3.0 Specifics for MCML Members
Access Management for MCML PIs
MCML PIs who want (members of their research group) to access the MCML partition (as part of the LRZ AI Systems), please open a service request with LRZ Servicedesk here
and choose/add
- Type: Service Request
- Description: "Access to MCML Segment @ LRZ AI Systems";
- Details:
- Please specify the name of the MCML PI
- If applicable (i.e. if the working group already has one), please provide a LRZ Master User/Linux Cluster project ID
Further instructions will follow via the communication in the service request ticket.
Once a suitable LRZ Master User/Linux Cluster project has been created/set up as a "MCML project", any account within this project which gets Linux Cluster permissions assigned (by the Master Users) will automatically also be granted access to LRZ AI Systems, including the MCML partition of this system. Similarly, removing Linux Cluster permissions from individual accounts will, also revoke LRZ AI Systems permissions, including the MCML partition.
Dedicated Compute Hardware
In addition to the generally available resources listed on General Description and Resources, the user IDs associated with the LRZ projects of MCML research groups are entitled to use the following hardware. Access will be granted automatically (for dedicated MCML LRZ projects) or upon request (for eligible accounts of pre-existing LRZ Master User projects). The allocation time limit for individual jobs is 4 days (4-00:00:00).
Slurm Partition | Number of nodes | CPU cores per node | Memory per node | GPUs per node | Memory per GPU | |
|---|---|---|---|---|---|---|
DGX A100 Architecture | mcml-dgx-a100-40x8 | 8 | 256 | 1 TB | 8 NVIDIA A100 | 40 GB |
In order to use resources of the mcml-dgx-a100-40x partition, eligible users need to specify the "mcml" quality of service (QoS) for their job allocation and/or submission, e.g.
$ salloc -p mcml-dgx-a100-40x8 -q mcml -n 8 --gres=gpu:8 # short form options, where available $ salloc --partition=mcml-dgx-a100-40x8 --qos=mcml --ntasks=8 --gres=gpu:8 # long form options
Dedicated Storage Options
As indicated on Storage on the LRZ AI Systems, there is a dedicated MCML DSS system (/dssmcmlfs01) available to eligible users. This is high-performance, SSD-based network storage. The system is intended for high-bandwidth, low-latency I/O operations, serving the demands of modern day AI applications.
The Master Users of eligible LRZ projects (i.e. MCML research groups) are welcome to submit a service request to LRZ Servicedesk asking for storage on the MCML DSS system. By using this link, they will open a ticket for the appropriate service. Select "AI topics", next "Master user only: Application for project storage space (DSS AI)" and confirm. Fill in the form and make sure to note that the request is for MCML DSS. Finally, submit the form.
Once granted, the Master User will subsequently act as DSS Data Curator and manage the assigned storage quotas for their project. A quota of up to 10 TB, 20.000.000 files and maximum 5 DSS containers (typically: a single container) will be assigned.