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What is Ludwig?

Ludwig is an open-source, declarative machine learning framework that makes it easy to define deep learning pipelines with a simple and flexible data-driven configuration system. Ludwig is suitable for a wide variety of AI tasks, and is hosted by the Linux Foundation AI & Data.

Ludwig allows users to define their deep learning pipeline by simply providing a configuration file, which lists the inputs and outputs, and their respective data types. Ludwig will then assemble and train a deep learning model and based on the configuration file, determine how inputs and outputs are preprocessed, encoded, decoded and which metrics and loss criterion to use.

Writing a configuration file for Ludwig is easy. The configuration file flexibility allows for full control of every aspect of the end-to-end pipeline. This includes exploring state-of-the-art model architectures, running a hyperparameter search, scaling up to larger than available memory datasets and multi-node clusters, and finally serving the best model in production. All of this is achieved through simple configuration file changes.

Finally, the use of abstract interfaces throughout the codebase makes it easy for users to extend Ludwig by adding new models, metrics, losses, preprocessing functions and register them to make them available immediately in the configuration system.

Main Features

Data-Driven configuration system

A config YAML file that describes the schema of your data (input features, output features, and their types) is all you need to start training deep learning models. Ludwig uses declared features to compose a deep learning model accordingly.

input_features:
  - name: data_column_1
    type: number
  - name: data_column_2
    type: category
  - name: data_column_3
    type: text
  - name: data_column_4
    type: image
  ...

output_features:
  - name: data_column_5
    type: number
  - name: data_column_6
    type: category
  ...

Training, prediction, and evaluation from the command line

Simple commands can be used to train models and predict new data.

ludwig train --config config.yaml --dataset data.csv
ludwig predict --model_path results/experiment_run/model --dataset test.csv
ludwig eval --model_path results/experiment_run/model --dataset test.csv

Programmatic API

Ludwig also provides a simple programmatic API for all of the functionality described above and more.

from ludwig.api import LudwigModel

# train a model
config = {
    "input_features": [...],
    "output_features": [...],
}
model = LudwigModel(config)
data = pd.read_csv("data.csv")
train_stats, _, model_dir = model.train(data)

# or load a model
model = LudwigModel.load(model_dir)

# obtain predictions
predictions = model.predict(data)

Distributed training

Train models in a distributed setting using Horovod, which allows training on a single machine with multiple GPUs or multiple machines with multiple GPUs.

Serving

Serve models using FastAPI.

ludwig serve --model_path ./results/experiment_run/model
curl http://0.0.0.0:8000/predict -X POST -F "movie_title=Friends With Money" -F "content_rating=R" -F "genres=Art House & International, Comedy, Drama" -F "runtime=88.0" -F "top_critic=TRUE" -F "review_content=The cast is terrific, the movie isn't."

Hyperparameter optimization

Run hyperparameter optimization locally or using Ray Tune.
```
ludwig hyperopt --config config.yaml --dataset data.csv
```
AutoML

Ludwig AutoML takes a dataset, the target column, and a time budget, and returns a trained Ludwig model.
Third-Party integrations

Ludwig provides an extendable interface to integrate with third-party systems for tracking experiments. Third-party integrations exist for Comet ML, Weights & Biases, WhyLabs and MLFlow.
Extensibility

Ludwig is built from the ground up with extensibility in mind. It is easy to add new data types by implementing clear, well-documented abstract classes that define functions to preprocess, encode, and decode data.

Furthermore, new torch nn.Module models can be easily added by them to a registry. This encourages reuse and sharing new models with the community. Refer to the Developer Guide for further details.

Quick Start

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

Step 1: Install

Install from PyPi. Be aware that Ludwig requires Python 3.7+.

pip install ludwig

Step 2: Define a configuration

Create a config that describes the schema of your data.

Assume we have a text classification task, with data containing a sentence and class column like the following.

sentence	class
Former president Barack Obama ...	politics
Juventus hired Cristiano Ronaldo ...	sport
LeBron James joins the Lakers ...	sport
...	...

A configuration will look like this.

input_features:
- name: sentence
  type: text

output_features:
- name: class
  type: category

Starting from a simple config like the one above, 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.

input_features:
- name: sentence
  type: text
  encoder: transformer
  layers: 6
  embedding_size: 512

output_features:
- name: class
  type: category
  loss: cross_entropy

trainer:
  epochs: 50
  batch_size: 64
  optimizer:
    type: adamw
    beat1: 0.9
  learning_rate: 0.001

backend:
  type: ray
  cache_format: parquet
  processor:
    type: dask
  trainer:
    use_gpu: true
    num_workers: 4
    resources_per_worker:
      CPU: 4
      GPU: 1

hyperopt:
  metric: f1
  sampler: random
  parameters:
    title.num_layers:
      lower: 1
      upper: 5
    trainer.learning_rate:
      values: [0.01, 0.003, 0.001]

For details on what can be configured, check out Ludwig Configuration docs.

Step 3: Train a model

Simple commands can be used to train models and predict new data.

ludwig train --config config.yaml --dataset data.csv

Step 4: Predict and evaluate

The training process will produce a model that can be used for evaluating on and obtaining predictions for new data.

ludwig predict –model path/to/trained/model –dataset heldout.csv
ludwig evaluate –model path/to/trained/model –dataset heldout.csv

Step 5: Visualize

Ludwig provides a suite of visualization tools allows you to analyze models' training and test performance and to compare them.

ludwig visualize --visualization compare_performance --test_statistics path/to/test_statistics_model_1.json path/to/test_statistics_model_2.json

For the full set of visualization see the Visualization Guide.

Step 6: Happy modeling!

Try applying Ludwig to your data. Reach out if you have any questions.

Advantages

Ludwig is a profound utility for research scientists, data scientists, and machine learning engineers.

Minimal machine learning boilerplate

Ludwig takes care of the engineering complexity of deep 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 newly registered 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, and MLflow.

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 Torschscript bundles.

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

Tutorials

Example Use Cases

More Information

Full official documentation.

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!

Allensmile / ludwig