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

Tutorial 3: Pretrain with existing models

• Tutorial 3: Pretrain with existing models
  • Start to Train
    • Train with a single GPU
    • Train with CPU
    • Train with multiple GPUs
    • Train with multiple machines
    • Launch multiple jobs on a single machine

This page provides basic usage about how to run algorithms and how to use some tools in MMSelfSup. For installation instructions and date preparation, please refer to install.md and prepare_data.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 using different number GPUs, the total batch size will change in proportion, you have to scale the learning rate following new_lr = old_lr * new_ngpus / old_ngpus.

Train with a single GPU

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

A simple example to start training:

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

Train with CPU

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

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

Optional arguments:

• --work-dir: Inidicate 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:

sh 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:

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

An example to start training with 8 GPUs:

# The default setting: GPUS_PER_NODE=8 GPUS=8
sh 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 following commands:

On the first machine:

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

On the second machine:

NNODES=2 NODE_RANK=1 PORT=${MASTER_PORT} MASTER_ADDR=${MASTER_ADDR} sh 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 refer to 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 communication conflict.

If you use dist_train.sh to launch training jobs:

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

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

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

Option 1:

In config1.py:

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

In config2.py:

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

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

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

CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 sh 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 configuration file.

CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 sh 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 sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py --work-dir tmp_work_dir_2 --cfg-options env_cfg.dist_cfg.port=29501