OSError: [Errno 28] No space left on device Docker, but I have space

Question

As the title suggests I'm running jupyter in a docker container and I'm getting the OSError from python deep in the scikit learn/numpy library at the following line:

pickler.file_handle.write(chunk.tostring('C'))

I've done some troubleshooting, and most of the problems people seem to have is their hard drive or RAM actually running out of space, which isn't the case for me AFAIK.

This is what my df looks like:

Filesystem                    1K-blocks       Used   Available Use% Mounted on
udev                           16419976          0    16419976   0% /dev
tmpfs                           3288208      26320     3261888   1% /run
/dev/sdb7                     125996884   72177548    47395992  61% /
tmpfs                          16441036     238972    16202064   2% /dev/shm
tmpfs                              5120          4        5116   1% /run/lock
tmpfs                          16441036          0    16441036   0% /sys/fs/cgroup
/dev/sdb2                         98304      32651       65653  34% /boot/efi
tmpfs                           3288208         68     3288140   1% /run/user/1000
//192.168.1.173/ppo-server3 16864389368 5382399064 11481990304  32% /mnt/ppo-server3

This is what my free looks like:

             total        used        free      shared  buff/cache   available
Mem:       32882072     7808928    14265280      219224    10807864    24357276
Swap:        976892      684392      292500

Am I looking at the right df and free outputs? Both of them are being run from a bash instance inside the container.

Answer 1

Docker leaves dangling images around that can take up your space. To clean up after docker, run the following:

docker system prune -af

or in older versions of docker:

docker rm $(docker ps -q -f 'status=exited')
docker rmi $(docker images -q -f "dangling=true")

This will remove exited and dangling images, which hopefully clears out device space.

Meta: Putting this answer here because it's the top stack-overflow result for that failure and this is a possible fix for it.

Answer 2

As mentioned in the coment by @PeerEZ , this happens when sklearn attempts to parallelize jobs.

sklearn attempts to communicate between processes by writing to /dev/shm, which is limited to 64mb on docker containers.

You can try running with n_jobs=1 as suggested by @PeerEZ (if you can't restart the container), or if parallellization is required, try running the container using the --shm-size option to set a bigger size for /dev/shm . Eg. -

docker run --shm-size=512m <image-name>

Answer 3

If it helps anyone, I received the same error and the problem was that one of my apps log files ( laravel.log ) was almost 11GB in size. Deleting that file resolved my problem.

Answer 4

i have the same problem when run parallel process in a Docker. The trouble is that by default some precess use /dev/shm for storing some cache data and the size of that location in linux is by default about 64MB. You can change de route where your parallel jobs store the cache with this two codelines in python. If the person that is reading this is using Pandarallel this solution can will help you.

import os

os.environ['JOBLIB_TEMP_FOLDER'] = '/tmp'

For users of pandarallel add this line too

pandarallel.initialize(use_memory_fs = False )