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.

Customization

You can use .mimrc for customization. Now we support customize default values of each sub-command. Please refer to customization.md for details.

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

command

# install latest version of onedl-mmcv
> mim install onedl-mmcv  # wheel
# install 1.5.0
> mim install onedl-mmcv==2.3.0

# install latest version of onedl-mmpretrain
> mim install onedl-mmpretrain
# install master branch
> mim install git+https://github.com/vbti-development/onedl-mmpretrain.git
# install local repo
> git clone https://github.com/vbti-development/onedl-mmpretrain.git
> cd mmclassification
> mim install .

# install extension based on OpenMMLab
mim install git+https://github.com/xxx/onedl-mmpretrain-project.git

api

from mim import install

# install mmcv
install('onedl-mmcv')

# install onedl-mmpretrain will automatically install mmcv if it is not installed
install('onedl-mmpretrain')

# install extension based on OpenMMLab
install('git+https://github.com/xxx/onedl-mmpretrain-project.git')

2. uninstall

command

# uninstall mmcv
> mim uninstall onedl-mmcv

# uninstall onedl-mmpretrain
> mim uninstall onedl-mmpretrain

api

from mim import uninstall

# uninstall mmcv
uninstall('onedl-mmcv')

# uninstall onedl-mmpretrain
uninstall('onedl-mmpretrain')

3. list

command
```
> mim list
> mim list --all
```

api

from mim import list_package

list_package()
list_package(True)

4. search

command

> mim search onedl-mmpretrain
> mim search onedl-mmpretrain==0.23.0 --remote
> mim search onedl-mmpretrain --config resnet18_8xb16_cifar10
> mim search onedl-mmpretrain --model resnet
> mim search onedl-mmpretrain --dataset cifar-10
> mim search onedl-mmpretrain --valid-field
> mim search onedl-mmpretrain --condition 'batch_size>45,epochs>100'
> mim search onedl-mmpretrain --condition 'batch_size>45 epochs>100'
> mim search onedl-mmpretrain --condition '128<batch_size<=256'
> mim search onedl-mmpretrain --sort batch_size epochs
> mim search onedl-mmpretrain --field epochs batch_size weight
> mim search onedl-mmpretrain --exclude-field weight paper

api

from mim import get_model_info

get_model_info('onedl-mmpretrain')
get_model_info('onedl-mmpretrain==0.23.0', local=False)
get_model_info('onedl-mmpretrain', models=['resnet'])
get_model_info('onedl-mmpretrain', training_datasets=['cifar-10'])
get_model_info('onedl-mmpretrain', filter_conditions='batch_size>45,epochs>100')
get_model_info('onedl-mmpretrain', filter_conditions='batch_size>45 epochs>100')
get_model_info('onedl-mmpretrain', filter_conditions='128<batch_size<=256')
get_model_info('onedl-mmpretrain', sorted_fields=['batch_size', 'epochs'])
get_model_info('onedl-mmpretrain', shown_fields=['epochs', 'batch_size', 'weight'])

5. download

command

> mim download onedl-mmpretrain --config resnet18_8xb16_cifar10
> mim download onedl-mmpretrain --config resnet18_8xb16_cifar10 --dest .

api

from mim import download

download('onedl-mmpretrain', ['resnet18_8xb16_cifar10'])
download('onedl-mmpretrain', ['resnet18_8xb16_cifar10'], dest_root='.')

6. train

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 onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 0
# Train models on a single server with one GPU
> mim train onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1
# Train models on a single server with 4 GPUs and pytorch distributed
> mim train onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 4 \
    --launcher pytorch
# Train models on a slurm HPC with one 8-GPU node
> mim train onedl-mmpretrain 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 onedl-mmpretrain
> mim train onedl-mmpretrain -h

api

from mim import train

train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=0,
      other_args=('--work-dir', 'tmp'))
train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=1,
      other_args=('--work-dir', 'tmp'))
train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=4,
      launcher='pytorch', other_args=('--work-dir', 'tmp'))
train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=8,
      launcher='slurm', gpus_per_node=8, partition='partition_name',
      other_args=('--work-dir', 'tmp'))

7. test

command

# Test models on a single server with 1 GPU, report accuracy
> mim test onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain
> mim test onedl-mmpretrain -h

api

from mim import test
test(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py',
     checkpoint='tmp/epoch_3.pth', gpus=1, other_args=('--metrics', 'accuracy'))
test(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py',
     checkpoint='tmp/epoch_3.pth', gpus=1, other_args=('--out', 'tmp.pkl'))
test(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py',
     checkpoint='tmp/epoch_3.pth', gpus=4, launcher='pytorch',
     other_args=('--metrics', 'accuracy'))
test(repo='onedl-mmpretrain', 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

command

# Get the Flops of a model
> mim run onedl-mmpretrain get_flops resnet101_b16x8_cifar10.py
# Publish a model
> mim run onedl-mmpretrain publish_model input.pth output.pth
# Train models on a slurm HPC with one GPU
> srun -p partition --gres=gpu:1 mim run onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain
> mim run onedl-mmpretrain -h
# Print help messages of sub-command run, print the help message of
# training script in onedl-mmpretrain
> mim run onedl-mmpretrain train -h

api

from mim import run

run(repo='onedl-mmpretrain', command='get_flops',
    other_args=('resnet101_b16x8_cifar10.py',))
run(repo='onedl-mmpretrain', command='publish_model',
    other_args=('input.pth', 'output.pth'))
run(repo='onedl-mmpretrain', command='train',
    other_args=('resnet101_b16x8_cifar10.py', '--work-dir', 'tmp'))
run(repo='onedl-mmpretrain', command='test',
    other_args=('resnet101_b16x8_cifar10.py', 'tmp/epoch_3.pth', '--metrics accuracy'))

9. gridsearch

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 onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain 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 onedl-mmpretrain
> mim gridsearch onedl-mmpretrain -h

api

from mim import gridsearch

gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=0,
           search_args='--optimizer.lr 1e-2 1e-3',
           other_args=('--work-dir', 'tmp'))
gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=1,
           search_args='--optimizer.lr 1e-2 1e-3',
           other_args=('--work-dir', 'tmp'))
gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=1,
           search_args='--optimizer.weight_decay 1e-3 1e-4',
           other_args=('--work-dir', 'tmp'))
gridsearch(repo='onedl-mmpretrain', 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='onedl-mmpretrain', 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='onedl-mmpretrain', 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.

Projects in VBTI-development

MMEngine: OpenMMLab foundational library for training deep learning models.
MMCV: OpenMMLab foundational library for computer vision.
MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
MMDetection: OpenMMLab detection toolbox and benchmark.
MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
MMDeploy: OpenMMLab model deployment framework.
MIM: MIM installs OpenMMLab packages.

Projects in OpenMMLab

MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
MMPose: OpenMMLab pose estimation toolbox and benchmark.
MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
MMRazor: OpenMMLab model compression toolbox and benchmark.
MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
MMTracking: OpenMMLab video perception toolbox and benchmark.
MMFlow: OpenMMLab optical flow toolbox and benchmark.
MMEditing: OpenMMLab image and video editing toolbox.
MMGeneration: OpenMMLab image and video generative models toolbox.
MMEval: A unified evaluation library for multiple machine learning libraries.
Playground: A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

Name		Name	Last commit message	Last commit date
Latest commit History 181 Commits
.devcontainer		.devcontainer
.github		.github
docs/en		docs/en
mim		mim
tests		tests
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.readthedocs.yml		.readthedocs.yml
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
setup.cfg		setup.cfg
uv.lock		uv.lock

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MIM: MIM Installs OpenMMLab Packages

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MIM: MIM Installs OpenMMLab Packages

Major Features

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Build custom projects with MIM

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Contributing

License

Projects in VBTI-development

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