Ludwig

What is Ludwig?

Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks.

Ludwig Features

• 🛠 Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures.

• ⚡ Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), paged and 8-bit optimizers, and larger-than-memory datasets.

• 📐 Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations.

• 🧱 Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning.

• 🚢 Engineered for production: prebuilt Docker containers, native support for running with Ray on Kubernetes, export models to Torchscript and Triton, upload to HuggingFace with one command.

Ludwig is hosted by the

Linux Foundation AI & Data.

💾 Installation

Install from PyPi. Be aware that Ludwig requires Python 3.8+.

pip install ludwig

Or install with all optional dependencies:

pip install ludwig[full]

Please see contributing for more detailed installation instructions.

🚂 Getting Started

Want to take a quick peak at some of the Ludwig 0.8 features? Check out this Colab Notebook 🚀 !Open In Colab

Looking to fine-tune Llama-2 or Mistral? Check out these notebooks:

1. Fine-Tune Llama-2-7b: !Open In Colab

2. Fine-Tune Llama-2-13b: !Open In Colab

3. Fine-Tune Mistral-7b: !Open In Colab

For a full tutorial, check out the official getting started guide, or take a look at end-to-end Examples.

Large Language Model Fine-Tuning

!Open In Colab

Let’s fine-tune a pretrained LLaMA-2-7b large language model to follow instructions like a chatbot (“instruction tuning”).

Prerequisites

• HuggingFace API Token

• Access approval to Llama2-7b-hf

• GPU with at least 12 GiB of VRAM (in our tests, we used an Nvidia T4)

Running

We’ll use the Stanford Alpaca dataset, which will be formatted as a table-like file that looks like this:

Create a YAML config file named model.yaml with the following:

model_type: llm

base_model: meta-llama/Llama-2-7b-hf

quantization:

  bits: 4

adapter:

  type: lora

prompt:

  template: |

    Below is an instruction that describes a task, paired with an input that may provide further context.

    Write a response that appropriately completes the request.

    ### Instruction:

    {instruction}

    ### Input:

    {input}

    ### Response:

input_features:

  - name: prompt

    type: text

output_features:

  - name: output

    type: text

trainer:

  type: finetune

  learning_rate: 0.0001

  batch_size: 1

  gradient_accumulation_steps: 16

  epochs: 3

  learning_rate_scheduler:

    decay: cosine

    warmup_fraction: 0.01

preprocessing:

  sample_ratio: 0.1

backend:

  type: local

And now let’s train the model:

export HUGGING_FACE_HUB_TOKEN = "<api_token>"

ludwig train --config model.yaml --dataset "ludwig://alpaca"

Supervised ML

Let’s build a neural network that predicts whether a given movie critic’s review on Rotten Tomatoes was positive or negative.

Our dataset will be a CSV file that looks like this:

Download a sample of the dataset from here.

wget https://ludwig.ai/latest/data/rotten_tomatoes.csv

Next create a YAML config file named model.yaml with the following:

input_features:

  - name: genres

    type: set

    preprocessing:

      tokenizer: comma

  - name: content_rating

    type: category

  - name: top_critic

    type: binary

  - name: runtime

    type: number

  - name: review_content

    type: text

    encoder:

      type: embed

output_features:

  - name: recommended

    type: binary

That’s it! Now let’s train the model:

ludwig train --config model.yaml --dataset rotten_tomatoes.csv

Happy modeling

Try applying Ludwig to your data. Reach out

if you have any questions.

❓ Why you should use Ludwig

• Minimal machine learning boilerplate

  Ludwig takes care of the engineering complexity of machine learning out of

  the box, enabling research scientists to focus on building models at the

  highest level of abstraction. Data preprocessing, hyperparameter

  optimization, device management, and distributed training for

torch.nn.Module models come completely free.

• Easily build your benchmarks

  Creating a state-of-the-art baseline and comparing it with a new model is a

  simple config change.

• Easily apply new architectures to multiple problems and datasets

  Apply new models across the extensive set of tasks and datasets that Ludwig

  supports. Ludwig includes a

  full benchmarking toolkit accessible to

  any user, for running experiments with multiple models across multiple

  datasets with just a simple configuration.

• Highly configurable data preprocessing, modeling, and metrics

  Any and all aspects of the model architecture, training loop, hyperparameter

  search, and backend infrastructure can be modified as additional fields in

  the declarative configuration to customize the pipeline to meet your

  requirements. For details on what can be configured, check out

  Ludwig Configuration

  docs.

• Multi-modal, multi-task learning out-of-the-box

  Mix and match tabular data, text, images, and even audio into complex model

  configurations without writing code.

• Rich model exporting and tracking

  Automatically track all trials and metrics with tools like Tensorboard,

  Comet ML, Weights & Biases, MLFlow, and Aim Stack.

• Automatically scale training to multi-GPU, multi-node clusters

  Go from training on your local machine to the cloud without code changes.

• Low-code interface for state-of-the-art models, including pre-trained Huggingface Transformers

  Ludwig also natively integrates with pre-trained models, such as the ones

  available in Huggingface Transformers.

  Users can choose from a vast collection of state-of-the-art pre-trained

  PyTorch models to use without needing to write any code at all. For example,

  training a BERT-based sentiment analysis model with Ludwig is as simple as:

  ludwig train --dataset sst5 --config_str "{input_features: [{name: sentence, type: text, encoder: bert}], output_features: [{name: label, type: category}]}"

• Low-code interface for AutoML

  Ludwig AutoML

  allows users to obtain trained models by providing just a dataset, the

  target column, and a time budget.

  auto_train_results = ludwig.automl.auto_train(dataset=my_dataset_df, target=target_column_name, time_limit_s=7200)

• Easy productionisation

  Ludwig makes it easy to serve deep learning models, including on GPUs.

  Launch a REST API for your trained Ludwig model.

  ludwig serve --model_path=/path/to/model

Ludwig supports exporting models to efficient Torchscript bundles.

  ludwig export_torchscript -–model_path=/path/to/model

📚 Tutorials

• Text Classification

• Tabular Data Classification

• Image Classification

• Multimodal Classification

🔬 Example Use Cases

• Named Entity Recognition Tagging

• Natural Language Understanding

• Machine Translation

• Chit-Chat Dialogue Modeling through seq2seq

• Sentiment Analysis

• One-shot Learning with Siamese Networks

• Visual Question Answering

• Spoken Digit Speech Recognition

• Speaker Verification

• Binary Classification (Titanic)

• Timeseries forecasting

• Timeseries forecasting (Weather)

• Movie rating prediction

• Multi-label classification

• Multi-Task Learning

• Simple Regression: Fuel Efficiency Prediction

• Fraud Detection

💡 More Information

Read our publications on Ludwig, declarative ML, and Ludwig’s SoTA benchmarks.

Learn more about how Ludwig works, how to get started, and work through more examples.

If you are interested in contributing, have questions, comments, or thoughts to share, or if you just want to be in the

know, please consider joining the Ludwig Slack and follow us on Twitter!

<img src="https://github.com/ludwig-ai/ludwig-docs/raw/master/docs/images/ludwig_hero_smaller.jpg" height="150">