iot-salzburg / gpu-jupyter

GPU-Jupyter: Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.
Apache License 2.0
708 stars 235 forks source link

Memory limited to 3.2Gig #76

Closed SamueLacombe closed 2 years ago

SamueLacombe commented 2 years ago

Hello,

I was really glad to find this docker image. Thanks for your hardwork!

I'm having an issue as I'm limited to 3.5 gig at all time even though there is over 100 Gig available. I searched on the internet and tried to change the config file to set NotebookApp.max_buffer_size = 26843545600 (25gig) but to no avail.

All the information on the internet seem to be related to Google Colab (outside of the NotebookApp.max_buffer_size).. and I have no clue how to remove that limitation.

Thank you, Samuel

mathematicalmichael commented 2 years ago

I'm assuming you mean memory as in RAM?

how are you launching the image? if you're using a jupyterhub for instance, memory limits are configured there regardless of which container is launched.

SamueLacombe commented 2 years ago

Hello,

You are correct, when I say memory I'm referring to RAM.

My current set-up is running using docker-compose on my own ubuntu server. We haven't put any restriction on the RAM in the docker set-up.

Honestly, I'm prety sure the issue is within the config of Jupyter, but I have tried everything aivalable on the internet and nothing avail.

P.S: I assume the error, because the Kernel die everytime I reach around 3.5Gig.

Thanks for the quick reply!

Best regards, Samuel

ChristophSchranz commented 2 years ago

Hi @SamueLacombe , can you post the result of docker info, docker inspect gpu-jupyter and your docker configs/daemon in etc/docker/ or /mnt/c/Users/user/.docker/?

How high is your memory limit in other containers? Do you have the same problem if you start the container with docker (without compose)? See this post and search for docker configuration.

SamueLacombe commented 2 years ago

Hello @ChristophSchranz

So I made a little mistake. The max RAM when the Kernel crash is 3.2G and not 3.5G.

We have only tried Docker, not with Docker Compose.

The other Dockers on the server are not limited in memory and there is plenty of free memory. The server is a threadripper with over 100G of RAM.

here are the ingo you asked:

`docker info

Client: Context: default Debug Mode: false Plugins: app: Docker App (Docker Inc., v0.9.1-beta3) buildx: Docker Buildx (Docker Inc., v0.7.1-docker) scan: Docker Scan (Docker Inc., v0.12.0)

Server: Containers: 87 Running: 20 Paused: 0 Stopped: 67 Images: 779 Server Version: 20.10.12 Storage Driver: overlay2 Backing Filesystem: extfs Supports d_type: true Native Overlay Diff: true userxattr: false Logging Driver: json-file Cgroup Driver: cgroupfs Cgroup Version: 1 Plugins: Volume: local Network: bridge host ipvlan macvlan null overlay Log: awslogs fluentd gcplogs gelf journald json-file local logentries splunk syslog Swarm: inactive Runtimes: io.containerd.runc.v2 io.containerd.runtime.v1.linux nvidia runc Default Runtime: runc Init Binary: docker-init containerd version: 7b11cfaabd73bb80907dd23182b9347b4245eb5d runc version: v1.0.2-0-g52b36a2 init version: de40ad0 Security Options: apparmor seccomp Profile: default Kernel Version: 5.4.0-99-generic Operating System: Ubuntu 20.04.3 LTS OSType: linux Architecture: x86_64 CPUs: 64 Total Memory: 125.7GiB Name: vgrdocker3 ID: MKE3:OELT:RMLI:7AMK:R64B:2OGY:WUX4:AKTJ:26TR:BUMZ:5DRA:NUVX Docker Root Dir: /var/lib/docker Debug Mode: false Registry: https://index.docker.io/v1/ Labels: Experimental: false Insecure Registries: 127.0.0.0/8 Live Restore Enabled: false

WARNING: No swap limit support

docker inspect gpu-jupyter

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"LANG=en_US.UTF-8", "LANGUAGE=en_US.UTF-8", "HOME=/home/jovyan", "XDG_CACHE_HOME=/home/jovyan/.cache/", "JULIA_DEPOT_PATH=/opt/julia", "JULIA_PKGDIR=/opt/julia", "JULIA_VERSION=1.7.1" ], "Cmd": [ "/bin/bash", "-o", "pipefail", "-c", "#(nop) COPY file:c69ac097f4dbd292626d206ab1615e506aa3c9ea18d3933ec4a0f3742a11ff63 in /etc/jupyter/ " ], "Image": "sha256:e8602ccf73af3e5363422d835a52b0eb7fa543c559abb957828647bb38458635", "Volumes": null, "WorkingDir": "/home/jovyan", "Entrypoint": [ "tini", "-g", "--" ], "OnBuild": null, "Labels": { "authors": "Christoph Schranz , Mathematical Michael ", "com.nvidia.cudnn.version": "8.1.1.33", "maintainer": "Christoph Schranz " }, "Shell": [ "/bin/bash", "-o", "pipefail", "-c" ] }, "DockerVersion": "20.10.12", "Author": "", "Config": { "Hostname": "", "Domainname": "", "User": "1000", "AttachStdin": false, "AttachStdout": false, "AttachStderr": false, "ExposedPorts": { "8888/tcp": {} }, "Tty": false, "OpenStdin": false, "StdinOnce": false, "Env": [ 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"MergedDir": "/var/lib/docker/overlay2/5ff90aaa66599b4e5ab38580cedf9cd365ca5e5eeeb8615a2ef219bedecdc76e/merged", "UpperDir": "/var/lib/docker/overlay2/5ff90aaa66599b4e5ab38580cedf9cd365ca5e5eeeb8615a2ef219bedecdc76e/diff", "WorkDir": "/var/lib/docker/overlay2/5ff90aaa66599b4e5ab38580cedf9cd365ca5e5eeeb8615a2ef219bedecdc76e/work" }, "Name": "overlay2" }, "RootFS": { "Type": "layers", "Layers": [ "sha256:9f54eef412758095c8079ac465d494a2872e02e90bf1fb5f12a1641c0d1bb78b", "sha256:1bd360619c259a00adf0ed5fec7c442c460f4a71f1790d4aee30146ef4f6e433", "sha256:6e52c6a02e59fb640733df5021d17e103afdff13fcbab8366338d5959cfcb220", "sha256:ce06dacd3d96432255317713b7d13fbab7d8c7d55e1e45c1398ac45f030cec10", "sha256:4f8af6e687479a548226b3f89a2faa295f933ec63f84b056c2397c08860f4c8e", "sha256:2885ff9ed5c79c51206c47b8e7ca873908f0b1499bad552bcadfea93e481ba4f", "sha256:16266fe134aca2d4837a5404129c6eba15e200d05ebc1361093455b74d2e9bde", "sha256:34c34b9955a837fdc64127bdea59aabe830ec55542cda2dc5f5fa6f3f959ff57", 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"sha256:ed903d8f18b3c596ed85f875368a49b5ed3e96588db3105100ed0b6db9379f54" ] }, "Metadata": { "LastTagTime": "2022-03-10T08:10:01.190829166-05:00" } } ]

cat /etc/docker/daemon.json

{ "dns": ["",""], "dns-search": ["domain.internal"], "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] } } }`

ChristophSchranz commented 2 years ago

Unfortunately, it is not clear to me why this happens.

Have you tried an older tag, e.g. docker pull cschranz/gpu-jupyter:v1.4_cuda-11.0_ubuntu-18.04_slim?

SamueLacombe commented 2 years ago

Hello @ChristophSchranz ,

Your suggestion seem to have fixed the issue!

Much thanks!

SamueLacombe commented 2 years ago

Hello, just to give more information.

I did some more testing to make sure this was not just random and anytime I use the latest version I get the issue with the RAM imitation. Anytime I run with docker pull cschranz/gpu-jupyter:v1.4_cuda-11.0_ubuntu-18.04_slim everything is chill.

I actually closed the issue, but should I leave it open? the issue is technicaly still there.

ChristophSchranz commented 2 years ago

Can you investigate which image is used if you pull the latest verstion? Does this also happen to you, if you tag this version?

SamueLacombe commented 2 years ago

Hello, here is the info:

SamueLacombe commented 2 years ago

Hello here is the info: (and yes its also happen if I tag)

"Id": "sha256:a148d3279e49e8a908359116a35539d07d985a667b6174d9588fe40d04ff60d8",

"RepoTags": ["cschranz/gpu-jupyter:latest"],

"RepoDigests": ["cschranz/gpu-jupyter@sha256:4e58b6d8cb40282977df41433c2d14f4b1236599dfd9a63e0f318768eb1e8841"]