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 # What is Ludwig? Ludwig is a [declarative machine learning framework](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/what_is_ludwig/#why-declarative-machine-learning-systems) that makes it easy to define machine learning pipelines using 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](https://lfaidata.foundation/). The configuration declares the input and output features, with their respective data types. Users can also specify additional parameters to preprocess, encode, and decode features, load from pre-trained models, compose the internal model architecture, set training parameters, or run hyperparameter optimization.  Ludwig will build an end-to-end machine learning pipeline automatically, using whatever is explicitly specified in the configuration, while falling back to smart defaults for any parameters that are not. # Declarative Machine Learning Ludwig’s declarative approach to machine learning empowers you to have full control of the components of the machine learning pipeline that you care about, while leaving it up to Ludwig to make reasonable decisions for the rest.  Analysts, scientists, engineers, and researchers use Ludwig to explore state-of-the-art model architectures, run hyperparameter search, scale up to larger than available memory datasets and multi-node clusters, and finally serve the best model in production. Finally, the use of abstract interfaces throughout the codebase makes it easy for users to extend Ludwig by adding new models, metrics, losses, and preprocessing functions that can be registered to make them immediately useable in the same unified configuration system. # Main Features - **[Data-Driven configuration system](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/how_ludwig_works)** 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. ```yaml 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](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/command_line_interface)** Simple commands can be used to train models and predict new data. ```shell 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](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/api/LudwigModel)** Ludwig also provides a simple programmatic API for all of the functionality described above and more. ```python 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](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/distributed_training)** Train models in a distributed setting which allows training on a single machine with multiple GPUs or multiple machines with multiple GPUs. - **[Serving](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/serving)** Serve models using FastAPI. ```shell 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](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/hyperopt)** Run hyperparameter optimization locally or using [Ray Tune](https://docs.ray.io/en/latest/tune/index.html). ```shell ludwig hyperopt --config config.yaml --dataset data.csv ``` - **[AutoML](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/automl)** Ludwig AutoML takes a dataset, the target column, and a time budget, and returns a trained Ludwig model. - **[Third-Party integrations](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/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](https://ludwig-ai.github.io/ludwig-docs/latest/developer_guide)** 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](https://ludwig-ai.github.io/ludwig-docs/latest/developer_guide) for further details. # Quick Start For a full tutorial, check out the official [getting started guide](https://ludwig-ai.github.io/ludwig-docs/latest/getting_started/), or take a look at end-to-end [Examples](https://ludwig-ai.github.io/ludwig-docs/latest/examples). ## Step 1: Install Install from PyPi. Be aware that Ludwig requires Python 3.8+. ```shell 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. ```yaml 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. ```yaml 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: *** 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](https://ludwig-ai.github.io/ludwig-docs/latest/configuration/) docs. ## Step 3: Train a model Simple commands can be used to train models and predict new data. ```shell 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. ```shell 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. ```shell 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](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/visualizations). ## Step 6: Happy modeling Try applying Ludwig to your data. [Reach out](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ) if you have any questions. # Advantages - **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](https://arxiv.org/abs/2111.04260) 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](https://ludwig-ai.github.io/ludwig-docs/latest/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](https://huggingface.co/docs/transformers/index). 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: ```shell 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](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/automl/) allows users to obtain trained models by providing just a dataset, the target column, and a time budget. ```python 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. ```shell ludwig serve --model_path=/path/to/model ``` Ludwig supports exporting models to efficient Torchscript bundles. ```shell ludwig export_torchscript -–model_path=/path/to/model ``` # Tutorials - [Text Classification](https://ludwig-ai.github.io/ludwig-docs/latest/examples/text_classification) - [Tabular Data Classification](https://ludwig-ai.github.io/ludwig-docs/latest/examples/adult_census_income) - [Image Classification](https://ludwig-ai.github.io/ludwig-docs/latest/examples/mnist) - [Multimodal Classification](https://ludwig-ai.github.io/ludwig-docs/latest/examples/multimodal_classification) # Example Use Cases - [Named Entity Recognition Tagging](https://ludwig-ai.github.io/ludwig-docs/latest/examples/ner_tagging) - [Natural Language Understanding](https://ludwig-ai.github.io/ludwig-docs/latest/examples/nlu) - [Machine Translation](https://ludwig-ai.github.io/ludwig-docs/latest/examples/machine_translation) - [Chit-Chat Dialogue Modeling through seq2seq](https://ludwig-ai.github.io/ludwig-docs/latest/examples/seq2seq) - [Sentiment Analysis](https://ludwig-ai.github.io/ludwig-docs/latest/examples/sentiment_analysis) - [One-shot Learning with Siamese Networks](https://ludwig-ai.github.io/ludwig-docs/latest/examples/oneshot) - [Visual Question Answering](https://ludwig-ai.github.io/ludwig-docs/latest/examples/visual_qa) - [Spoken Digit Speech Recognition](https://ludwig-ai.github.io/ludwig-docs/latest/examples/speech_recognition) - [Speaker Verification](https://ludwig-ai.github.io/ludwig-docs/latest/examples/speaker_verification) - [Binary Classification (Titanic)](https://ludwig-ai.github.io/ludwig-docs/latest/examples/titanic) - [Timeseries forecasting](https://ludwig-ai.github.io/ludwig-docs/latest/examples/forecasting) - [Timeseries forecasting (Weather)](https://ludwig-ai.github.io/ludwig-docs/latest/examples/weather) - [Movie rating prediction](https://ludwig-ai.github.io/ludwig-docs/latest/examples/movie_ratings) - [Multi-label classification](https://ludwig-ai.github.io/ludwig-docs/latest/examples/multi_label) - [Multi-Task Learning](https://ludwig-ai.github.io/ludwig-docs/latest/examples/multi_task) - [Simple Regression: Fuel Efficiency Prediction](https://ludwig-ai.github.io/ludwig-docs/latest/examples/fuel_efficiency) - [Fraud Detection](https://ludwig-ai.github.io/ludwig-docs/latest/examples/fraud) # More Information Read our publications on [Ludwig](https://arxiv.org/pdf/1909.07930.pdf), [declarative ML](https://arxiv.org/pdf/2107.08148.pdf), and [Ludwig’s SoTA benchmarks](https://openreview.net/pdf?id=hwjnu6qW7E4). Learn more about [how Ludwig works](https://ludwig-ai.github.io/ludwig-docs/latest/user_guide/how_ludwig_works/), [how to get started](https://ludwig-ai.github.io/ludwig-docs/latest/getting_started/), and work through more [examples](https://ludwig-ai.github.io/ludwig-docs/latest/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](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ) and follow us on [Twitter](https://twitter.com/ludwig_ai)! # 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!
# Getting Involved - [Slack](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ) - [Twitter](https://twitter.com/ludwig_ai) - [Medium](https://medium.com/ludwig-ai) - [GitHub Issues](https://github.com/ludwig-ai/ludwig/issues)

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