Desmond
System Requirements
Supported Operating Systems
Hardware Requirements
| Required |
|
|
| Driver |
The driver (master job) must run for the complete duration of the job without being interrupted. This means the computing resource on which it runs cannot be a spot or preemptible cloud instance. These nodes can be pre-empted (terminated) and if that happens your whole job will be lost. The -DRIVERHOST argument determines where the driver runs. Select a host entry that is for an on-demand (i.e. not preemptible) node type. |
If sufficient licenses and computational resources are available to run multiple Active Learning Glide jobs simultaneously, it is recommended to configure the driver host entry so that it requests an entire node, to avoid multiple drivers potentially using the same node and scratch filesystem, and thereby doubling (or more) the space requirement. |
| Processor (CPU) |
x86_64 compatible processor |
For large jobs, computing on a cluster with a queueing system is recommended, with the following hardware components:
|
| System memory (RAM) |
64 GB memory for the entire node |
RAM is not related to the input file size, only the disk space is related. |
| Disk space |
|
GPGPU Requirements
(General-purpose computing on graphics processing units)
We support the following NVIDIA solutions:
| Architecture | Server / HPC | Workstation |
| Pascal |
Tesla P40 Tesla P100 |
Quadro P5000 |
| Volta |
Tesla V100 |
|
| Turing |
Tesla T4 |
Quadro RTX 5000 |
| Ampere |
A100 |
RTX A4000 RTX A5000 |
| Ada Lovelace |
L4 |
RTX 4000 SFF Ada RTX 2000 Ada |
| Hopper |
H100 |
|
| Blackwell |
B200 (SXM) |
RTX PRO 6000 Blackwell Workstation |
Unless otherwise specified, we only support and test on the PCIe variant of the cards listed above.
To check the compute capability of NVIDIA cards, see NVIDIA CUDA GPU Compute Capability.
Supported Linux drivers
- We support only the NVIDIA recommended / certified / production branch' Linux drivers for these cards with minimum CUDA version 12.0. Download from NVIDIA's Drivers webpage.
Supported Multi-Instance GPU (MIG)
- We support NVIDIA’s Multi-Instance GPU (MIG) feature. Please make sure that MIG-enabled GPUs are being used in conjunction with a queueing system which supports this feature, see How do I make use of Multi-instance GPUs (MIG).
Pre-configured Schrödinger compatible GPU boxes
- For information on pre-configured Schrödinger compatible GPU boxes see this article.
Notes
- Standard support does not cover consumer-level GPU cards such as GeForce GTX cards. Learn more about our rigorous validation process and why we exclusively support professional-grade NVIDIA hardware in this article.
- If you already have another NVIDIA GPGPU and would like to know if we have experience with it, please contact our support at help@schrodinger.com.