Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach, also known as dynamic computational graphs, as well as object-oriented high-level APIs to build and train neural networks. It supports CUDA and cuDNN using CuPy for high performance training and inference.
Before running the container, use
docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.
In the Tags section, locate the container image release that you want to run.
In the Pull column, click the icon to copy the
docker pull command.
Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.
Run the container image. A typical command to launch the container is:
nvidia-docker run --rm -v local_dir:container_dir nvcr.io/nvidia/chainer:<xx.xx>
it means run in interactive mode
--rm will delete the container when finished
-v is the mounting directory
local_dir is the directory or file from your host system (absolute path) that you want to access from inside your container. For example, the
local_dir in the following path is
If you are inside the container, for example,
ls /data/mnist, you will see the same files as if you issued the
ls /home/jsmith/data/mnist command from outside the container.
- `container_dir` is the target directory when you are inside your container. For example, `/data/mnist` is the target directory in the example: ``` -v /home/jsmith/data/mnist:/data/mnist ``` - `<xx.xx>` is the tag. For example, `17.12`.
For more information about Chainer, including tutorials, documentation, and examples, see: