## What Is DIGITS?

The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists.

DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks.

DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model from the results browser for deployment.

DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. 

## Running DIGITS

Before you can run an NGC deep learning framework container, your Docker environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in the [Running A Container](https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#runcont) chapter in the *NVIDIA Containers And Frameworks User Guide* and specify the registry, repository, and tags. For more information about using NGC, refer to the [NGC Container User Guide](https://docs.nvidia.com/ngc/ngc-user-guide/index.html).

The method implemented in your system depends on the DGX OS version installed (for DGX systems), the specific NGC Cloud Image provided by a Cloud Service Provider, or the software that you have installed in preparation for running NGC containers on TITAN PCs, Quadro PCs, or vGPUs.

Procedure
1. Select the **Tags** tab and locate the container image release that you want to run.
2. In the **Pull Tag** column, click the icon to copy the **`docker pull`** command.
3. 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.
4. Run the container image. 

	If you have Docker 19.03 or later, a typical command to launch the container is:
	``` 
	docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/digits:xx.xx-framework
	```

	If you have Docker 19.02 or earlier, a typical command to launch the container is:
	``` 
	nvidia-docker run -it --rm -v local_dir:container_dir nvcr.io/nvidia/digits:xx.xx-framework
	```

	Where:
 - `-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 `/home/jsmith/data/mnist`. 
 
 ```
 -v /home/jsmith/data/mnist:/data/mnist
 ```
 
 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 container version. For example, `20.01`.
 - `framework` is the framework name. For example, `caffe` or `tensorflow`.
 
 a. To run the server as a daemon and expose port 5000 in the container to port 8888 on your host:
 ```
 docker run --gpus all --name digits -d -p 8888:5000 
 nvcr.io/nvidia/digits:xx.xx-framework
 ```
	b. To mount one local directory containing your data (read-only), and another for writing your DIGITS jobs:
	```
	docker run --gpus all --name digits -d -p 8888:5000 -v 
 /home/username/data:/data:ro -v /home/username/digits- 
 jobs:/workspace/jobs nvcr.io/nvidia/digits:xx.xx-framework
	``` 
 Note: In order to share data between ranks, NCCL may require shared system memory for IPC and pinned (page-locked) system memory resources. 
 The operating systemâs limits on these resources may need to be increased accordingly. Refer to your systemâs documentation for details. 
 In particular, Docker containers default to limited shared and pinned memory resources. When using NCCL inside a container, it is recommended 
 that you increase these resources by issuing:
 
 ``` 
 --shm-size=1g --ulimit memlock=-1
 ```
 
 in the command line to:
 ```
 docker run --gpus all
 ```

4. See **`/workspace/README.md`** inside the container for information on customizing your DIGITS image.


## Suggested Reading

For the latest Release Notes, see the [DIGITS Release Notes Documentation website]( http://docs.nvidia.com/deeplearning/digits/digits-release-notes/index.html).

For a full list of the supported software and specific versions that come packaged with this framework based on the container image, see the [Frameworks Support Matrix]( https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).

For more information about DIGITS, see:

* [DIGITS website]( https://developer.nvidia.com/digits)
* [DIGITS project]( https://github.com/NVIDIA/DIGITS/blob/digits-5.0/README.md)
* [nvidia-docker documentation]( https://github.com/NVIDIA/nvidia-docker/wiki/DIGITS)

Note: There may be slight variations between the NVIDIA-docker images and this image.

## Security CVEs

To review known CVEs on the 21.07 image, please refer to the Known Issues section of the [Product Release Notes]( https://docs.nvidia.com/deeplearning/digits/digits-release-notes/rel_21-07.html#rel_21-07).

## License

By pulling and using the container, you accept the terms and conditions of this [End User License Agreement]( https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license).

digits

The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists.

DIGITS

Description

Publisher

Latest Tag

Modified

Compressed Size

Multinode Support

Multi-Arch Support

21.09-tensorflow-py3 (Latest) Scan Results