Based on RubyData's docker-stacks
- Ruby Kernel for Jupyter: Use IRuby to run Ruby code interactively within Jupyter notebooks.
- NLP Libraries: Includes gems for text tokenization, sentiment analysis, entity extraction, and various language utilities.
- Cross-Language Integration: Leverage Python libraries from Ruby via pycall for mixed-language workflows.
- Data Analysis: Utilize gems like daru and daru-view for data manipulation and visualization.
- Docker installed on your machine.
- Basic familiarity with Jupyter Notebooks and Docker commands.
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Clone the Repository
git clone https://github.com/yourusername/your-repo.git cd your-repo -
Build the Docker Image
The provided
Dockerfileuses a multi-stage build:- Stage 1 (Builder): Installs Ruby, build dependencies, and RubyGems listed in the
Gemfile. - Stage 2 (Final Image): Uses the Jupyter minimal-notebook as the base, copies Ruby binaries and installed gems from the builder stage, and sets up runtime dependencies.
To build the image, run:
docker build -t jupyter-ruby-nlp . - Stage 1 (Builder): Installs Ruby, build dependencies, and RubyGems listed in the
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Verify Build
Once built, list your Docker images:
docker images
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Launch the Notebook Server
Run the container, mapping port 8888 to your host:
docker run --rm -p 8888:8888 jupyter-ruby-nlp
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Access Jupyter
Open your browser and go to
http://localhost:8888. The startup logs will display an authentication token—use it to log in. -
Using the Ruby Kernel
Within Jupyter, create a new notebook and select the Ruby kernel (IRuby). You can then run Ruby code and take advantage of the installed NLP gems.
- IRuby Notebooks: Start exploring Ruby-based data analysis and NLP tasks directly in your notebook.
- Integrate Python: With pycall installed, you can call Python libraries (e.g., spaCy for NLP) from your Ruby code.
- NLP Pipelines: Use gems such as ruby-spacy and langchainrb to build language processing pipelines and experiment with language models.
Below is a brief description of some of the key RubyGems included in the Gemfile:
- iruby: Provides the Ruby kernel for Jupyter, enabling interactive Ruby sessions.
- pycall: Allows Ruby code to call Python functions and libraries, bridging the gap between Ruby and Python ecosystems.
- ruby-spacy: A Ruby wrapper for spaCy, offering NLP functionalities like tokenization, part-of-speech tagging, and dependency parsing.
- langchainrb: Offers utilities for building language model workflows, useful for tasks like chaining prompts and responses.
- daru & daru-view: Libraries for data analysis and visualization in Ruby.
- fuzzy_tools & pragmatic_tokenizer: Gems that facilitate text processing tasks such as fuzzy matching and tokenization.
- Other Utilities: Gems such as
open3,redis,jsonl, and varioustty-*packages support logging, concurrency, and terminal UI improvements.
For a complete list of the gems and their respective versions, please review the Gemfile.
- Gemfile Updates: To add or update RubyGems, modify the Gemfile and rebuild the image.
- Kernel Patching: The Dockerfile applies a patch to
ruby-spacy(viarespond_to_missing.patch)—customize this patch if needed for your setup. - Dockerfile Tweaks: Adjust the Dockerfile as necessary (e.g., to add extra system packages or change configurations).
- Build Issues: Verify that all build dependencies are available and that your Docker daemon has enough resources.
- Kernel Registration: If the Ruby kernel does not appear in Jupyter, ensure that
iruby register --forceran successfully during the build. - Port Conflicts: If port 8888 is already in use, modify the
docker runcommand to map to a different host port.
This project is licensed under the MIT License. See the LICENSE file for details.
Contributions are welcome! Please fork the repository and submit pull requests for any improvements or bug fixes.