GPUs can fasten the training process for any Deep Learning model. The massive architecture of GPUs can help in easing out the tasks, such as Video Analysis, Natural Language Processing, and Image Classification. The usage of GPUs can reduce the tasks taking days to complete in hours. NVIDIA NGC is a collection of GPU-optimised software to be used for HPC, Artificial Intelligence, and Visualisation. It consists of a number of containers, which are the set of libraries, applications, runtime compilers, and dependencies in a self-contained environment, and can easily get deployed to use them all as a package.
DIGITS or NVIDIA Deep Learning GPU Training System is also a container available in NVIDIA. DIGITS combines the pros of deep learning with Engineering and Data Science. It can train the perfect Deep Neural Network-based models for segmentation, object detection tasks, and image classification. It helps simplify the general Deep Learning related tasks, such as designing and training of neural networks, performance monitoring, and data management. In this article, you will learn more about DIGITS.
What is DIGITS Container?
DIGITS, also known as the GPU Training System of Deep Learning, is an app that supports the TensorFlow Framework and trains Deep Learning Models. DIGITS set down Deep Learning’s power into the hands of data scientists and engineers. It is not a framework but acts as a wrapper for TensorFlow that offers a graphical web interface for those who do not like working on the command line.
DIGITS offers an interactive interface that helps data scientists to focus on the training and designing of networks and models rather than on debugging and coding. You can get it either from Github or the Docker repository of NVIDIA. In this article, we shall explain downloading and running the DIGITS container. To install the DIGITS application, click on the link through the NVIDIA Docker’s repository. It will have the container image prebuilt and will get installed at location /usr/local/python/directory. DIGITS also consists of the TensorFlow deep learning framework of NVIDIA.
Before running DIGITS, make sure that your Docker environment supports the NVIDIA GPU. In such an environment, the below things should occur when you run an NVIDIA container:
The image gets loaded by the Docker engine into the container that runs the software. The runtime resources of the container get defined including the settings and flags to be used with the command. The GPUs get the definition for the docker container.
Below are the steps for running DIGITS:
Step1: Execute the below pull command in order to pull the container:
docker pull nvcr.io/nvidia/digits:21.04-TensorFlow
Step2: Execute the pull command on the command line prompt. It will start pulling the image. Only once it is done, move to the next step.
Step3: Execute the container image you have got by downloading the DIGITS container with the below appropriate command given below:
- Check the Docker version. If it is 19.03 or later, use the below command:
docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/digits:xx.xx-framework
- If the Docker version is 19.02 or earlier, use the below command:
- nvidia-docker run -it --rm -v local_dir:container_dir nvcr.io/nvidia/digits:xx.xx-framework
- -it: It refers to the interactive mode
- --rm: It is used for the deletion of the container when done
- -v: It refers to the mounting directory
- Local_dir: it refers to the directory from the host system that you will access from the container. For instance, in the below command /home/jsmith/data/mnist is the value of local dir.
-v /home/jsmith/data/mnist: /home/jsmith /data/mnist
- Container_dir: It refers to the target directory that you will refer to from inside the container. For instance, in the below command /data/mnist is the value of container_dir.
- xx.xx: It refers to the version of the container
- framework: It refers to the name of the framework used. For example TensorFlow.
Few more commands related to DIGITS Container:
- Below is the command that you need to run with a server and port value as 5000 (in the container) to 8888 (on the host).
docker run --gpus all --name digits -d -p 8888:5000
- Use the below command if you want to mount your local directory having your data into another for writing a DIGITS job.
/home/username/data:/data:ro -v /home/username/digits- jobs:/workspace/jobs nvcr.io/nvidia/digits:xx.xx-framework
To conclude, DIGITS is an NVIDIA container that uses the GPU cloud and reduces the training time of models. It eases out the latest technology-based models created using Artificial Intelligence and Deep Learning. If you also want to get the cloud computing GPU installed for your organisation, there are a number of options that you can consider. E2E Clouds is one such GPU cloud provider in India that offers the cheapest cloud GPU servers. Because of their low prices, they are the best choice for getting the cloud GPU for startups. Contact us today and fulfil your need for cloud GPU for Deep Learning.
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