.. _infrastructure: Infrastructure ############## C025 Hub Server =============== You can use our workstation in C025 lab if you need computing facilities such as GPU, but the available resouces are limited with 24 cores of CPU, 40GB RAM, and 2x Nvidia 3090 with 24 GB GPU memory each. However, if you need more resources, please request access to `the university HPC cluster `_. We also provide a JupyterHub server on the workstation so that you do not have to setup the environment. Hub Server ---------- The address of the JupyterHub server is `https://10.20.118.78:32025 `_ and LDAP is used for the login. Multiple environments can be provided and is described in `here `_. You can choose the one that meets your needs or PR to the aforementioned repository if you need to update the libraries. We can, of course, provide more environment options as per request. `Deep Learning env with Tensorflow and Pytorch` is default environment and contains several deep learning libraries defined in `gpu-notebook `_. The GPUs are accessible by all authorized users and there is no individual GPU resouce allocation. Access ------ In order to get access to the hub, we need to add your LDAP username. .. hint:: Admins can add new users by going to admin page `https://10.20.118.78:32025/hub/admin `_. * Within the university network: You can access the hub directly from the university network. * Remote access * University VPN Follow `this instruction `_ to setup VPN in your local machine. Once you are connected to the university VPN, you can open the Hub address. * SSH tunnel If you do not want to use VPN, you can port forward from your FB02 account to C025 workstation .. code-block:: bash # replace account2s with your FBUID ssh -L 32025:10.20.118.78:32025 account2s@home.inf.h-brs.de Then, open browser and go to `https://localhost:32025 `_. .. note:: SSH access to the workstation is only possible with ssh key, so if you need it, please send us your ssh key. University Cluster ==================