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- contributing
- contributing
- documentation
- project_overview
- tests
- installation
- migration
- benchmark
- automl
- AutoML-Benchmark
- basic_example
- benchmark_on_openml
- important_params
- specific_task_fold_example
- concepts
- authentication
- benchmarking
- data
- flows
- runs
- sharing
- tagging
- tasks
- contributing
- OpenML-Docs
- Style
- backend
- API-development
- Datasets
- Java-App
- Local-Installation
- clients
- Client-API-Standards
- Rest
- metadata_definition
- resources
- website
- Dash
- Flask
- React
- Website
- data
- specs
- use
- ecosystem
- Java
- MOA
- Python_extensions
- Rest
- Scikit-learn
- basic_tutorial
- datasets_tutorial
- Weka
- mlr
- examples
- 20_basic
- introduction_tutorial
- simple_datasets_tutorial
- simple_flows_and_runs_tutorial
- simple_suites_tutorial
- 30_extended
- benchmark_with_optunahub
- configure_logging
- create_upload_tutorial
- custom_flow_
- datasets_tutorial
- fetch_evaluations_tutorial
- fetch_runtimes_tutorial
- flow_id_tutorial
- flows_and_runs_tutorial
- plot_svm_hyperparameters_tutorial
- run_setup_tutorial
- study_tutorial
- suites_tutorial
- task_manual_iteration_tutorial
- tasks_tutorial
- 40_paper
- 2015_neurips_feurer_example
- 2018_ida_strang_example
- 2018_kdd_rijn_example
- 2018_neurips_perrone_example
- SUMMARY
- help
- intro
- Governance
- showcase
- terms
- julia
- notebooks/getting_started
- python
- CONTRIBUTING
- ISSUE_TEMPLATE
- PULL_REQUEST_TEMPLATE
- docker/readme
- docs
- contributing
- extensions
- progress
- usage
- extensions
- progress
- usage
- pytorch
- API reference
- Callbacks
- Custom Datasets
- Metrics
- OpenML Connection
- Trainer
- Examples
- Create Dataset and Task
- Custom Training Pipelines
- Class Weighting
- Custom Learning Rate Scheduler
- Custom Loss Function
- Custom Optimizer
- Using Models from other Frameworks
- HuggingFace
- Getting data from OpenML
- Images
- Image Classification Task
- Pretrained Transformer Image Classification Task
- Sequential/Sequential Classification Task
- Tabular/Tabular Classification
- Upload results to OpenML
- Integrations of OpenML in PyTorch
- Limitations of the API
- Philosophy behind the API Design
- reference
- SUMMARY
- __version__
- _api_calls
- base
- cli
- config
- datasets
- data_feature
- dataset
- functions
- evaluations
- evaluation
- functions
- exceptions
- extensions
- extension_interface
- functions
- sklearn
- extension
- flows
- flow
- functions
- runs
- functions
- run
- trace
- setups
- functions
- setup
- study
- functions
- study
- tasks
- functions
- split
- task
- testing
- utils
- r
- search
- services
- tensorflow
- API reference
- Config
- OpenML integration
- Docker reference/Docker
- Examples
- tf_image_classification_Indoorscenes_dataset
- tf_image_classification_sanity_check
- tf_image_classification
- tf_pretrained_model_Indoorscenes_dataset
- tf_transfer_learning
- Limitations of the API
- openml_tensorflow
- __init__
- config
- extension
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