CML

The Center for Machine Learning (CML) at the University of Maryland is located within the Institute for Advanced Computer Studies. The CML has a cluster of computational (CPU/GPU) resources that are available to be scheduled.

Data Storage

Until the final storage investment arrives we have made available a temporary allocation of storage. There are 3 types of storage available to users in the CML.

Home Directories

Home directories in the CML computational infrastructure are available from the Institute's NFShomes. These home directories have very limited storage and are intended for your personal files, configuration and source code. Your home directory is _*not*_ intended for data sets or other large scale data holdings.

This data has both snapshots and is backed up nightly.

Project Directories

Users within the CML compute infrastructure can request project based allocations for up to 1TB for up to 120 days from staff@umiacs.umd.edu with approval from a CML faculty member. These allocations will be available from /fs/cml-projects under a name that you provide when you request the allocation. Once the allocation period is over the user will be contacted and give a window of opportunity to clean and secure their data before staff will remove the allocation.

This data is backed up nightly.

Scratch Directories

There are two types of scratch directories in the CML compute infrastructure, network and local scratch directories. Scratch data has no data protection including no snapshots and the data is not backed up.

Network Scratch Directory

Users granted access to the CML compute infrastructure are each allocated 200GB of network attached scratch. This is available as /fs/cml-scratch/USERNAME where USERNAME is your username.

Local Scratch Directory

Each computational node that a user can schedule compute jobs on has one or more local scratch directories. These are always named /scratch0, /scratch1, etc. These are almost always more performant than any other storage available to the job. However users must stage their data within the confine of their job and stage the data out before the end of their job.