> For the complete documentation index, see [llms.txt](https://docs.ccv.brown.edu/oscar/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.ccv.brown.edu/oscar/gpu-computing/gpus/ampere-architecture-gpus.md).

# Ampere Architecture GPUs

The new Ampere architecture GPUs do not support older CUDA modules. Users must re-compile their applications with the newer CUDA/11 or older modules. Here are detailed instructions to compile major frameworks such as PyTorch, and TensorFlow.

## PyTorch

Users can install PyTorch from a pip virtual environment or use pre-built singularity containers provided by Nvidia NGC.

### To install via virtual environment:

```bash
# Make sure none of the LMOD modules are loaded
module purge 
module list

# create and activate the environment
python -m venv pytorch.venv
source pytorch.venv/bin/activate
pip install torch torchvision torchaudio

# test if it can detect GPUs 
```

### To use NGC containers via Singularity :

* Pull the image from NGC

```bash
singularity build pytorch:21.06-py3 docker://nvcr.io/nvidia/pytorch:21.06-py3
```

* Export PATHs to mount the Oscar file system

```bash
export SINGULARITY_BINDPATH="/oscar/home/$USER,/oscar/scratch/$USER,/oscar/data/"
```

* To use the image interactively

```bash
singularity shell --nv pytorch\:21.06-py3
```

* To submit batch jobs

```bash
#!/bin/bash

# Request a GPU partition node and access to 1 GPU
#SBATCH -p 3090-gcondo,gpu --gres=gpu:1

# Ensures all allocated cores are on the same node
#SBATCH -N 1

# Request 2 CPU cores
#SBATCH -n 2
#SBATCH --mem=40g
#SBATCH --time=10:00:00

#SBATCH -o %j.out

export SINGULARITY_BINDPATH="/oscar/home/$USER,/oscar/scratch/$USER,/oscar/data/"
singularity --version

# Use environment from the singularity image
singularity exec --nv pytorch:21.06-py3 python pytorch-cifar100/train.py -net vgg16 -gpu
```


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.ccv.brown.edu/oscar/gpu-computing/gpus/ampere-architecture-gpus.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.