Technical specification of workstations
| Machine Name | CPU | Memory | GPU | Disk | Max Power | OS |
|---|---|---|---|---|---|---|
| ML1 |
Intel Core i9-14900KF - 24 Cores |
192GB DDR5 5200MHz | GeForce RTX 4090 - 24GB | M.2 NVMe SSD 2TB | 1000W | Ubuntu 22.04.4 LTS |
| ML2 | Intel Core i9-14900KF - 24 Cores | 192GB DDR5 5200MHz | GeForce RTX 4090 - 24GB | M.2 NVMe SSD 2TB | 1000W | Ubuntu 22.04.4 LTS |
| ML3 | Intel Core i9-14900KF - 24 Cores | 192GB DDR5 5200MHz | GeForce RTX 4090 - 24GB | M.2 NVMe SSD 2TB | 1000W | Ubuntu 22.04.4 LTS |
| ML4 | AMD Ryzen 9 5900X - 12 Cores | 94GB DDR4 3200MHz | GeForce RTX 3090 - 24GB | M.2 NVMe SSD 2TB & 7200 RPM HDD 2TB | 850W | Ubuntu 22.04.4 LTS |
| ML5 | Intel Core i7-11700KF - 8 Cores | 64GB DDR4 3200MHz | GeForce RTX 3070 Ti - 8GB | M.2 NVMe SSD 1TB | 850W | Ubuntu 22.04.4 LTS |
Access
To get access to the machines, send a request via email to the Lab Responsible. Access is granted for a limited time period (renewable).
Machines can be accessed and used remotely via SSH (terminal command line), and files can be transferred using SFTP.
Users can create dedicated Python environments using venv, and packages can be installed using pip. Packages can be installed only when working on the machine locally.
GPU performance can be monitored using nvtop.
Only green data can be used on these workstations.
GPU Memory Management
When multiple users are running Tensorflow 2 Python scripts on the same machine, or when a single user is running multiple scripts, GPU memory must be managed explicitly. By default, Tensorflow scripts pre-allocate and occupy the entire GPU memory, even if only a small fraction is needed. Memory is released when the script ends, preventing other scripts from running (they will fail for lack of resources). In such cases there are two options:
- Disable GPU memory pre-allocation using the following code at the beginning of the script. A script may still fail if the cumulative memory requirement exceed the total available resources.
gpu = tf.config.experimental.list_physical_devices('GPU')[0]
tf.config.experimental.set_memory_growth(gpu, True)
- Set a limit for the GPU memory pre-allocation using the following code at the beginning of the script. The limit is expressed in MB. With this approach you need to know the GPU memory requirements of the script, which can be profiled using the previous option and monitoring the performance with nvtop. Also in this case a script may still fail if the cumulative memory allocation exceed the total available resources, which is unlikely to happen if users coordinate and agree on the share of memory they will use.
gpu = tf.config.experimental.list_physical_devices('GPU')[0]
tf.config.experimental.set_virtual_device_configuration(gpu, [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5000)])