Package: autodl-gpu
English |简体中文1. NeurIPS AutoDL Challenge 1'st Solution1stsolution forAutoDL Challenge@NeurIPS, competition rules can be found atAutoDL Competition.1.0.1. MotivationThere exists a series of common and tough problems in the real world, such as limited resources (CPU/ memory), skewed data, hand-craft features, model selection, network architecture details tuning, sensitivity of pre-trained models, sensitivity of hyperparameters and so on. How to solve them wholly and efficiently?1.0.2. SolutionAutoDL concentrates on developing generic algorithms for multi-label classification problems in ANY modalities: image, video, speech, text and tabular data without ANY human intervention.Ten secondsat the soonest, our solution achieved SOTA performances on all the 24 offline datasets and 15 online datasets, beating a number of top players in the world.1.1. Table of Contents1. NeurIPS AutoDL Challenge 1'st Solution1.0.1. Motivation1.0.2. Solution1.1. Table of Contents1.2. Features1.3. Evaluation1.4. Installation1.4.1. With pip1.5. Quick Tour1.5.1. Run local test tour1.5.2. Tour of Image Classification1.5.3. Tour of Video Classification1.5.4. Tour of Speech Classification1.5.5. Tour of Text Classification1.5.6. Tour of Tabular Classification1.6. Public Datasets1.6.1. Optional: Download public datasets1.6.2. Public datasets sample info1.7. Usage for AutoDL local development and testing1.8. Contributing1.9. Contact us1.10. Join the Community1.11. License1.2. FeaturesFull-AutoML/AutoDL: Fully automated Deep Learning without ANY human intervention covering the whole pipelines.Generic & Universal: Supporting ANY modality(image, video, speech, text, tabular) data, andANYclassification problems including binary-class, multi-class and multi-label problems.SOTA: Winner solution of AutoDL challenge, involving both tranditional machine learning models and deep learning model backbones.Out-of-the-Box: You can use the solution out-of-the-box.Fast: You can train your model inten secondsat the soonest to get highly competitive performance.Real-time: You can get the performance feedback(AUC score) in real time.1.3. EvaluationFeedback-phase leaderboard: DeepWisdom Top 1, average rank 1.2, won 4 out of 5 datasets.Final-phase leaderboard visualization: DeepWisdom Top 1, average rank 1.2, won 7 out of 10 datasets.1.4. InstallationThis repo is tested on Python 3.6+, PyTorch 1.0.0+ and TensorFlow 2.0.You should install AutoDL in avirtual environment. If you're unfamiliar with Python virtual environments, check out theuser guide.Create a virtual environment with the version of Python you're going to use and activate it.Now, if you want to use AutoDL, you can install it with pip.1.4.1. With pipAutoDL can be installed using pip as follows:pipinstallautodl-gpupipinstallautodl-gpu=1.0.01.5. Quick Tour1.5.1. Run local test tourseeQuick Tour - Run local test tour.1.5.2. Tour of Image ClassificationseeQuick Tour - Image Classification Demo.1.5.3. Tour of Video ClassificationseeQuick Tour - Video Classification Demo.1.5.4. Tour of Speech ClassificationseeQuick Tour - Speech Classification Demo.1.5.5. Tour of Text ClassificationseeQuick Tour - Text Classification Demo.1.5.6. Tour of Tabular ClassificationseeQuick Tour - Tabular Classification Demo.1.6. Public Datasets1.6.1. Optional: Download public datasetspythondownload_public_datasets.py1.6.2. Public datasets sample info#NameTypeDomainSizeSourceData (w/o test labels)Test labels1MunsterImageHWR18 MBMNISTmunster.datamunster.solution2CityImageObjects128 MBCifar-10city.datacity.solution3ChuckyImageObjects128 MBCifar-100chucky.datachucky.solution4PedroImagePeople377 MBPA-100Kpedro.datapedro.solution5DecalImageAerial73 MBNWPU VHR-10decal.datadecal.solution6HammerImageMedical111 MBHam10000hammer.datahammer.solution7KreaturVideoAction469 MBKTHkreatur.datakreatur.solution8Kreatur3VideoAction588 MBKTHkreatur3.datakreatur3.solution9KrautVideoAction1.9 GBKTHkraut.datakraut.solution10KatzeVideoAction1.9 GBKTHkatze.datakatze.solution11data01SpeechSpeaker1.8 GB--data01.datadata01.solution12data02SpeechEmotion53 MB--data02.datadat02.solution13data03SpeechAccent1.8 GB--data03.datadata03.solution14data04SpeechGenre469 MB--data04.datadata04.solution15data05SpeechLanguage208 MB--data05.datadata05.solution16O1TextComments828 KB--O1.dataO1.solution17O2TextEmotion25 MB--O2.dataO2.solution18O3TextNews88 MB--O3.dataO3.solution19O4TextSpam87 MB--O4.dataO4.solution20O5TextNews14 MB--O5.dataO5.solution21AdultTabularCensus2 MBAdultadult.dataadult.solution22DilbertTabular--162 MB--dilbert.datadilbert.solution23DigitsTabularHWR137 MBMNISTdigits.datadigits.solution24MadelineTabular--2.6 MB--madeline.datamadeline.solution1.7. Usage for AutoDL local development and testingw1. Git clone the repocd <path_to_your_directory>
git clone https://github.com/DeepWisdom/AutoDL.gitPrepare pretrained models.
Download modelspeech_model.h5and put it toAutoDL_sample_code_submission/at_speech/pretrained_models/directory.Optional: run in the exact same environment as on the challenge platform with docker.CPUcd path/to/autodl/
docker run -it -v "$(pwd):/app/codalab" -p 8888:8888 evariste/autodl:cpu-latestGPUnvidia-docker run -it -v "$(pwd):/app/codalab" -p 8888:8888 evariste/autodl:gpu-latestPrepare sample datasets, using the toy data inAutoDL_sample_dataor download new datasets.Run local testpython run_local_test.pyThe full usage ispython run_local_test.py -dataset_dir='AutoDL_sample_data/miniciao' -code_dir='AutoDL_sample_code_submission'Then you can view the real-time feedback with a learning curve by opening the
HTML page inAutoDL_scoring_output/.Details can be seen inAutoDL Challenge official starting_kit.1.8. ContributingFeel free to dive in!Open an issueor submit PRs.1.9. Contact us1.10. Join the CommunityScan QR code and join AutoDL community!1.11. LicenseApache License 2.0