diff --git a/doc.zih.tu-dresden.de%2Fdocs%2Fsoftware/doc.zih.tu-dresden.de/docs/software/ngc_containers.md b/doc.zih.tu-dresden.de%2Fdocs%2Fsoftware/doc.zih.tu-dresden.de/docs/software/ngc_containers.md deleted file mode 100644 index cd1be603bbdf0853f53629f4aefe0b574f7b79f8..0000000000000000000000000000000000000000 --- a/doc.zih.tu-dresden.de%2Fdocs%2Fsoftware/doc.zih.tu-dresden.de/docs/software/ngc_containers.md +++ /dev/null @@ -1,89 +0,0 @@ -# GPU-accelerated containers for deep learning (NGC containers) - -## Containers - -Containers are executable portable units of software in which -application code is packaged, along with its -libraries and dependencies. -[Containerization](https://www.ibm.com/cloud/learn/containerization) encapsulating or packaging up -software code and all its dependencies to run uniformly and consistently -on any infrastructure with other words it is agnostic to host sprefic environment like OS, etc. - -Containers are a widely adopted method of taming the complexity of deploying HPC and AI software. -The entire software environment, from the deep learning framework itself, -down to the math and communication libraries are necessary for performance, is packaged into -a single bundle. Since workloads inside a container -always use the same environment, the performance is reproducible and portable. - -On Taurus [Singularity](https://sylabs.io/) used as a standard container solution. - -## NGC containers - -[NGC](https://developer.nvidia.com/ai-hpc-containers), a registry of highly GPU-optimized software, -has been enabling scientists and researchers by providing regularly updated -and validated containers of HPC and AI applications. - -NGC containers support Singularity. - -NGC containers are optimized for high-performance computing (HPC) applications. -NGC containers are **GPU-optimized** containers -for **deep learning,** **machine learning**, visualization: - -- Built-in libraries and dependencies - -- Faster training with Automatic Mixed Precision (AMP) - -- Opportunity to scaling up from single-node to multi-node systems - -- Allowing you to develop on the cloud, on premises, or at the edge - -- Highly versatile with support for various container runtimes such as Docker, Singularity, cri-o, etc - -- Performance optimized - -## Run NGC containers on ZIH system - -### Preparation - -The first step is a choice of the necessary software (container) to run. -The [NVIDIA NGC catalog](https://ngc.nvidia.com/catalog) -contains a host of GPU-optimized containers for deep learning, -machine learning, visualization, and high-performance computing (HPC) applications that are tested -for performance, security, and scalability. -It is necessary to register to have a full access to the catalouge. - -To find a container which fits to the requirements of your task please check -the [resourse](https://github.com/NVIDIA/DeepLearningExamples) -with the list of main containers with their features and precularities. - -### Building and Run the Container - -To use NGC containers it is necessary to undertend main Singularity commands. -If you are nor familiar with singularity syntax please find the information [here](https://sylabs.io/guides/3.0/user-guide/quick_start.html#interact-with-images). - -Create a container from the image from the NGC catalog. For the exemple alpha partition was used. - -```console -marie@login$ srun -p alpha --nodes 1 --ntasks-per-node 1 --ntasks 1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 bash #allocate alpha partition with one GPU - -marie@compute$ cd /scratch/ws/<name_of_your_workspace>/containers #please create a Workspace - -marie@compute$ singularity pull pytorch:21.08-py3.sif docker://nvcr.io/nvidia/pytorch:21.08-py3 -``` - -Now you have a fully functional PyTorch container. - -In majority of cases the container doesn't containe the datasets for training models. -To download the dataset please follow the instructions for the exact container [here](https://github.com/NVIDIA/DeepLearningExamples). -Also you can find the instructions in a README file which you can find inside the container: - -```console -marie@compute$ singularity exec pytorch:21.06-py3_beegfs vim /workspace/examples/resnet50v1.5/README.md -``` - -As an exemple please find the full command to run the Resnet50 model on the ImageNet dataset -inside the PyTorch container - -```console -marie@compute$ singularity exec --nv -B /scratch/ws/0/anpo879a-ImgNet/imagenet:/data/imagenet pytorch:21.06-py3 python /workspace/examples/resnet50v1.5/multiproc.py --nnodes=1 --nproc_per_node 1 --node_rank=0 /workspace/examples/resnet50v1.5/main.py --data-backend dali-cpu --raport-file raport.json -j16 -p 100 --lr 2.048 --optimizer-batch-size 2048 --warmup 8 --arch resnet50 -c fanin --label-smoothing 0.1 --lr-schedule cosine --mom 0.875 --wd 3.0517578125e-05 -b 256 --epochs 90 /data/imagenet -```