path: root/python-ludwig.spec

%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
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<div align="center">

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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)
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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)
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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