Train with different devices

Note: The default learning rate in config files is for 8 GPUs. 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. We recommend to use tools/dist_train.sh even with 1 gpu, since some methods do not support non-distributed training.

Training with CPU

export CUDA_VISIBLE_DEVICES=-1
python tools/train.py ${CONFIG_FILE}

Note: We do not recommend users to use CPU for training because it is too slow and some algorithms are using SyncBN which is based on distributed training. We support this feature to allow users to debug on machines without GPU for convenience.

Train with single/multiple GPUs

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

Note: During training, checkpoints and logs are saved in the same folder structure as the config file under work_dirs/. Custom work directory is not recommended since evaluation scripts infer work directories from the config file name. If you want to save your weights somewhere else, please use symlink, for example:

ln -s ${YOUR_WORK_DIRS} ${MMRAZOR}/work_dirs

Alternatively, if you run MMRazor on a cluster managed with slurm:

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

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:

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/xxx/dist_train.sh ${CONFIG_FILE} 4 --work_dir tmp_work_dir_1
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 sh tools/xxx/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:

dist_params = dict(backend='nccl', port=29500)

In config2.py:

dist_params = 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 tmp_work_dir_1
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py tmp_work_dir_2

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 tmp_work_dir_1 --cfg-options dist_params.port=29500
CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 sh tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py tmp_work_dir_2 --cfg-options dist_params.port=29501