Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Added curl to test script, GPU test commands, some clarifications #2494

Merged
merged 2 commits into from
Apr 15, 2024
Merged

Added curl to test script, GPU test commands, some clarifications #2494

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
252e31c
Added curl to test script, GPU test commands, clarifications
smythp Apr 12, 2024
df6d204
Ran monopod
smythp Apr 15, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 19 additions & 8 deletions images/pytorch-cuda12/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -46,24 +46,35 @@ docker run --rm -i -t \
cgr.dev/chainguard/pytorch:latest
```

If your environment has access to GPUs, you may provide PyTorch access to it by running
## Testing GPU Access

If your environment has connected GPUs, you can check that PyTorch has access with the following:

```bash
docker run --rm -it --gpus all cgr.dev/chainguard/pytorch-cuda12:latest
bash-5.2$ python
Python 3.11.8 (main, Feb 7 2024, 00:46:15) [GCC 13.2.0] on linux
Python 3.11.9 (main, Apr 2 2024, 15:40:32) [GCC 13.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.cuda.is_available())
>>> torch.cuda.is_available()
True
>>> torch.cuda.device_count()
1
>>> torch.cuda.get_device_name(0)
'Tesla V100-SXM2-16GB'
```
As a quick intro, we will use PyTorch to create a very simple deep learning model with two linear layers and an activation function. We’ll create an instance of it and ask it to report on its parameters. The script can be found in ```model_builder.py``` in this directory.

To run this script,
```bash
## Testing PyTorch

docker run --rm -it -v /home/srishihegde/quick.py:/tmp/model_builder.py --gpus all cgr.dev/chainguard/pytorch-cuda12:latest -c "python /tmp/model_builder.py"
As a quick intro, we will use PyTorch to create a very simple deep learning model with two linear layers and an activation function. We’ll create an instance of it and ask it to report on its parameters. Running the below will fetch a [model_builder.py](https://github.com/chainguard-images/images/blob/main/images/pytorch-cuda12/model_builder.py) script from the Chainguard Images repository, place it in a folder on your host machine, and run the script in a pytorch-cuda12 container from a volume.

```bash
mkdir pytorch-test &&\
curl https://raw.githubusercontent.com/chainguard-images/images/main/images/pytorch-cuda12/model_builder.py > pytorch-test/model_builder.py &&\
docker run --rm -it -v "$PWD/pytorch-test:/tmp/pytorch-test" --gpus all cgr.dev/chainguard/pytorch-cuda12:latest -c "python /tmp/pytorch-test/model_builder.py"
```
A quickstart tutorial as outlined [here](https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html) can also be run using the tests/quickstart.py script similar to the above run

You may also consider running this [quickstart script](https://github.com/chainguard-images/images/blob/main/images/pytorch-cuda12/tests/quickstart.py) based on the [official PyTorch quickstart tutorial](https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html) using the same approach as above.

### Using Helm charts

Expand Down
Loading