Train¶

In this tutorial, we will introduce how to use the scripts provided in MMPretrain to start a training task. If you need, we also have some practice examples about how to pretrain with custom dataset and how to finetune with custom dataset.

Train with your PC¶

You can use tools/train.py to train a model on a single machine with a CPU and optionally a GPU.

Here is the full usage of the script:

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

Note

By default, MMPretrain prefers GPU to CPU. If you want to train a model on CPU, please empty CUDA_VISIBLE_DEVICES or set it to -1 to make GPU invisible to the program.

CUDA_VISIBLE_DEVICES=-1 python tools/train.py ${CONFIG_FILE} [ARGS]

ARGS	Description
`CONFIG_FILE`	The path to the config file.
`--work-dir WORK_DIR`	The target folder to save logs and checkpoints. Defaults to a folder with the same name of the config file under `./work_dirs`.
`--resume [RESUME]`	Resume training. If specify a path, resume from it, while if not specify, try to auto resume from the latest checkpoint.
`--amp`	Enable automatic-mixed-precision training.
`--no-validate`	Not suggested. Disable checkpoint evaluation during training.
`--auto-scale-lr`	Auto scale the learning rate according to the actual batch size and the original batch size.
`--no-pin-memory`	Whether to disable the `pin_memory` option in dataloaders.
`--no-persistent-workers`	Whether to disable the `persistent_workers` option in dataloaders.
`--cfg-options CFG_OPTIONS`	Override some settings in the used config, the key-value pair in xxx=yyy format will be merged into the config file. If the value to be overwritten is a list, it should be of the form of either `key="[a,b]"` or `key=a,b`. The argument also allows nested list/tuple values, e.g. `key="[(a,b),(c,d)]"`. Note that the quotation marks are necessary and that no white space is allowed.
`--launcher {none,pytorch,slurm,mpi}`	Options for job launcher.

Train with multiple GPUs¶

We provide a shell script to start a multi-GPUs task with torch.distributed.launch.

bash ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [PY_ARGS]

ARGS	Description
`CONFIG_FILE`	The path to the config file.
`GPU_NUM`	The number of GPUs to be used.
`[PY_ARGS]`	The other optional arguments of `tools/train.py`, see here.

You can also specify extra arguments of the launcher by environment variables. For example, change the communication port of the launcher to 29666 by the below command:

PORT=29666 bash ./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [PY_ARGS]

If you want to startup multiple training jobs and use different GPUs, you can launch them by specifying different ports and visible devices.

CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash ./tools/dist_train.sh ${CONFIG_FILE1} 4 [PY_ARGS]
CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 bash ./tools/dist_train.sh ${CONFIG_FILE2} 4 [PY_ARGS]

Train with multiple machines¶

Multiple machines in the same network¶

If you launch a training job with multiple machines connected with ethernet, you can run the following commands:

On the first machine:

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

On the second machine:

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

Comparing with multi-GPUs in a single machine, you need to specify some extra environment variables:

ENV_VARS	Description
`NNODES`	The total number of machines.
`NODE_RANK`	The index of the local machine.
`PORT`	The communication port, it should be the same in all machines.
`MASTER_ADDR`	The IP address of the master machine, it should be the same in all machines.

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

Multiple machines managed with slurm¶

If you run MMPretrain on a cluster managed with slurm, you can use the script tools/slurm_train.sh.

[ENV_VARS] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [PY_ARGS]

Here are the arguments description of the script.

ARGS	Description
`PARTITION`	The partition to use in your cluster.
`JOB_NAME`	The name of your job, you can name it as you like.
`CONFIG_FILE`	The path to the config file.
`WORK_DIR`	The target folder to save logs and checkpoints.
`[PY_ARGS]`	The other optional arguments of `tools/train.py`, see here.

Here are the environment variables can be used to configure the slurm job.

ENV_VARS	Description
`GPUS`	The number of GPUs to be used. Defaults to 8.
`GPUS_PER_NODE`	The number of GPUs to be allocated per node..
`CPUS_PER_TASK`	The number of CPUs to be allocated per task (Usually one GPU corresponds to one task). Defaults to 5.
`SRUN_ARGS`	The other arguments of `srun`. Available options can be found here.