autogluon | AutoGluon : AutoML for Image , Text , and Tabular Data | Machine Learning library

 by   awslabs Python Version: v0.4.0 License: Apache-2.0

kandi X-RAY | autogluon Summary

kandi X-RAY | autogluon Summary

autogluon is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. autogluon has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However autogluon build file is not available. You can install using 'pip install autogluon' or download it from GitHub, PyPI.

Install Instructions | Documentation (Stable | Latest). AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on image, text, and tabular data.

            kandi-support Support

              autogluon has a medium active ecosystem.
              It has 4341 star(s) with 570 fork(s). There are 84 watchers for this library.
              It had no major release in the last 12 months.
              There are 126 open issues and 516 have been closed. On average issues are closed in 181 days. There are 10 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of autogluon is v0.4.0

            kandi-Quality Quality

              autogluon has 0 bugs and 0 code smells.

            kandi-Security Security

              autogluon has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              autogluon code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              autogluon is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              autogluon releases are available to install and integrate.
              Deployable package is available in PyPI.
              autogluon has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              autogluon saves you 18426 person hours of effort in developing the same functionality from scratch.
              It has 46965 lines of code, 3441 functions and 494 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed autogluon and discovered the below as its top functions. This is intended to give you an instant insight into autogluon implemented functionality, and help decide if they suit your requirements.
            • Trains the train function .
            • Generate config .
            • Perform a permutation feature importance .
            • Default implementation of early stopping
            • Computes the pac - score of a solution .
            • Distill the specified data point .
            • Executes SSH command .
            • Train a network .
            • Evaluate predicted predictions .
            • Performs a multi - head optimization .
            Get all kandi verified functions for this library.

            autogluon Key Features

            No Key Features are available at this moment for autogluon.

            autogluon Examples and Code Snippets

            Ensemble One-Shot NAS,Our Trained Model / Checkpoint,Supernet
            Pythondot img1Lines of Code : 92dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            def get_args():
                parser = argparse.ArgumentParser("OneShot_cifar_Experiments_Configuration")
                parser.add_argument('--signal', type=str, default='different_hpo', help='describe:glboal_hpo/')
                parser.add_argument('--different-hpo', action='sto  
            Ensemble One-Shot NAS,Usage,2. Train Supernet
            Pythondot img2Lines of Code : 16dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            cd src/Supernet_cifar
            python3 --num-classes 10 --signal different_hpo --different-hpo --num-trials 16 --total-iters 7800 --batch-size 64 --block 4 --lr-range "0.01,0.2" --wd-range "4e-5,5e-3"
            python3 --num-classes 10 --signal glboa  
            Rdot img3Lines of Code : 11dot img3License : Weak Copyleft (LGPL-3.0)
            copy iconCopy
              # Instantiate Learner
              lrn = LearnerClassifKerasFF$new()
              # Set Learner Hyperparams
              lrn$param_set$values$epochs = 50
              lrn$param_set$values$layer_units = 12
              # Train and Predict
            autogluon - prepare glue
            Pythondot img4Lines of Code : 603dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            # Disclaimer! The script here is partially based on
            # and
            import os
            import shutil
            import ar  
            autogluon - dataset
            Pythondot img5Lines of Code : 474dot img5License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            import abc
            import os
            import pandas as pd
            from autogluon.multimodal.constants import (
            from autogluon.multimodal.utils import download
            # TODO: release t  
            autogluon - automm distillation pawsx
            Pythondot img6Lines of Code : 193dot img6License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            import argparse
            from autogluon.multimodal import MultiModalPredictor
            from datasets import load_dataset
            from time import time
            import os
            import pandas as pd
            PAWS_TASKS = ["en", "de", "es", "fr", "ja", "ko", "zh"]
            def tasks_to_id(pawsx_tasks):

            Community Discussions


            How to install mxnet on google colab?
            Asked 2021-Sep-26 at 13:55

            I'm trying to install mxnet with gpu on colab.

            I guess current colab has cuda 11.1 installed by default as



            Answered 2021-Sep-25 at 19:06

            The following approach works for cuda-10.0 and cuda-11.0:



            pickled python machine learning model uses hardcoded paths, doesn't run on other machine - what to do?
            Asked 2021-Feb-08 at 11:06

            I use AutoGluon to create ML models locally on my computer. Now I want to deploy them through AWS, but I realized that all the pickle files created in the process use hardcoded path references to other pickle files:


            I use cloudpickle.dump(predictor, open('FINAL_MODEL.pkl', 'wb')) to pickle the final ensemble model, but AutoGluon creates numerous other pickle files of the individual models, which are then referenced as /home/myname/Desktop/ETC_PATH/AutoGluon/models/ and /home/myname/Desktop/ETC_PATH/AutoGluon/models/specific_model/ and so forth...

            How can I achieve that all absolute paths everywhere are replaced by relative paths like root/AutoGluon/WHATEVER_PATH, where root could be set to anything, depending on where the model is later saved.

            Any pointers would be helpful.

            EDIT: I'm reasonably sure I found the problem. If, instead of loading FINAL_MODEL.pkl (that seems to hardcode paths) I use AutoGluon's predictor = task.load(model_dir) it should find all dependencies correctly, whether or not the AutoGluon folder as a whole was moved. This issue on github helped



            Answered 2021-Feb-08 at 11:06

            EDIT: This solved the problem: If, instead of loading FINAL_MODEL.pkl (that seems to hardcode paths) I use AutoGluon's predictor = task.load(model_dir) it should find all dependencies correctly, whether or not the AutoGluon folder as a whole was moved. This issue on github helped



            evaluate the output of autoML results
            Asked 2020-May-05 at 03:47

            How do I interpret following results? What is the best possible algorithm to train based on autogluon summary?



            Answered 2020-May-05 at 03:47

            weighted_ensemble_k0_l2 is the best result in terms of validation score (score_val) because it has the highest value. You may wish to do predictor.leaderboard(test_data) to get the test scores for each of the models.

            Note that the result shows a negative score because AutoGluon always considers higher to be better. If a particular metric such as logloss prefers lower values to be better, AutoGluon flips the sign of the metric. I would guess a val_score of 0 would be a perfect score in your case.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install autogluon

            You can install using 'pip install autogluon' or download it from GitHub, PyPI.
            You can use autogluon like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            We are actively accepting code contributions to the AutoGluon project. If you are interested in contributing to AutoGluon, please read the Contributing Guide to get started.
            Find more information at:

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            gh repo clone awslabs/autogluon

          • sshUrl


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