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Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations.

Project description

Declarative deep learning framework built for scale and efficiency.

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📖 What is Ludwig?

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

Key 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.

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💾 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

Running

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

instruction input output
Give three tips for staying healthy. 1.Eat a balanced diet and make sure to include...
Arrange the items given below in the order to ... cake, me, eating I eating cake.
Write an introductory paragraph about a famous... Michelle Obama Michelle Obama is an inspirational woman who r...
... ... ...

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:

movie_title content_rating genres runtime top_critic review_content recommended
Deliver Us from Evil R Action & Adventure, Horror 117.0 TRUE Director Scott Derrickson and his co-writer, Paul Harris Boardman, deliver a routine procedural with unremarkable frights. 0
Barbara PG-13 Art House & International, Drama 105.0 FALSE Somehow, in this stirring narrative, Barbara manages to keep hold of her principles, and her humanity and courage, and battles to save a dissident teenage girl whose life the Communists are trying to destroy. 1
Horrible Bosses R Comedy 98.0 FALSE These bosses cannot justify either murder or lasting comic memories, fatally compromising a farce that could have been great but ends up merely mediocre. 0
... ... ... ... ... ... ...

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

🔬 Example Use Cases

💡 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!

🤝 Join the community to build Ludwig with us

Ludwig is an actively managed open-source project that relies on contributions from folks just like you. Consider joining the active group of Ludwig contributors to make Ludwig an even more accessible and feature rich framework for everyone to use!


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