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NLP-Text-Generation

Last updated: Sep 29th 2021

This is our GitHub repository for the Paperspace Gradient NLP Text Generation Tutorial example. It runs the GPT-2 model from HuggingFace: https://huggingface.co/gpt2 .

The example shows:

  • Text generation from a modern deep-learning-based natural language processing model, GPT-2
  • Gradient Projects linked to GitHub repositories
  • Gradient Workflows
  • Triggering a Workflow to rerun based upon a change in the repository, as needed in many production systems
  • Versioned Gradient-managed Datasets as output

The repo contains 2 files: nlp_text_generation.py in the main directory, and nlp_text_generation.yamlin the .gradient/workflows directory. The YAML file contains the Gradient Workflow which in turn calls the Python script.

The Workflow is triggered to run when the YAML file is present in the .gradient/workflows/ directory, and the repo is linked to the user's Gradient project. The Workflow clones this repo and then in turn calls the Python script. The script outputs the generated text to the file outputs.txt in the Gradient-managed Dataset demo-dataset, which the user can then view.

The Workflow runs on the Paperspace HuggingFace NLP container (paperspace/transformers-gpu:0.4.0).

Steps to run this tutorial

Easiest way: clone the example repository

Assuming you are up and running with Gradient, and have the GitHub app installed to your GitHub username, this Project runs as a sample repository.

  • Create a Project
  • Under the Workflows tab of the Project, click Create a Workflow
  • In the illustrated list of Projects in the central panel, select the one for "NLP Text Generation"
  • Select your GitHub username from the Account and Organizations dropdown list
  • Choose a repository name
  • Click "Create Project"

The Workflow will then run.

Alternative method: create your own repo fork first

You can also fork your own copy of this tutorial repo, then create a repo-linked Workflow that points to the fork:

  • Navigate to https://github.com/gradient-ai/NLP-Text-Generation in your browser
  • In the resulting GitHub GUI page, click "Fork" in the top right
  • Follow the usual GitHub procedure by selecting your GitHub account to fork the repo to
  • Create a Project
  • Under the Workflows tab of the Project, click Create a Workflow
  • Instead of selecting the sample repo for "NLP Text Generation", click "import an existing gradient repository"
  • Choose the repo fork you just created from the dropdown list
  • This will take you to the "Let's create a Workflow" screen. Change any of the files in your forked repo to trigger the nlp_text_generation.yaml file under .gradient/workflows/ to run. For example, add a few characters to the readme.md.

The result should be the same as above.

Note: When running the Workflow from a project linked to your own fork of the repo, it will still be cloning from the original location https://github.com/gradient-ai/NLP-Text-Generation, unless you choose to alter the location that the Workflow points to, which is optional.

Altering the model settings and triggering a Workflow rerun

The ability to trigger Workflow reruns is useful in several situations, especially more MLOps and production-oriented ones where the state of the collection of code, data, deployments, models, and other components should be consistent.

Changing the model settings can be used to trigger a rerun of the model. The 4 values under "Settings" in the nlp_text_generation.py script (random seed, maximum text length, number of returned text sequences, and the initial text sentence) can be altered to generate different text.

If the resulting updated version of the nlp_text_generation.py file is uploaded to the repo main directory to replace the one present, and the project remains linked to the repo, the Workflow will be rerun. A new output.txt file is generated, and placed in a new version of the output Gradient-managed Dataset.

Next Steps

See the documentation page for this tutorial for some suggested next steps (e.g., you can run the newer+larger GPT-Neo instead of GPT-2).