Sometimes I can't communicate with my Nvidia GPUs inside a docker container when I came back to my workplace from home, even though the previously launched process that utilizes GPUs is running well. The running process (training a neural network via Pytorch) is not affected by the disconnection but I cannot launch a new process.
nvidia-smi gives Failed to initialize NVML: Unknown Error and torch.cuda.is_available() returns False likewise.
I met two different cases:
nvidia-smi works fine when it is done at the host machine. In this case, the situation can be solved by restarting the docker container via docker stop $MYCONTAINER followed by docker start $MYCONTAINER at the host machine.
nvidia-smi doesn't work at the host machine nor nvcc --version, throwing Failed to initialize NVML: Driver/library version mismatch and Command 'nvcc' not found, but can be installed with: sudo apt install nvidia-cuda-toolkit error. Strange point is that the current process still runs well. In this case, installing the driver again or rebooting the machine solves the problem.
However, these solutions require stopping all current processes. It would be unavailable when I should not stop the current process.
Does somebody has suggestion for solving this situation?
Many thanks.
(sofwares)
Docker version: 20.10.14, build a224086
OS: Ubuntu 22.04
Nvidia driver version: 510.73.05
CUDA version: 11.6
(hardwares)
Supermicro server
Nvidia A5000 * 8
(pic1) nvidia-smi not working inside of a docker container, but worked well on the host machine.
(pic2) nvidia-smi works after restarting a docker container, which is the case 1 I mentioned above
For the problem of Failed to initialize NVML: Unknown Error and having to restart the container, please see this ticket and post your system/package information there as well:
https://github.com/NVIDIA/nvidia-docker/issues/1671
There's a workaround on the ticket, but it would be good to have others post their configuration to help fix the issue.
Downgrading containerd.io to 1.6.6 works as long as you specify no-cgroups = true in /etc/nvidia-container-runtime/config.toml and specify the devices to docker run like docker run --gpus all --device /dev/nvidia0:/dev/nvidia0 --device /dev/nvidia-modeset:/dev/nvidia-modeset --device /dev/nvidia-uvm:/dev/nvidia-uvm --device /dev/nvidia-uvm-tools:/dev/nvidia-uvm-tools --device /dev/nvidiactl:/dev/nvinvidiactl --rm -it nvidia/cuda:11.4.2-base-ubuntu18.04 bash
so sudo apt-get install -y --allow-downgrades containerd.io=1.6.6-1 and sudo apt-mark hold containerd.io to prevent the package from being updated. So do that, edit the config file, and pass all of the /dev/nvidia* devices in to docker run.
For the Failed to initialize NVML: Driver/library version mismatch issue, that is caused by the drivers updating but you haven't rebooted yet. If this is a production machine, I would also hold the driver package to stop that from auto-updating as well. You should be able to figure out the package name from something like sudo dpkg --get-selections "*nvidia*"
Related
I followed the instructions to install the nvidia-docker2 from the official documentation https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html
Whenever I run their test example:
sudo docker run --rm --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi
I still get the error:
docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]]. 3
I rebooted but still no effect.
I am on Ubuntu 22.04 with my nvidia drivers updated.
Nvidia-smi works on the machine but not working using docker
EDIT (SOLVED): Finally I found out what was going on.
When reinstalling, it was working, however if rebooting, it was going again to a previous state where it was not working.
This was due to the installation of another docker service installed using "snapd" so I had to purge completely docker:
sudo snap remove docker and after I could "Reinstall everything" and it finally is stable, even after rebooting
Unfortunately I was not able to "Fix" properly the issue so I purge all docker package and all nvidia container packages and reinstalled everything and now it works!!
Good old methods work fine :)
you need to restart the docker daemon :
sudo systemctl restart docker
if the problem still occurs install the nvidia-container-toolkit then restart docker daemon.
I am trying to run a private tangle on my computer through linux docker containers.
Therefore I followed the guide over at https://wiki.iota.org/chrysalis-docs/tutorials/one_click_private_tangle
Every step succeeded up until we tried to execute
./private_tangle.sh install
This reports
Error 1
as seen in the screenshot below:
We do net get any further information, is anyone familiar with this error, or has any clue how to get some more information on the error so that we can at least have a clue where to look?
Some further information:
After executing docker ps -a we see that not a single container is running.
I am running on a windows 10 machine
I execute the commands from within ubuntu (version 20.04)
Ubuntu, docker-desktop and docker-desktop-data are all running WSL2
Docker integration with ubuntu is activated
I thought the error could maybe come from no hornet node initially being installed, so I installed a hornet node successfully, according the guide that https://wiki.iota.org/chrysalis-docs/tutorials/one_click_private_tangle. This changed nothing to the Error.
The version of docker and docker-compose are compliant with the requirements
If any more details are needed to help me solve this problem, please let me know.
I used the documentation (https://wiki.iota.org/chrysalis-docs/tutorials/one_click_private_tangle) to install these containers on my local ubuntu 18.04.
My docker version is: 20.10.12
And docker-compose version is: 1.29.2
By following the steps of the tutorial I managed to successfully start all of the containers without trouble.
My guess here would be that the permission of the 'private-tangle.sh' are not correct or that there is permission problem on the docker level.
You should start with checking the permission level of the private-tangle.sh script by using $ls -l
Here is my output -rwxrwxr-x 1 ben ben 9413 Jan 11 11:28 private-tangle.sh
It could also be due to the docker rights if you have to use sudo when executing a docker command it will give some troubles when executing the script.
You need to add yourself to a docker group to be able to run docker commands without sudo. You can do this by running sudo usermod -aG docker $USER with damiaan-vh as $user.
Solution from source https://stackoverflow.com/posts/70665394/edit
Suggesting to downgrade ubuntu version to 18.04 for more stable version.
For reinstalling the docker and docker-compose programs follow this documentations
(docker: https://docs.docker.com/engine/install/ubuntu/ )
(docker-compose: https://docs.docker.com/compose/install/ )
I'm a complete newcomer to Docker, so the following questions might be a bit naive, but I'm stuck and I need help.
I'm trying to reproduce some results in research. The authors just released code along with a specification of how to build a Docker image to reproduce their results. The relevant bit is copied below:
I believe I installed Docker correctly:
$ docker --version
Docker version 19.03.13, build 4484c46d9d
$ sudo docker run hello-world
Hello from Docker!
This message shows that your installation appears to be working correctly.
To generate this message, Docker took the following steps:
1. The Docker client contacted the Docker daemon.
2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
(amd64)
3. The Docker daemon created a new container from that image which runs the
executable that produces the output you are currently reading.
4. The Docker daemon streamed that output to the Docker client, which sent it
to your terminal.
To try something more ambitious, you can run an Ubuntu container with:
$ docker run -it ubuntu bash
Share images, automate workflows, and more with a free Docker ID:
https://hub.docker.com/
For more examples and ideas, visit:
https://docs.docker.com/get-started/
However, when I try checking that my nvidia-docker installation was successful, I get the following error:
$ sudo docker run --gpus all --rm nvidia/cuda:10.1-base nvidia-smi
docker: Error response from daemon: OCI runtime create failed: container_linux.go:349: starting container process caused "process_linux.go:449: container init caused \"process_linux.go:432: running prestart hook 0 caused \\\"error running hook: exit status 1, stdout: , stderr: nvidia-container-cli: initialization error: nvml error: driver not loaded\\\\n\\\"\"": unknown.
It looks like the key error is:
nvidia-container-cli: initialization error: nvml error: driver not loaded
I don't have a GPU locally and I'm finding conflicting information on whether CUDA needs to be installed before NVIDIA Docker. For instance, this NVIDIA moderator says "A proper nvidia docker plugin installation starts with a proper CUDA install on the base machine."
My questions are the following:
Can I install NVIDIA Docker without having CUDA installed?
If so, what is the source of this error and how do I fix it?
If not, how do I create this Docker image to reproduce the results?
Can I install NVIDIA Docker without having CUDA installed?
Yes, you can. The readme states that nvidia-docker only requires NVIDIA GPU driver and Docker engine installed:
Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed
If so, what is the source of this error and how do I fix it?
That's either because you don't have a GPU locally or it's not NVIDIA, or you messed up somewhere when installed drivers. If you have a CUDA-capable GPU I recommend using NVIDIA guide to install drivers. If you don't have a GPU locally, you can still build an image with CUDA, then you can move it somewhere where there is a GPU.
If not, how do I create this Docker image to reproduce the results?
The problem is that even if you manage to get rid of CUDA in Docker image, there is software that requires it. In this case fixing the Dockerfile seems to me unnecessary - you can just ignore Docker and start fixing the code to run it on CPU.
I think you need
ENV NVIDIA_VISIBLE_DEVICES=void
then
RUN your work
finally
ENV NVIDIA_VISIBLE_DEVICES=all
I use singularity and I need to install a nvidia driver in my singularity container to do some deep learning with a gtx 1080.
This singularity image is created from a nvidia docker from here:
https://ngc.nvidia.com/catalog/containers/nvidia:kaldi and converted to a singularity container.
There was no nvidia drivers I think because nvidia-smi was not found before I install the driver.
I did the following commmands :
add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
apt install nvidia-418
after that I wanted to see if the driver was well installed, I did the command :
nvidia-smi
which return : Failed to initialize NVML: Driver/library version mismatch
I searched about how to solve this error and found this topic :
NVIDIA NVML Driver/library version mismatch
One answer says to do the command :
lsmod | grep nvidia
and then to rmmod on each except nvidia and finally to rmmod nvidia.
rmmod drm
But when I do this, as the topic excepted it, I have the error :
rmmod: ERROR: Module nvidia is in use.
The topic says to tap lsof /dev/nvidia*, and to kill the process that use the module, but I see nothing with drm written, and it seems to be a very bad idea to kill the process (Xorg, gnome-she).
Here is the answer to the command lsof /dev/nvidia*, followed by the command lsmod | grep nvidia, and then rmmod drm
Rebooting the computer also didn't work.
what should I do to manage using nvidia-smi and be able to use my GPU from inside the singularity container ?
Thank you
You may need to do the above steps in the host OS and not in the container itself. /dev is mounted into the container as is and still subject to use by the host, though the processes are run in a different userspace.
thank you for your answer.
I wanted to install the GPU driver in the singularity container because when inside the container, I wasn't able to use the GPU (nvidia-smi : command not found) while outside of the container I could use nvidia-smi.
You are right, the driver should be installed outside of the container, I wanted to install it in the container to avoid my problem of not having access to the driver from inside the container.
Now I found the solution : To use GPU from inside the singularity container, you must add --nv when calling the container.
example :
singularity exec --nv singularity_container.simg ~/test_gpu.sh
or
singularity shell --nv singularity_container.simg
When you add --nv, the container will have access to the nvidia driver and nvidia-smi will work.
Without this you will not be able to use GPU, nvidia-smi will not work.
I am trying to get docker working on my sys
However, not able to
Steps to reproduce the issue that I am facing:
Installed EPEL on 6.5 RHEL
Installed docker-io
Able to run "docker" command
When running "docker run -i -t fedora /bin/bash" command using without root, getting below error:
FATA[0000] Post http:///var/run/docker.sock/v1.17/containers/create:
dial unix /var/run/docker.sock
http:///var/run/docker.sock/v1.17/containers/create:%20dial%20unix%20/var/run/docker.sock :
permission denied.
Are you trying to connect to a TLS-enabled daemon without TLS?
However, with root getting diff error like below for the same command:
FATA[0000] Cannot connect to the Docker daemon. Is 'docker -d' running on this host?
A RHEL 6.5, also termed Update 5, is from 21 November 2013 and comes with kernel 2.6.32-431.
That seems quite an old kernel for docker to be installed and run successfully. Docker would need ideally a 3.10+ kernel.
Although Adrian Mouat mentions in the comments that the Red Hat Enterprise Linux 6.5 (64-bit) or later is supported
You will need 64 bit RHEL 6.5 or later, with a RHEL 6 kernel version 2.6.32-431 or higher as this has specific kernel fixes to allow Docker to work.
So make sure the docker daemon is started:
sudo service docker start
Then try some sudo docker commands:
sudo docker run -i -t fedora /bin/bash
The doc mentions:
If you get a Cannot start container error mentioning SELinux or permission denied, you may need to update the SELinux policies.
This can be done using sudo yum upgrade selinux-policy and then rebooting.