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This file system is usually very fastCurrently, but only if it is used for workloads it is designed for. Remember that it there is a distributed storagesufficient space left, this means that metadata access (such as file attributeshowever, or I kindly ask you to not keep data you do not use anymore on which server to look the Ceph filesystem for a specific file) is over a database and can be a bottlenecktoo long. In effectparticular, performance breaks down dramatically if writing or accessing many small filesplease delete old checkpoints of training runs you will never need again - I have seeen people use several Terabytes for their training histories. If you still need these, or having many small files in a single directory (which forces metadata please move them onto your own computers. If you really want to be stored keep old stuff lying around on the cluster filesystem, maybe because you are not sure whether you will need it again later on , then please put it into a single server)folder which is not backed up.For this, every user can mount the Ceph directory
'''TL;DR, and this is very important: when using CephFS, make sure to organize <syntaxhighlight lang="bash">/cephfs/abyss/archive/nobackup/<your dataset in few large files (e.g. HDF5), and not many small ones !'''-username></syntaxhighlight>
If this which can be used as an archive. Make sure that the directory is created if it does not possible for youexist, then you need to resort to persistent volumes residing on local storage on a single node, which for small files is orders of magnitude faster, but you are bound to a particular node (or have to duplicate the data in different local filesystems). A tutorial followsby specifying "type: DirectoryOrCreate".
* <syntaxhighlight lang="bash">/raid/local-data/<your-username></syntaxhighlight>
→Local storage on the node
== The CephFS file system ==
As explained in the [[CCU:GPU Cluster Quick Start|quick start tutorial]], every user can mount certain local host paths inside their pods, which refer to a global distributed Ceph file system. Reminder, the primary home directory is <syntaxhighlight lang="bash">/cephfs/abyss/home/<your-username></syntaxhighlight> This file system is usually quite fast, but only if it is used for workloads it is designed for. It is a distributed storage, where the filesystem metadata is stored in databases on different servers, and the actual content of the files on other ones. This means that metadata access (such as reading file attributes, or on which server to look for a specific file) can be a bottleneck. In effect, the task of reading the metadata for a small file is orders of magnitude more expensive than reading the actual contents of the file itself. This means that performance breaks down dramatically if writing or accessing many small files. In particular, having many small files in a single directory (say >10k) makes any simple filesystem operations such as directory listings take ages, and in particular automated backup jobs might run into problems. '''TL;DR, and this is very important: when using CephFS, make sure to organize your dataset in few large files (e.g. HDF5), and not many small ones ! If you really have to have individual files, then make sure they are stored in subdirectories which do not become too large. ''' For example, if you have a million images of the form abcdef.jpg in a single directory, you better distribute them over a directory tree a/b/c/def.jpg, so that it is only 1000 files per directory. An interesting option if you have a dataset consisting of many small files might be to keep it in a tar archive and mount that archive using [https://github.com/mxmlnkn/ratarmount ratarmount]. If this is not possible for you, then you need to use the local SSD storage on a single node, which for small files is orders of magnitude faster, but you are bound to a particular node (or have to duplicate the data in different local filesystems). See below for details on local filesystems. == CephFS capacity and backup strategy == The storage on the Ceph filesystem is quite expensive due to redundancy built in (if any server reboots or is otherwise unavailable, the others can still serve all of the data). The contents of the home directories are also backed up daily onto a backup server with a file history - if you ever accidentally overwrite or otherwise lose an extremely important file, you can contact me and check if I have an old copy in a backup.
== Local storage on the node ==
The path for local storage for each user is
You can mount it as a hostPath, but have to make sure that the directory is created if it does not exist, by specifying "type: DirectoryOrCreate".
The data will remain persistent on the host, but note that it also only exists on this particular host. If you need to access it again, you need to make sure the pod always ends up on the same specific node. See example below. Otherwise, write your scripts in such a way that they check for existence of the local data, and if it is not there yet, copy it over from somewhere on the internet.
'''In contrast to Ceph storage, local paths on the hosts are not backed up. You have been warned.'''
== Example ==
An even better variant would be "rsync -av" instead of scp, as this only copies files which are different or do not exist in the destination. By reversing source and destination, you can also copy data out of the container this way.
=== Copying data from your local machine the outside ===
From the local machine which has kubectl access to the cluster, you can directly copy data to and from the container using kubectl cp, which has a very similar syntax as scp:
