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NUS Apollo

Table of Contents

Connecting

Connect to SoC VPN then you can use ssh to connect to the lab machine directly.

ssh USERNAME@hpc-ai-01.d2.comp.nus.edu.sg

❗ Download Rules

❗ ❗ ❗ Please read this section before use

If you want to download a large dataset, please limit your download rate so that it will not perform DDOS to the network. You can use trickle to specify how fast you download your dataset. In general, you should limit your download rate to 10 MB/s to play safe. We use trickle to control the download rate.

Some options of trickle are:

  • -s: run in standalone mode
  • -u: upload rate in KB/s
  • -d: download rate in KB/s
# this will download CIFAR10 dataset at around 4 MB/s
trickle -s  -u 1024 -d 4096 wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz

# this will download CIFAR10 dataset at around 2 MB/s
trickle -s  -u 1024 -d 2048 wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz

Storage

We store datasets which are commonly used by many users in /data/common. If you feel that your dataset is common but no in /data/common, you can notify the system admin to download this dataset for you or move the downloaded dataset to data/common.

Please store large files (e.g. personal datasets, model weights) in /data/personal. You need to create your own directory.

Software Stack

Environment Module

We use Environment Module to manage the software stack. You can use module av to list the available software package and use module load to load the software you want to use.

module load miniconda3 cuda/11.3.1

Singularity Image

You can use singularity to run container.

  1. Run Image: singularity shell --nv
  2. Clone Environment: conda create --clone
  3. Install Other Python Dependency with pip or conda

New environment will create under $HOME/.conda/envs. Then activate the environment again without reconfiguring the environment, use it directly after source.

$ module load singularity
$ singularity shell --nv /opt/nussif/pytorch_21.12-py3.sif
Singularity> conda create --clone base --name ngc-torch-21.11 # only need when first time 
Singularity> source activate ngc-torch-21.11
Singularity> python -c "import torch;print(torch.cuda.is_available())"

Hardware Overview

Node Name GPU CPU Memory Storage
hpc-ai-01 NVIDIA HGX A100 8-GPU 2 x AMD EPYC 7742 64-Core 1TB 3200 MT/s DDR4 1.8 TB System NVME 11 TB Dada NVME