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MIM Installs OpenMMLab packages

Project description

MIM: MIM Installs OpenMMLab Packages

MIM provides a unified interface for launching and installing OpenMMLab projects and their extensions, and managing the OpenMMLab model zoo.

Major Features

  • Package Management

    You can use MIM to manage OpenMMLab codebases, install or uninstall them conveniently.

  • Model Management

    You can use MIM to manage OpenMMLab model zoo, e.g., download checkpoints by name, search checkpoints that meet specific criteria.

  • Unified Entrypoint for Scripts

    You can execute any script provided by all OpenMMLab codebases with unified commands. Train, test and inference become easier than ever. Besides, you can use gridsearch command for vanilla hyper-parameter search.

License

This project is released under the Apache 2.0 license.

Changelog

v0.1.1 was released in 13/6/2021.

Build custom projects with MIM

We provide some examples of how to build custom projects based on OpenMMLAB codebases and MIM in MIM-Example. Without worrying about copying codes and scripts from existing codebases, users can focus on developing new components and MIM helps integrate and run the new project.

Installation

Please refer to installation.md for installation.

Command

1. install

asciicast

  • command

    # install latest version of mmcv-full
    > mim install mmcv-full  # wheel
    # install 1.3.1
    > mim install mmcv-full==1.3.1
    # install master branch
    > mim install mmcv-full -f https://github.com/open-mmlab/mmcv.git
    
    # install latest version of mmcls
    > mim install mmcls
    # install 0.11.0
    > mim install mmcls==0.11.0  # v0.11.0
    # install master branch
    > mim install mmcls -f https://github.com/open-mmlab/mmclassification.git
    # install local repo
    > git clone https://github.com/open-mmlab/mmclassification.git
    > cd mmclassification
    > mim install .
    
    # install extension based on OpenMMLab
    mim install mmcls-project -f https://github.com/xxx/mmcls-project.git
    
  • api

    from mim import install
    
    # install mmcv
    install('mmcv-full')
    
    # install mmcls
    # install mmcls will automatically install mmcv if it is not installed
    install('mmcv-full', find_url='https://github.com/open-mmlab/mmcv.git')
    install('mmcv-full==1.3.1', find_url='https://github.com/open-mmlab/mmcv.git')
    
    # install extension based on OpenMMLab
    install('mmcls-project', find_url='https://github.com/xxx/mmcls-project.git')
    
2. uninstall

asciicast

  • command

    # uninstall mmcv
    > mim uninstall mmcv-full
    
    # uninstall mmcls
    > mim uninstall mmcls
    
  • api

    from mim import uninstall
    
    # uninstall mmcv
    uninstall('mmcv-full')
    
    # uninstall mmcls
    uninstall('mmcls)
    
3. list

asciicast

  • command

    > mim list
    > mim list --all
    
  • api

    from mim import list_package
    
    list_package()
    list_package(True)
    
4. search

asciicast

  • command

    > mim search mmcls
    > mim search mmcls==0.11.0 --remote
    > mim search mmcls --config resnet18_b16x8_cifar10
    > mim search mmcls --model resnet
    > mim search mmcls --dataset cifar-10
    > mim search mmcls --valid-field
    > mim search mmcls --condition 'bs>45,epoch>100'
    > mim search mmcls --condition 'bs>45 epoch>100'
    > mim search mmcls --condition '128<bs<=256'
    > mim search mmcls --sort bs epoch
    > mim search mmcls --field epoch bs weight
    > mim search mmcls --exclude-field weight paper
    
  • api

    from mim import get_model_info
    
    get_model_info('mmcls')
    get_model_info('mmcls==0.11.0', local=False)
    get_model_info('mmcls', models=['resnet'])
    get_model_info('mmcls', training_datasets=['cifar-10'])
    get_model_info('mmcls', filter_conditions='bs>45,epoch>100')
    get_model_info('mmcls', filter_conditions='bs>45 epoch>100')
    get_model_info('mmcls', filter_conditions='128<bs<=256')
    get_model_info('mmcls', sorted_fields=['bs', 'epoch'])
    get_model_info('mmcls', shown_fields=['epoch', 'bs', 'weight'])
    
5. download

asciicast

  • command

    > mim download mmcls --config resnet18_b16x8_cifar10
    > mim download mmcls --config resnet18_b16x8_cifar10 --dest .
    
  • api

    from mim import download
    
    download('mmcls', ['resnet18_b16x8_cifar10'])
    download('mmcls', ['resnet18_b16x8_cifar10'], dest_dir='.')
    
6. train

asciicast

  • command

    # Train models on a single server with CPU by setting `gpus` to 0 and
    # 'launcher' to 'none' (if applicable). The training script of the
    # corresponding codebase will fail if it doesn't support CPU training.
    > mim train mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 0
    # Train models on a single server with one GPU
    > mim train mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1
    # Train models on a single server with 4 GPUs and pytorch distributed
    > mim train mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 4 \
        --launcher pytorch
    # Train models on a slurm HPC with one 8-GPU node
    > mim train mmcls resnet101_b16x8_cifar10.py --launcher slurm --gpus 8 \
        --gpus-per-node 8 --partition partition_name --work-dir tmp
    # Print help messages of sub-command train
    > mim train -h
    # Print help messages of sub-command train and the training script of mmcls
    > mim train mmcls -h
    
  • api

    from mim import train
    
    train(repo='mmcls', config='resnet18_b16x8_cifar10.py', gpus=0,
          other_args='--work-dir tmp')
    train(repo='mmcls', config='resnet18_b16x8_cifar10.py', gpus=1,
          other_args='--work-dir tmp')
    train(repo='mmcls', config='resnet18_b16x8_cifar10.py', gpus=4,
          launcher='pytorch', other_args='--work-dir tmp')
    train(repo='mmcls', config='resnet18_b16x8_cifar10.py', gpus=8,
          launcher='slurm', gpus_per_node=8, partition='partition_name',
          other_args='--work-dir tmp')
    
7. test

asciicast

  • command

    # Test models on a single server with 1 GPU, report accuracy
    > mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
        tmp/epoch_3.pth --gpus 1 --metrics accuracy
    # Test models on a single server with 1 GPU, save predictions
    > mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
        tmp/epoch_3.pth --gpus 1 --out tmp.pkl
    # Test models on a single server with 4 GPUs, pytorch distributed,
    # report accuracy
    > mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
        tmp/epoch_3.pth --gpus 4 --launcher pytorch --metrics accuracy
    # Test models on a slurm HPC with one 8-GPU node, report accuracy
    > mim test mmcls resnet101_b16x8_cifar10.py --checkpoint \
        tmp/epoch_3.pth --gpus 8 --metrics accuracy --partition \
        partition_name --gpus-per-node 8 --launcher slurm
    # Print help messages of sub-command test
    > mim test -h
    # Print help messages of sub-command test and the testing script of mmcls
    > mim test mmcls -h
    
  • api

    from mim import test
    test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
         checkpoint='tmp/epoch_3.pth', gpus=1, other_args='--metrics accuracy')
    test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
         checkpoint='tmp/epoch_3.pth', gpus=1, other_args='--out tmp.pkl')
    test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
         checkpoint='tmp/epoch_3.pth', gpus=4, launcher='pytorch',
         other_args='--metrics accuracy')
    test(repo='mmcls', config='resnet101_b16x8_cifar10.py',
         checkpoint='tmp/epoch_3.pth', gpus=8, partition='partition_name',
         launcher='slurm', gpus_per_node=8, other_args='--metrics accuracy')
    
8. run

asciicast

  • command

    # Get the Flops of a model
    > mim run mmcls get_flops resnet101_b16x8_cifar10.py
    # Publish a model
    > mim run mmcls publish_model input.pth output.pth
    # Train models on a slurm HPC with one GPU
    > srun -p partition --gres=gpu:1 mim run mmcls train \
        resnet101_b16x8_cifar10.py --work-dir tmp
    # Test models on a slurm HPC with one GPU, report accuracy
    > srun -p partition --gres=gpu:1 mim run mmcls test \
        resnet101_b16x8_cifar10.py tmp/epoch_3.pth --metrics accuracy
    # Print help messages of sub-command run
    > mim run -h
    # Print help messages of sub-command run, list all available scripts in
    # codebase mmcls
    > mim run mmcls -h
    # Print help messages of sub-command run, print the help message of
    # training script in mmcls
    > mim run mmcls train -h
    
  • api

    from mim import run
    
    run(repo='mmcls', command='get_flops',
        other_args='resnet101_b16x8_cifar10.py')
    run(repo='mmcls', command='publish_model',
        other_args='input.pth output.pth')
    run(repo='mmcls', command='train',
        other_args='resnet101_b16x8_cifar10.py --work-dir tmp')
    run(repo='mmcls', command='test',
        other_args='resnet101_b16x8_cifar10.py tmp/epoch_3.pth --metrics accuracy')
    
9. gridsearch

asciicast

  • command

    # Parameter search on a single server with CPU by setting `gpus` to 0 and
    # 'launcher' to 'none' (if applicable). The training script of the
    # corresponding codebase will fail if it doesn't support CPU training.
    > mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 0 \
        --search-args '--optimizer.lr 1e-2 1e-3'
    # Parameter search with on a single server with one GPU, search learning
    # rate
    > mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \
        --search-args '--optimizer.lr 1e-2 1e-3'
    # Parameter search with on a single server with one GPU, search
    # weight_decay
    > mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \
        --search-args '--optimizer.weight_decay 1e-3 1e-4'
    # Parameter search with on a single server with one GPU, search learning
    # rate and weight_decay
    > mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \
        --search-args '--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay 1e-3 \
        1e-4'
    # Parameter search on a slurm HPC with one 8-GPU node, search learning
    # rate and weight_decay
    > mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 8 \
        --partition partition_name --gpus-per-node 8 --launcher slurm \
        --search-args '--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay 1e-3 \
        1e-4'
    # Parameter search on a slurm HPC with one 8-GPU node, search learning
    # rate and weight_decay, max parallel jobs is 2
    > mim gridsearch mmcls resnet101_b16x8_cifar10.py --work-dir tmp --gpus 8 \
        --partition partition_name --gpus-per-node 8 --launcher slurm \
        --max-jobs 2 --search-args '--optimizer.lr 1e-2 1e-3 \
        --optimizer.weight_decay 1e-3 1e-4'
    # Print the help message of sub-command search
    > mim gridsearch -h
    # Print the help message of sub-command search and the help message of the
    # training script of codebase mmcls
    > mim gridsearch mmcls -h
    
  • api

    from mim import gridsearch
    
    gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=0,
               search_args='--optimizer.lr 1e-2 1e-3',
               other_args='--work-dir tmp')
    gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=1,
               search_args='--optimizer.lr 1e-2 1e-3',
               other_args='--work-dir tmp')
    gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=1,
               search_args='--optimizer.weight_decay 1e-3 1e-4',
               other_args='--work-dir tmp')
    gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=1,
               search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'
                           '1e-3 1e-4',
               other_args='--work-dir tmp')
    gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=8,
               partition='partition_name', gpus_per_node=8, launcher='slurm',
               search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'
                           ' 1e-3 1e-4',
               other_args='--work-dir tmp')
    gridsearch(repo='mmcls', config='resnet101_b16x8_cifar10.py', gpus=8,
               partition='partition_name', gpus_per_node=8, launcher='slurm',
               max_workers=2,
               search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'
                           ' 1e-3 1e-4',
               other_args='--work-dir tmp')
    

Contributing

We appreciate all contributions to improve mim. Please refer to CONTRIBUTING.md for the contributing guideline.

License

This project is released under the Apache 2.0 license.

Project details


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