AI之AutoML：Ludwig(无需编写代码/易于使用的界面和可视化自动机器学习工具)的简介、安装、使用方法之详细攻略

目录

Ludwig的简介

Ludwig(无需编写代码/易于使用的界面和可视化自动机器学习工具)的简介

Ludwig的安装

Ludwig的使用方法

Ludwig的简介

Ludwig(无需编写代码/易于使用的界面和可视化自动机器学习工具)的简介

GitHUb：GitHub - ludwig-ai/ludwig: Data-centric declarative deep learning framework

Ludwig的安装

pip install ludwigpip install -i https://pypi.tuna./simple ludwig

Microsoft Windows [版本 10.0.19044.2728](c) Microsoft Corporation。保留所有权利。C:\Windows\system32>pip install -i https://pypi.tuna./simple ludwigLooking in indexes: https://pypi.tuna./simpleCollecting ludwigDownloading https://pypi.tuna./packages/01/46/73a9a279ce63ab0de902ec472bf27c7f86779d72ca3de7dc68f47b374336/ludwig-0.7.4.tar.gz (874 kB)|████████████████████████████████| 874 kB 939 kB/sInstalling build dependencies ... doneGetting requirements to build wheel ... donePreparing wheel metadata ... doneRequirement already satisfied: absl-py in d:\programdata\anaconda3\lib\site-packages (from ludwig) (1.4.0)Collecting experiment-impact-trackerDownloading https://pypi.tuna./packages/3c/ce/19d9b83a78998aaaf195af8217dc963004595156d3497c4f490dc7680d1e/experiment_impact_tracker-0.1.8-py2.py3-none-any.whl (10.5 MB)|████████████████████████████████| 10.5 MB 656 kB/sCollecting pyarrowUsing cached 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for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=385b1f4f4f6218dda677c2f9d032a4a3c9b46c1fa7c9170d6d0c04c85cfb130eStored in directory: c:\users\99386\appdata\local\pip\cache\wheels\3c\4c\0b\b17aca42475c28654c70c60038563b3b0a1ade6a2f97933363Successfully built ludwig bootstrapped bs4 gpustat kaggle pylatex sacremosesInstalling collected packages: ansicon, torch, ratelim, ordered-set, marshmallow, jinxed, geographiclib, whylogs-sketching, whylabs-client, typing-inspect, torchdata, tokenizers, shapely, pylatex, py-cpuinfo, psutil, protobuf, progiter, nvidia-ml-py, marshmallow-enum, geopy, geocoder, deepdiff, commonmark, bs4, bootstrapped, blessed, xlwt, whylogs, transformers, torchtext, torchmetrics, torchinfo, torchaudio, sacremoses, rich, retry, pyarrow, marshmallow-jsonschema, marshmallow-dataclass, kaggle, jsonschema, gpustat, experiment-impact-tracker, dataclasses-json, ludwigAttempting uninstall: torchFound existing installation: torch 1.9.0Uninstalling torch-1.9.0:Successfully uninstalled torch-1.9.0Attempting uninstall: tokenizersFound existing installation: tokenizers 0.13.2Uninstalling tokenizers-0.13.2:Successfully uninstalled tokenizers-0.13.2Attempting uninstall: psutilFound existing installation: psutil 5.8.0Uninstalling psutil-5.8.0:Successfully uninstalled psutil-5.8.0Attempting uninstall: protobufFound existing installation: protobuf 3.19.1Uninstalling protobuf-3.19.1:Successfully uninstalled protobuf-3.19.1Attempting uninstall: transformersFound existing installation: transformers 4.27.1Uninstalling transformers-4.27.1:Successfully uninstalled transformers-4.27.1Attempting uninstall: torchtextFound existing installation: torchtext 0.10.0Uninstalling torchtext-0.10.0:Successfully uninstalled torchtext-0.10.0Attempting uninstall: jsonschemaFound existing installation: jsonschema 4.4.0Uninstalling jsonschema-4.4.0:Successfully uninstalled jsonschema-4.4.0ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.spyder 5.1.5 requires pyqt5<5.13, which is not installed.spyder 5.1.5 requires pyqtwebengine<5.13, which is not installed.conda-repo-cli 1.0.4 requires pathlib, which is not installed.tensorflow-intel 2.11.0 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.jupyter-server 1.13.5 requires pywinpty<2; os_name == "nt", but you have pywinpty 2.0.2 which is incompatible.Successfully installed ansicon-1.89.0 blessed-1.20.0 bootstrapped-0.0.2 bs4-0.0.1 commonmark-0.9.1 dataclasses-json-0.5.7 deepdiff-6.3.0 experiment-impact-tracker-0.1.8 geocoder-1.38.1 geographiclib-2.0 geopy-2.3.0 gpustat-1.1 jinxed-1.2.0 jsonschema-4.6.2 kaggle-1.5.13 ludwig-0.7.4 marshmallow-3.19.0 marshmallow-dataclass-8.5.4 marshmallow-enum-1.5.1 marshmallow-jsonschema-0.13.0 nvidia-ml-py-11.525.112 ordered-set-4.1.0 progiter-1.3.0 protobuf-3.20.3 psutil-5.9.4 py-cpuinfo-9.0.0 pyarrow-11.0.0 pylatex-1.4.1 ratelim-0.1.6 retry-0.9.2 rich-12.4.4 sacremoses-0.0.53 shapely-2.0.1 tokenizers-0.12.1 torch-2.0.0 torchaudio-2.0.1 torchdata-0.6.0 torchinfo-1.7.2 torchmetrics-0.11.4 torchtext-0.15.1 transformers-4.21.3 typing-inspect-0.8.0 whylabs-client-0.4.6 whylogs-1.1.37 whylogs-sketching-3.4.1.dev3 xlwt-1.3.0C:\Windows\system32>

Ludwig的使用方法

1、基础用法

# Ludwig文本分类案例，使用IMDB电影评论数据集

import pandas as pdfrom ludwig.api import LudwigModel# 加载IMDB数据集df = pd.read_csv('/tf-datasets/tfds-test0.1.0/imdb_reviews/plain_text/0.1.0/{}.csv.gz'.format('train'))df = df.sample(frac=1).reset_index(drop=True)# 配置和训练模型model = LudwigModel(config={'input_features': [{'name': 'text', 'type': 'text'}], 'output_features': [{'name': 'sentiment', 'type': 'category'}]})train_stats = model.train(df)# 使用模型进行预测text = "This movie was great!"predictions = model.predict({'text': text})print(predictions['sentiment_predictions'][0]['class_name'])

AI之AutoML：Ludwig(无需编写代码/易于使用的界面和可视化自动机器学习工具)的简介 安装 使用方法之详细攻略

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