Docker offers multiple platforms that allow developers to operate an application in a loosely isolated environment, known as a container. A Docker container is isolated from one another. The isolation of containers allows them to bundle their software, configuration files, and libraries. You can run numerous containers simultaneously on a given host. The great thing about containers is that it can communicate with each other via well-defined channels.
Docker lets you tremendously enhance your workflow to enable you to produce industry standards for containerizing apps. You can get started with Docker by using:
- Docker Desktop: It offers developer productivity tools that you can use in your local environment.
- Docker Hub: This is a cloud-based Docker application registry service. And it also includes a platform for developers to collaborate.
- Play with Docker: You can try out Docker and learn its tricks and trades in a Docker cloud-based environment.
Besides Docker containers, the two other significant parts of Docker are:
Docker File: It is a text file, containing major commands' list (which is similar to Linux commands) for creating a Docker image. It helps developers to automate Docker image creation.
Docker Image: It is a read-only template that is needed to create a Docker container, which can operate in a Docker environment. It offers a simple solution to package applications and pre-configured server environments.
For starters, Docker allows you to get started for free. If your needs increase, you can jump to a paid version.
What is Natural Language Processing (NLP)?
Natural language processing is one of the parts of artificial intelligence. It lets computers understand, interpret, and manipulate human languages. Using natural language processing, data scientists and other AI professionals can process a massive volume of natural language data.
NLP makes use of sophisticated algorithms to enable computers to find and extract the natural language rules to convert unstructured language data into a form that machines can understand. One of the most common examples of natural language processing is a spell check used by many pieces of software.
Python is one of the most common programming languages among NLP researchers. It is also prevalent in the field of data science, machine learning, and other similar technologies. Rosoka 188.8.131.52 has a Python API to enable professionals the ability to easily include advanced natural language processing in their solutions. Rosoka 184.108.40.206 supports popular programming languages, such as Python and Java.
How to use Docker with NLP
To use Docker with NLP, you need to first install the Docker environment. In this case, we will be using the Rosoka SDK 220.127.116.11 with Jupyter Notebook.
1. Setup Docker:
Your first task is to setup Docker. Here is the list of guide to install Docker on:
2. Docker Hub Signup
Docker Hub lets you share Docker images. You need to push the Docker image from your local computer to a Docker hub for allowing other people to extract and use Docker images.
3. Create a Dockerfile
You need to create a Docker file to form a Docker image. For simplifying the NLP process, you can check out Rosoka’s NLP tools. Check out an example of how you can create a new Dockerfile in the Github site.
4. Build a Docker image
After creating a new Dockerfile, start by building a new Docker image. You will see the Docker command and Docker image tag once you set up a new image.
5. Start a Docker container
Upon building a Docker image, you can now create a Docker container. If you do it correctly, you will see the output, telling you everything is running fine.
6. Push a Docker image
To share a Docker image with the public, you need to push your Docker image from your local machine to a Docker hub. You can do it by using a similar command as below.
Docker push zhangyuefeng123/rosokanlp:1.0
7. Pulling a Docker image
If you already have a Docker image in Dockerhub with expected functionalities, there is no need for you to create a fresh Docker image. You can easily pull the Docker image from the Docker hub.
8. Operating SparkNLP with Jupyter Notebook in Docker Container
Once you do all the hard work by operating a Docker container, you can copy and paste the generated URL for starting the Jupyter Notebook web interface.
Using Docker is more straightforward than a conventional method where one needs to allocate a lot of time to set CUDA with cudNN and installing drivers for Ubuntu for NLP processing. Even after all the hard work, something could go wrong.
For more detailed instructions, you can check out this resource along with this webpage.
When to use Docker with NLP
Using conventional methods to make use of NLP for projects, such as text analysis, chatbot development, and others, is difficult and time-consuming. If you want to harness NLP's power without much hassle quickly, you can use Docker with NLP. By combining Docker with NLP and deep learning, you can save a lot of time. And Docker images are suitable for the majority of tasks.
Improve NLP with Rosoka
Using simple NLP solutions is not enough in modern times. It would be best if you strived to enhance your NLP models to get more out of your efforts. Rosoka is a software provider that dominates the field of text analytics solutions. It helps organizations find crucial entities in unstructured texts, like people and places, while conveying the sentiment and intent of messages written in multiple languages.
Rosoka offers a wide range of features of the variety of text analytics software in one integrated solution. It supports 200 languages, which means you don't need to purchase any other licenses. With Rosoka, you can quickly determine the emotions in specific texts. It will help companies to identify new industry trends. And you also get extra support from dedicated professionals of the company.