# 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](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](docs/en/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](https://github.com/open-mmlab/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](docs/en/installation.md) for installation.
## Command
<details>
<summary>1. install</summary>
- command
```bash
# 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
```python
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')
```
</details>
<details>
<summary>2. uninstall</summary>
- command
```bash
# uninstall mmcv
> mim uninstall onedl-mmcv
# uninstall onedl-mmpretrain
> mim uninstall onedl-mmpretrain
```
- api
```python
from mim import uninstall
# uninstall mmcv
uninstall('onedl-mmcv')
# uninstall onedl-mmpretrain
uninstall('onedl-mmpretrain')
```
</details>
<details>
<summary>3. list</summary>
- command
```bash
> mim list
> mim list --all
```
- api
```python
from mim import list_package
list_package()
list_package(True)
```
</details>
<details>
<summary>4. search</summary>
- command
```bash
> 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
```python
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'])
```
</details>
<details>
<summary>5. download</summary>
- command
```bash
> mim download onedl-mmpretrain --config resnet18_8xb16_cifar10
> mim download onedl-mmpretrain --config resnet18_8xb16_cifar10 --dest .
```
- api
```python
from mim import download
download('onedl-mmpretrain', ['resnet18_8xb16_cifar10'])
download('onedl-mmpretrain', ['resnet18_8xb16_cifar10'], dest_root='.')
```
</details>
<details>
<summary>6. train</summary>
- command
```bash
# 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
```python
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'))
```
</details>
<details>
<summary>7. test</summary>
- command
```bash
# 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
```python
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'))
```
</details>
<details>
<summary>8. run</summary>
- command
```bash
# 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
```python
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'))
```
</details>
<details>
<summary>9. gridsearch</summary>
- command
```bash
# 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
```python
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'))
```
</details>
## Contributing
We appreciate all contributions to improve mim. Please refer to [CONTRIBUTING.md](https://github.com/vbti-development/onedl-mmcv/blob/master/CONTRIBUTING.md) for the contributing guideline.
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in VBTI-development
- [MMEngine](https://github.com/vbti-development/onedl-mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/vbti-development/onedl-mmcv): OpenMMLab foundational library for computer vision.
- [MMPreTrain](https://github.com/vbti-development/onedl-mmpretrain): OpenMMLab pre-training toolbox and benchmark.
- [MMDetection](https://github.com/vbti-development/onedl-mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMRotate](https://github.com/vbti-development/onedl-mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMSegmentation](https://github.com/vbti-development/onedl-mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMDeploy](https://github.com/vbti-development/onedl-mmdeploy): OpenMMLab model deployment framework.
- [MIM](https://github.com/vbti-development/onedl-mim): MIM installs OpenMMLab packages.
## Projects in OpenMMLab
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
Raw data
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"description": "# MIM: MIM Installs OpenMMLab Packages\n\nMIM provides a unified interface for launching and installing OpenMMLab projects and their extensions, and managing the OpenMMLab model zoo.\n\n## Major Features\n\n- **Package Management**\n\n You can use MIM to manage OpenMMLab codebases, install or uninstall them conveniently.\n\n- **Model Management**\n\n You can use MIM to manage OpenMMLab model zoo, e.g., download checkpoints by name, search checkpoints that meet specific criteria.\n\n- **Unified Entrypoint for Scripts**\n\n 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.\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## Changelog\n\nv0.1.1 was released in 13/6/2021.\n\n## Customization\n\nYou can use `.mimrc` for customization. Now we support customize default values of each sub-command. Please refer to [customization.md](docs/en/customization.md) for details.\n\n## Build custom projects with MIM\n\nWe provide some examples of how to build custom projects based on OpenMMLAB codebases and MIM in [MIM-Example](https://github.com/open-mmlab/mim-example).\nWithout 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.\n\n## Installation\n\nPlease refer to [installation.md](docs/en/installation.md) for installation.\n\n## Command\n\n<details>\n<summary>1. install</summary>\n\n- command\n\n ```bash\n # install latest version of onedl-mmcv\n > mim install onedl-mmcv # wheel\n # install 1.5.0\n > mim install onedl-mmcv==2.3.0\n\n # install latest version of onedl-mmpretrain\n > mim install onedl-mmpretrain\n # install master branch\n > mim install git+https://github.com/vbti-development/onedl-mmpretrain.git\n # install local repo\n > git clone https://github.com/vbti-development/onedl-mmpretrain.git\n > cd mmclassification\n > mim install .\n\n # install extension based on OpenMMLab\n mim install git+https://github.com/xxx/onedl-mmpretrain-project.git\n ```\n\n- api\n\n ```python\n from mim import install\n\n # install mmcv\n install('onedl-mmcv')\n\n # install onedl-mmpretrain will automatically install mmcv if it is not installed\n install('onedl-mmpretrain')\n\n # install extension based on OpenMMLab\n install('git+https://github.com/xxx/onedl-mmpretrain-project.git')\n ```\n\n</details>\n\n<details>\n<summary>2. uninstall</summary>\n\n- command\n\n ```bash\n # uninstall mmcv\n > mim uninstall onedl-mmcv\n\n # uninstall onedl-mmpretrain\n > mim uninstall onedl-mmpretrain\n ```\n\n- api\n\n ```python\n from mim import uninstall\n\n # uninstall mmcv\n uninstall('onedl-mmcv')\n\n # uninstall onedl-mmpretrain\n uninstall('onedl-mmpretrain')\n ```\n\n</details>\n\n<details>\n<summary>3. list</summary>\n\n- command\n\n ```bash\n > mim list\n > mim list --all\n ```\n\n- api\n\n ```python\n from mim import list_package\n\n list_package()\n list_package(True)\n ```\n\n</details>\n\n<details>\n<summary>4. search</summary>\n\n- command\n\n ```bash\n > mim search onedl-mmpretrain\n > mim search onedl-mmpretrain==0.23.0 --remote\n > mim search onedl-mmpretrain --config resnet18_8xb16_cifar10\n > mim search onedl-mmpretrain --model resnet\n > mim search onedl-mmpretrain --dataset cifar-10\n > mim search onedl-mmpretrain --valid-field\n > mim search onedl-mmpretrain --condition 'batch_size>45,epochs>100'\n > mim search onedl-mmpretrain --condition 'batch_size>45 epochs>100'\n > mim search onedl-mmpretrain --condition '128<batch_size<=256'\n > mim search onedl-mmpretrain --sort batch_size epochs\n > mim search onedl-mmpretrain --field epochs batch_size weight\n > mim search onedl-mmpretrain --exclude-field weight paper\n ```\n\n- api\n\n ```python\n from mim import get_model_info\n\n get_model_info('onedl-mmpretrain')\n get_model_info('onedl-mmpretrain==0.23.0', local=False)\n get_model_info('onedl-mmpretrain', models=['resnet'])\n get_model_info('onedl-mmpretrain', training_datasets=['cifar-10'])\n get_model_info('onedl-mmpretrain', filter_conditions='batch_size>45,epochs>100')\n get_model_info('onedl-mmpretrain', filter_conditions='batch_size>45 epochs>100')\n get_model_info('onedl-mmpretrain', filter_conditions='128<batch_size<=256')\n get_model_info('onedl-mmpretrain', sorted_fields=['batch_size', 'epochs'])\n get_model_info('onedl-mmpretrain', shown_fields=['epochs', 'batch_size', 'weight'])\n ```\n\n</details>\n\n<details>\n<summary>5. download</summary>\n\n- command\n\n ```bash\n > mim download onedl-mmpretrain --config resnet18_8xb16_cifar10\n > mim download onedl-mmpretrain --config resnet18_8xb16_cifar10 --dest .\n ```\n\n- api\n\n ```python\n from mim import download\n\n download('onedl-mmpretrain', ['resnet18_8xb16_cifar10'])\n download('onedl-mmpretrain', ['resnet18_8xb16_cifar10'], dest_root='.')\n ```\n\n</details>\n\n<details>\n<summary>6. train</summary>\n\n- command\n\n ```bash\n # Train models on a single server with CPU by setting `gpus` to 0 and\n # 'launcher' to 'none' (if applicable). The training script of the\n # corresponding codebase will fail if it doesn't support CPU training.\n > mim train onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 0\n # Train models on a single server with one GPU\n > mim train onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1\n # Train models on a single server with 4 GPUs and pytorch distributed\n > mim train onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 4 \\\n --launcher pytorch\n # Train models on a slurm HPC with one 8-GPU node\n > mim train onedl-mmpretrain resnet101_b16x8_cifar10.py --launcher slurm --gpus 8 \\\n --gpus-per-node 8 --partition partition_name --work-dir tmp\n # Print help messages of sub-command train\n > mim train -h\n # Print help messages of sub-command train and the training script of onedl-mmpretrain\n > mim train onedl-mmpretrain -h\n ```\n\n- api\n\n ```python\n from mim import train\n\n train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=0,\n other_args=('--work-dir', 'tmp'))\n train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=1,\n other_args=('--work-dir', 'tmp'))\n train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=4,\n launcher='pytorch', other_args=('--work-dir', 'tmp'))\n train(repo='onedl-mmpretrain', config='resnet18_8xb16_cifar10.py', gpus=8,\n launcher='slurm', gpus_per_node=8, partition='partition_name',\n other_args=('--work-dir', 'tmp'))\n ```\n\n</details>\n\n<details>\n<summary>7. test</summary>\n\n- command\n\n ```bash\n # Test models on a single server with 1 GPU, report accuracy\n > mim test onedl-mmpretrain resnet101_b16x8_cifar10.py --checkpoint \\\n tmp/epoch_3.pth --gpus 1 --metrics accuracy\n # Test models on a single server with 1 GPU, save predictions\n > mim test onedl-mmpretrain resnet101_b16x8_cifar10.py --checkpoint \\\n tmp/epoch_3.pth --gpus 1 --out tmp.pkl\n # Test models on a single server with 4 GPUs, pytorch distributed,\n # report accuracy\n > mim test onedl-mmpretrain resnet101_b16x8_cifar10.py --checkpoint \\\n tmp/epoch_3.pth --gpus 4 --launcher pytorch --metrics accuracy\n # Test models on a slurm HPC with one 8-GPU node, report accuracy\n > mim test onedl-mmpretrain resnet101_b16x8_cifar10.py --checkpoint \\\n tmp/epoch_3.pth --gpus 8 --metrics accuracy --partition \\\n partition_name --gpus-per-node 8 --launcher slurm\n # Print help messages of sub-command test\n > mim test -h\n # Print help messages of sub-command test and the testing script of onedl-mmpretrain\n > mim test onedl-mmpretrain -h\n ```\n\n- api\n\n ```python\n from mim import test\n test(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py',\n checkpoint='tmp/epoch_3.pth', gpus=1, other_args=('--metrics', 'accuracy'))\n test(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py',\n checkpoint='tmp/epoch_3.pth', gpus=1, other_args=('--out', 'tmp.pkl'))\n test(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py',\n checkpoint='tmp/epoch_3.pth', gpus=4, launcher='pytorch',\n other_args=('--metrics', 'accuracy'))\n test(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py',\n checkpoint='tmp/epoch_3.pth', gpus=8, partition='partition_name',\n launcher='slurm', gpus_per_node=8, other_args=('--metrics', 'accuracy'))\n ```\n\n</details>\n\n<details>\n<summary>8. run</summary>\n\n- command\n\n ```bash\n # Get the Flops of a model\n > mim run onedl-mmpretrain get_flops resnet101_b16x8_cifar10.py\n # Publish a model\n > mim run onedl-mmpretrain publish_model input.pth output.pth\n # Train models on a slurm HPC with one GPU\n > srun -p partition --gres=gpu:1 mim run onedl-mmpretrain train \\\n resnet101_b16x8_cifar10.py --work-dir tmp\n # Test models on a slurm HPC with one GPU, report accuracy\n > srun -p partition --gres=gpu:1 mim run onedl-mmpretrain test \\\n resnet101_b16x8_cifar10.py tmp/epoch_3.pth --metrics accuracy\n # Print help messages of sub-command run\n > mim run -h\n # Print help messages of sub-command run, list all available scripts in\n # codebase onedl-mmpretrain\n > mim run onedl-mmpretrain -h\n # Print help messages of sub-command run, print the help message of\n # training script in onedl-mmpretrain\n > mim run onedl-mmpretrain train -h\n ```\n\n- api\n\n ```python\n from mim import run\n\n run(repo='onedl-mmpretrain', command='get_flops',\n other_args=('resnet101_b16x8_cifar10.py',))\n run(repo='onedl-mmpretrain', command='publish_model',\n other_args=('input.pth', 'output.pth'))\n run(repo='onedl-mmpretrain', command='train',\n other_args=('resnet101_b16x8_cifar10.py', '--work-dir', 'tmp'))\n run(repo='onedl-mmpretrain', command='test',\n other_args=('resnet101_b16x8_cifar10.py', 'tmp/epoch_3.pth', '--metrics accuracy'))\n ```\n\n</details>\n\n<details>\n<summary>9. gridsearch</summary>\n\n- command\n\n ```bash\n # Parameter search on a single server with CPU by setting `gpus` to 0 and\n # 'launcher' to 'none' (if applicable). The training script of the\n # corresponding codebase will fail if it doesn't support CPU training.\n > mim gridsearch onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 0 \\\n --search-args '--optimizer.lr 1e-2 1e-3'\n # Parameter search with on a single server with one GPU, search learning\n # rate\n > mim gridsearch onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \\\n --search-args '--optimizer.lr 1e-2 1e-3'\n # Parameter search with on a single server with one GPU, search\n # weight_decay\n > mim gridsearch onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \\\n --search-args '--optimizer.weight_decay 1e-3 1e-4'\n # Parameter search with on a single server with one GPU, search learning\n # rate and weight_decay\n > mim gridsearch onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 1 \\\n --search-args '--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay 1e-3 \\\n 1e-4'\n # Parameter search on a slurm HPC with one 8-GPU node, search learning\n # rate and weight_decay\n > mim gridsearch onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 8 \\\n --partition partition_name --gpus-per-node 8 --launcher slurm \\\n --search-args '--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay 1e-3 \\\n 1e-4'\n # Parameter search on a slurm HPC with one 8-GPU node, search learning\n # rate and weight_decay, max parallel jobs is 2\n > mim gridsearch onedl-mmpretrain resnet101_b16x8_cifar10.py --work-dir tmp --gpus 8 \\\n --partition partition_name --gpus-per-node 8 --launcher slurm \\\n --max-jobs 2 --search-args '--optimizer.lr 1e-2 1e-3 \\\n --optimizer.weight_decay 1e-3 1e-4'\n # Print the help message of sub-command search\n > mim gridsearch -h\n # Print the help message of sub-command search and the help message of the\n # training script of codebase onedl-mmpretrain\n > mim gridsearch onedl-mmpretrain -h\n ```\n\n- api\n\n ```python\n from mim import gridsearch\n\n gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=0,\n search_args='--optimizer.lr 1e-2 1e-3',\n other_args=('--work-dir', 'tmp'))\n gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=1,\n search_args='--optimizer.lr 1e-2 1e-3',\n other_args=('--work-dir', 'tmp'))\n gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=1,\n search_args='--optimizer.weight_decay 1e-3 1e-4',\n other_args=('--work-dir', 'tmp'))\n gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=1,\n search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'\n '1e-3 1e-4',\n other_args=('--work-dir', 'tmp'))\n gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=8,\n partition='partition_name', gpus_per_node=8, launcher='slurm',\n search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'\n ' 1e-3 1e-4',\n other_args=('--work-dir', 'tmp'))\n gridsearch(repo='onedl-mmpretrain', config='resnet101_b16x8_cifar10.py', gpus=8,\n partition='partition_name', gpus_per_node=8, launcher='slurm',\n max_workers=2,\n search_args='--optimizer.lr 1e-2 1e-3 --optimizer.weight_decay'\n ' 1e-3 1e-4',\n other_args=('--work-dir', 'tmp'))\n ```\n\n</details>\n\n## Contributing\n\nWe appreciate all contributions to improve mim. Please refer to [CONTRIBUTING.md](https://github.com/vbti-development/onedl-mmcv/blob/master/CONTRIBUTING.md) for the contributing guideline.\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## Projects in VBTI-development\n\n- [MMEngine](https://github.com/vbti-development/onedl-mmengine): OpenMMLab foundational library for training deep learning models.\n- [MMCV](https://github.com/vbti-development/onedl-mmcv): OpenMMLab foundational library for computer vision.\n- [MMPreTrain](https://github.com/vbti-development/onedl-mmpretrain): OpenMMLab pre-training toolbox and benchmark.\n- [MMDetection](https://github.com/vbti-development/onedl-mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMRotate](https://github.com/vbti-development/onedl-mmrotate): OpenMMLab rotated object detection toolbox and benchmark.\n- [MMSegmentation](https://github.com/vbti-development/onedl-mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.\n- [MMDeploy](https://github.com/vbti-development/onedl-mmdeploy): OpenMMLab model deployment framework.\n- [MIM](https://github.com/vbti-development/onedl-mim): MIM installs OpenMMLab packages.\n\n## Projects in OpenMMLab\n\n- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.\n- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.\n- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.\n- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.\n- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.\n",
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