hydra.yaml

hydra:
  run:
    dir: ${path.log_dir}
  sweep:
    dir: ${oc.env:FUSION_BENCH_PROJECT_ROOT,"."}/outputs/multirun/${hydra.job.config_name}/${now:%Y-%m-%d_%H-%M-%S}
    subdir: ${hydra.job.num}
  launcher:
    _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher
  sweeper:
    _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper
    max_batch_size: null
    params: null
  help:
    app_name: fusion_bench
    header: == ${hydra.help.app_name} ==
    footer: 'Powered by Hydra (https://hydra.cc)

      Use --hydra-help to view Hydra specific help'
    template: '${hydra.help.header}


      fusion_bench is the command line interface for running model fusion benchmarks
      in the FusionBench project.

      It provides a flexible way to configure and execute various fusion algorithms
      on different model pools and evaluate them across multiple tasks.


      == Configuration groups ==

      Compose your configuration from these groups (method, modelpool, taskpool are
      the most important):


      $APP_CONFIG_GROUPS


      == Config ==

      You can override options, for example:

      fusion_bench method=task_arithmetic modelpool=clip-vit-base-patch32_svhn_and_mnist
      taskpool=clip-vit-base-patch32_svhn_and_mnist


      == Basic usage ==

      fusion_bench [--config-path CONFIG_PATH] [--config-name CONFIG_NAME] OPTION_1=VALUE_1
      OPTION_2=VALUE_2 ...


      == Key options ==

      --help, -h            : Print this help message and exit

      --hydra-help          : Hydra''s help

      --cfg, -c             : Show config instead of running [job|hydra|all]

      --config-path, -cp    : Overrides the config_path

      --config-name, -cn    : Overrides the config_name

      --shell-completion, -sc : Install or Uninstall shell completion


      For more detailed information on options and usage, please refer to the online
      documentation:

      https://tanganke.github.io/fusion_bench/cli/fusion_bench/


      ${hydra.help.footer}'
  hydra_help:
    template: 'Hydra (${hydra.runtime.version})

      See https://hydra.cc for more info.


      == Flags ==

      $FLAGS_HELP


      == Configuration groups ==

      Compose your configuration from those groups (For example, append hydra/job_logging=disabled
      to command line)


      $HYDRA_CONFIG_GROUPS


      Use ''--cfg hydra'' to Show the Hydra config.

      '
    hydra_help: ???
  hydra_logging:
    version: 1
    formatters:
      simple:
        format: '[%(asctime)s][HYDRA] %(message)s'
    handlers:
      console:
        class: logging.StreamHandler
        formatter: simple
        stream: ext://sys.stdout
    root:
      level: INFO
      handlers:
      - console
    loggers:
      logging_example:
        level: DEBUG
    disable_existing_loggers: false
  job_logging:
    version: 1
    formatters:
      simple:
        format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s'
      rich_handler:
        format: '%(message)s'
    handlers:
      console:
        class: rich.logging.RichHandler
        formatter: rich_handler
      file:
        class: logging.FileHandler
        formatter: simple
        filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log
    root:
      level: INFO
      handlers:
      - console
      - file
    disable_existing_loggers: false
  env: {}
  mode: RUN
  searchpath: []
  callbacks: {}
  output_subdir: ''
  overrides:
    hydra:
    - hydra.mode=RUN
    task:
    - path.log_dir="outputs/convnext-base-224/eurosat/batch_size=64,lr=0.01"
    - seed=0
    - method=classification/image_classification_finetune
    - method.max_epochs=-1
    - method.max_steps=4000
    - method.save_top_k=-1
    - method.save_interval=1000
    - method.save_on_train_epoch_end=false
    - method.optimizer.lr=0.01
    - method.lr_scheduler=null
    - method.dataloader_kwargs.batch_size=64
    - modelpool=ConvNextForImageClassification/convnext-base-224
    - modelpool.models._pretrained_.dataset_name=eurosat
    - +dataset/image_classification/train@modelpool.train_datasets=eurosat
    - +dataset/image_classification/test@modelpool.val_datasets=eurosat
  job:
    name: cli
    chdir: null
    override_dirname: +dataset/image_classification/test@modelpool.val_datasets=eurosat,+dataset/image_classification/train@modelpool.train_datasets=eurosat,method.dataloader_kwargs.batch_size=64,method.lr_scheduler=null,method.max_epochs=-1,method.max_steps=4000,method.optimizer.lr=0.01,method.save_interval=1000,method.save_on_train_epoch_end=false,method.save_top_k=-1,method=classification/image_classification_finetune,modelpool.models._pretrained_.dataset_name=eurosat,modelpool=ConvNextForImageClassification/convnext-base-224,path.log_dir="outputs/convnext-base-224/eurosat/batch_size=64,lr=0.01",seed=0
    id: ???
    num: ???
    config_name: model_fusion
    env_set:
      HYDRA_FULL_ERROR: ${oc.env:HYDRA_FULL_ERROR,1}
    env_copy: []
    config:
      override_dirname:
        kv_sep: '='
        item_sep: ','
        exclude_keys: []
  runtime:
    version: 1.3.2
    version_base: '1.3'
    cwd: /data/users/anke/fusion_bench
    config_sources:
    - path: hydra.conf
      schema: pkg
      provider: hydra
    - path: /data/users/anke/fusion_bench/config
      schema: file
      provider: main
    - path: ''
      schema: structured
      provider: schema
    output_dir: /data/users/anke/fusion_bench/outputs/convnext-base-224/eurosat/batch_size=64,lr=0.01
    choices:
      dataset/image_classification/test@modelpool.val_datasets: eurosat
      dataset/image_classification/train@modelpool.train_datasets: eurosat
      taskpool: dummy
      method: classification/image_classification_finetune
      modelpool: ConvNextForImageClassification/convnext-base-224
      path: default
      hydra: default
      hydra/env: default
      hydra/callbacks: null
      hydra/job_logging: rich_logging
      hydra/hydra_logging: default
      hydra/hydra_help: default
      hydra/help: fusion_bench_help
      hydra/sweeper: basic
      hydra/launcher: basic
      hydra/output: default
  verbose: false