AutoDLComp19

AutoDL Competition 2019

Installation

Activate a conda python3.5 environment then run

bash install/requirements_gcc.sh
pip install -r install/requirements.txt
bash install/requirements_torch_cuda100.sh
bash install/install_winner_cv.sh
bash install/install_winner_speech.sh
bash install/install_just.sh        # Optional command runner
bash install/install_precommit.sh   # Developer dependency

Usage

Running locally

To run the competition evaluation locally run

python -m src.competition.run_local_test \
    --dataset_dir DATASET_DIR \
    --code_dir src \
    --model_config_name CONFIG.yaml \
    --experiment_group EXPERIMENT_GROUP \
    --experiment_name EXPERIMENT_NAME \
    --time_budget 1200 \

CONFIG corresponds to one of the names of the general configs in src/configs/. If this argument is ommited, src/configs/default.yaml is used.

You can use --time_budget_approx <LOWER_TIME> and --time_budget <ACTUAL_TIME> to simulate cutting a run with budget <ACTUAL_TIME> after <LOWER_TIME> seconds.

If you want to overwrite the output dir (for repeated local testing for example), supply the --overwrite flag.

Do not run pre-commit hooks

To commit without runnning pre-commit use git commit --no-verify -m <COMMIT MESSAGE>.

Generate a performance matrix

0. create available_datasets.py dynamically (to be implemented)

1. Create the arguments for the HPO and run them on META as follows

python submission/create_hpo_args.py --command_file_name ARGS_FILE_NAME #--> args file outputted
sbatch submission/meta_kakaobrain_optimized_per_dataset.sh # --> set ARGS_FILE parameter to newly created ARGS_FILE_NAME, set experiment_group to EXPERIMENT_DIR, set budgets

2. Generate incumbent configs

mkdir src/configs/INC_OUTPUT_DIR
python src/hpo/incumbents_to_config.py --output_dir src/configs/INC_OUTPUT_DIR --experiment_group_dir EXPERIMENT_DIR # --> .yaml configs outputted to EXPERIMENT_DIR

3. Evaluate configurations

python submission/create_datasets_x_configs_args.py --configs_path src/configs/INC_OUTPUT_DIR --command_file_name EVAL_ARGS_FILE_NAME # --> EVAL_ARGS_FILE_NAME stored in submission/
sbatch submission/meta_kakaobrain_datasets_x_configs.sh # --> set ARGS_FILE to EVAL_ARGS_FILE_NAME, set --experiment_group to EVALUATION_DIR_PATH, evaluations stored in EVALUATION_DIR_PATH

4. Once the evaluation directory has been generated, generate the pandas DataFrames and csv files with the following command

python src/hpo/performance_matrix_from_evaluation.py --experiment_group_dir EVALUATION_DIR_PATH

Pre-Computing meta features

To pre-compute meta features run

python -m src.meta_features.precompute_meta_features --dataset_path DATASET_PATH

the default output_path is src/meta_features/meta_features.yaml.

Making a submission

To create a submission .zip for the codalab platform run

python submission/codalab.py

This uses the settings in src/configs/default.yaml.

Project Structure

├── experiments/                           <<  Files generated during runtime
│
├── install/                               <<  Requirements and scripts for installation
│
├── src/                                   <<  Source code
│   └── winner_<TRACK>/                    <<  Winner code for <TRACK>
│   └── competition/                       <<  Competition source code
│       └── run_local_test.py              <<  Execute competition evaluation locally
│
├── submission/                            <<  Submission utilities
│    └── competition.py                    <<  Create codalab submission
│
└── justfile                               <<  Command runner file akin to Makefile

License

Apache 2.0

stjordanis / autodlcomp19 Goto Github PK

autodlcomp19's Introduction