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+/ludwig-0.7.4.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-ludwig
+Version:	0.7.4
+Release:	1
+Summary:	Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations.
+License:	Apache 2.0
+URL:		https://github.com/ludwig-ai/ludwig
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/01/46/73a9a279ce63ab0de902ec472bf27c7f86779d72ca3de7dc68f47b374336/ludwig-0.7.4.tar.gz
+BuildArch:	noarch
+
+
+%description
+![Ludwig logo](https://github.com/ludwig-ai/ludwig-docs/raw/master/docs/images/ludwig_hero.png "Ludwig logo")
+
+<div align="center">
+
+[![PyPI version](https://badge.fury.io/py/ludwig.svg)](https://badge.fury.io/py/ludwig)
+[![Commit Activity](https://img.shields.io/github/commit-activity/m/ludwig-ai/ludwig)](https://img.shields.io/github/commit-activity/m/ludwig-ai/ludwig)
+[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/4210/badge)](https://bestpractices.coreinfrastructure.org/projects/4210)
+[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ)
+
+[![DockerHub](https://img.shields.io/docker/pulls/ludwigai/ludwig.svg)](https://hub.docker.com/r/ludwigai)
+[![Downloads](https://pepy.tech/badge/ludwig)](https://pepy.tech/project/ludwig)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/ludwig-ai/ludwig/blob/master/LICENSE)
+[![Twitter](https://img.shields.io/twitter/follow/ludwig_ai.svg?style=social&logo=twitter)](https://twitter.com/ludwig_ai)
+
+Full Documentation: [ludwig.ai](https://ludwig.ai)
+
+</div>
+
+# 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.
+
+![img](https://raw.githubusercontent.com/ludwig-ai/ludwig-docs/master/docs/images/ludwig_legos_unanimated.gif)
+
+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.
+
+![img](images/why_declarative.png)
+
+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 using [Horovod](https://github.com/horovod/horovod),
+  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: 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](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 Torschscript 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!
+
+<a href="https://github.com/ludwig-ai/ludwig/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=ludwig-ai/ludwig" />
+</a><br/>
+
+# 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)
+
+%package -n python3-ludwig
+Summary:	Declarative machine learning: End-to-end machine learning pipelines using data-driven configurations.
+Provides:	python-ludwig
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-ludwig
+![Ludwig logo](https://github.com/ludwig-ai/ludwig-docs/raw/master/docs/images/ludwig_hero.png "Ludwig logo")
+
+<div align="center">
+
+[![PyPI version](https://badge.fury.io/py/ludwig.svg)](https://badge.fury.io/py/ludwig)
+[![Commit Activity](https://img.shields.io/github/commit-activity/m/ludwig-ai/ludwig)](https://img.shields.io/github/commit-activity/m/ludwig-ai/ludwig)
+[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/4210/badge)](https://bestpractices.coreinfrastructure.org/projects/4210)
+[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ)
+
+[![DockerHub](https://img.shields.io/docker/pulls/ludwigai/ludwig.svg)](https://hub.docker.com/r/ludwigai)
+[![Downloads](https://pepy.tech/badge/ludwig)](https://pepy.tech/project/ludwig)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/ludwig-ai/ludwig/blob/master/LICENSE)
+[![Twitter](https://img.shields.io/twitter/follow/ludwig_ai.svg?style=social&logo=twitter)](https://twitter.com/ludwig_ai)
+
+Full Documentation: [ludwig.ai](https://ludwig.ai)
+
+</div>
+
+# 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.
+
+![img](https://raw.githubusercontent.com/ludwig-ai/ludwig-docs/master/docs/images/ludwig_legos_unanimated.gif)
+
+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.
+
+![img](images/why_declarative.png)
+
+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 using [Horovod](https://github.com/horovod/horovod),
+  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: 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](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 Torschscript 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!
+
+<a href="https://github.com/ludwig-ai/ludwig/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=ludwig-ai/ludwig" />
+</a><br/>
+
+# 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)
+
+%package help
+Summary:	Development documents and examples for ludwig
+Provides:	python3-ludwig-doc
+%description help
+![Ludwig logo](https://github.com/ludwig-ai/ludwig-docs/raw/master/docs/images/ludwig_hero.png "Ludwig logo")
+
+<div align="center">
+
+[![PyPI version](https://badge.fury.io/py/ludwig.svg)](https://badge.fury.io/py/ludwig)
+[![Commit Activity](https://img.shields.io/github/commit-activity/m/ludwig-ai/ludwig)](https://img.shields.io/github/commit-activity/m/ludwig-ai/ludwig)
+[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/4210/badge)](https://bestpractices.coreinfrastructure.org/projects/4210)
+[![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ)
+
+[![DockerHub](https://img.shields.io/docker/pulls/ludwigai/ludwig.svg)](https://hub.docker.com/r/ludwigai)
+[![Downloads](https://pepy.tech/badge/ludwig)](https://pepy.tech/project/ludwig)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/ludwig-ai/ludwig/blob/master/LICENSE)
+[![Twitter](https://img.shields.io/twitter/follow/ludwig_ai.svg?style=social&logo=twitter)](https://twitter.com/ludwig_ai)
+
+Full Documentation: [ludwig.ai](https://ludwig.ai)
+
+</div>
+
+# 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.
+
+![img](https://raw.githubusercontent.com/ludwig-ai/ludwig-docs/master/docs/images/ludwig_legos_unanimated.gif)
+
+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.
+
+![img](images/why_declarative.png)
+
+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 using [Horovod](https://github.com/horovod/horovod),
+  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: 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](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 Torschscript 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!
+
+<a href="https://github.com/ludwig-ai/ludwig/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=ludwig-ai/ludwig" />
+</a><br/>
+
+# 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)
+
+%prep
+%autosetup -n ludwig-0.7.4
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-ludwig -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.4-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..be2aa64
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+1bd9df7f8551db58974751e85809aa36  ludwig-0.7.4.tar.gz