mmselfsup/docs/en/user_guides/3_pretrain.md

# Tutorial 3: Pretrain with Existing Models

- [Tutorial 3: Pretrain with Existing Models](#tutorial-3-pretrain-with-existing-models)
  - [Start to Train](#start-to-train)
    - [Train with a single GPU](#train-with-a-single-gpu)
    - [Train with CPU](#train-with-cpu)
    - [Train with multiple GPUs](#train-with-multiple-gpus)
    - [Train with multiple machines](#train-with-multiple-machines)
    - [Launch multiple jobs on a single machine](#launch-multiple-jobs-on-a-single-machine)

This page provides the basic usage about how to run algorithms and how to use some tools in MMSelfSup. For installation instructions and data preparation, please refer to [get_started.md](../get_started.md) and [dataset_prepare.md](2_dataset_prepare.md).

## Start to Train

**Note**: The default learning rate in config files is for specific number of GPUs, which is indicated in the config names. If you use different number of GPUs, the total batch size will be changed in proportion. In this case, you have to scale the learning rate following `new_lr = old_lr * new_batchsize / old_batchsize`.

### Train with a single GPU

```shell
python tools/train.py ${CONFIG_FILE} [optional arguments]
```

A simple example to start training:

```shell
python tools/train.py configs/selfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k.py
```

### Train with CPU

```shell
export CUDA_VISIBLE_DEVICES=-1
python tools/train.py ${CONFIG_FILE} [optional arguments]
```

**Note**: We do not recommend users to use CPU for training because it is too slow. We support this feature to allow users to debug on machines without GPU for convenience.

### Train with multiple GPUs

```shell
bash tools/dist_train.sh ${CONFIG_FILE} ${GPUS} [optional arguments]
```

Optional arguments:

- `--work-dir`: Indicate your custom work directory to save checkpoints and logs.
- `--resume`: Automatically find the latest checkpoint in your work directory. Or set `--resume ${CHECKPOINT_PATH}` to load the specific checkpoint file.
- `--amp`: Enable automatic-mixed-precision training.
- `--cfg-options`: Setting `--cfg-options` will modify the original configs. For example, setting `--cfg-options randomness.seed=0` will set seed for random number.

An example to start training with 8 GPUs:

```shell
bash tools/dist_train.sh configs/selfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k.py 8
```

Alternatively, if you run MMSelfSup on a cluster managed with [slurm](https://slurm.schedmd.com/):

```shell
GPUS_PER_NODE=${GPUS_PER_NODE} GPUS=${GPUS} SRUN_ARGS=${SRUN_ARGS} bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} [optional arguments]
```

An example to start training with 8 GPUs:

```shell
# The default setting: GPUS_PER_NODE=8 GPUS=8
bash tools/slurm_train.sh Dummy Test_job configs/selfsup/mae/mae_vit-base-p16_8xb512-coslr-400e_in1k.py
```

### Train with multiple machines

If you launch with multiple machines simply connected with ethernet, you can simply run the following commands:

On the first machine:

```shell
NNODES=2 NODE_RANK=0 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} bash tools/dist_train.sh ${CONFIG} ${GPUS}
```

On the second machine:

```shell
NNODES=2 NODE_RANK=1 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} bash tools/dist_train.sh ${CONFIG} ${GPUS}
```

Usually it is slow if you do not have high speed networking like InfiniBand.

If you launch with slurm, the command is the same as that on single machine described above, but you need to refer to [slurm_train.sh](https://github.com/open-mmlab/mmselfsup/blob/dev-1.x/tools/slurm_train.sh) to set appropriate parameters and environment variables.

### Launch multiple jobs on a single machine

If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, you need to specify different ports (29500 by default) for each job to avoid the communication conflict.

If you use `dist_train.sh` to launch training jobs:

```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash tools/dist_train.sh ${CONFIG_FILE} 4 --work-dir tmp_work_dir_1

CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 bash tools/dist_train.sh ${CONFIG_FILE} 4 --work-dir tmp_work_dir_2
```

If you launch training jobs with slurm, you have two options to set different communication ports:

Option 1:

In `config1.py`:

```python
env_cfg = dict(dist_cfg=dict(backend='nccl', port=29500))
```

In `config2.py`:

```python
env_cfg = dict(dist_cfg=dict(backend='nccl', port=29501))
```

Then you can launch two jobs with config1.py and config2.py.

```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py [optional arguments]

CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py [optional arguments]
```

Option 2:

You can set different communication ports without the need to modify the configuration file, but have to set the `--cfg-options` to overwrite the default port in the configuration file.

```shell
CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py --work-dir tmp_work_dir_1 --cfg-options env_cfg.dist_cfg.port=29500

CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py --work-dir tmp_work_dir_2 --cfg-options env_cfg.dist_cfg.port=29501
```