Using tensorflow:
import tensorflow as tf
from tensorflow.python.platform import build_info as build
print(f"tensorflow version: {tf.__version__}")
print(f"Cuda Version: {build.build_info['cuda_version']}")
print(f"Cudnn version: {build.build_info['cudnn_version']}")
tensorflow version: 2.4.0
Cuda Version: 11.0
Cudnn version: 8
Programmatically with the CUDA Runtime API C++ wrappers (caveat: I'm the author):
auto v1 = cuda::version::maximum_supported_by_driver();
auto v2 = cuda::version::runtime();
This gives you a cuda::version_t structure, which you can compare and also print/stream e.g.:
if (v2 < cuda::version_t{ 8, 0 } ) {
std::cerr << "CUDA version " << v2 << " is insufficient." std::endl;
}
You can check the version of CUDA using
nvcc -V
or you can use
nvcc --version
or You can check the location of where the CUDA is using
whereis cuda
and then do
cat location/of/cuda/you/got/from/above/command
On my cuda-11.6.0 installation, the information can be found in /usr/local/cuda/version.json. It contains the full version number (11.6.0 instead of 11.6 as shown by nvidia-smi.
The information can be retrieved as follows:
python -c 'import json; print(json.load(open("/usr/local/cuda/version.json"))["cuda"]["version"])'
If there is a version mismatch between nvcc and nvidia-smi then different versions of cuda are used as driver and run time environemtn.
To ensure same version of CUDA drivers are used what you need to do is to get CUDA on system path.
First run whereis cuda and find the location of cuda driver.
Then go to .bashrc and modify the path variable and set the directory precedence order of search using variable 'LD_LIBRARY_PATH'.
for instance
$ whereis cuda
cuda: /usr/lib/cuda /usr/include/cuda.h /usr/local/cuda
CUDA is installed at /usr/local/cuda, now we need to to .bashrc and add the path variable as:
vim ~/.bashrc
export PATH="/usr/local/cuda/bin:${PATH}"
and after this line set the directory search path as:
export LD_LIBRARY_PATH="/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
Then save the .bashrc file. And refresh it as:
$ source ~/.bashrc
This will ensure you have nvcc -V and nvidia-smi to use the same version of drivers.