Training and Test

• Training and Test

  • Training

    • Training with your PC

    • Training with multiple GPUs

    • Training with multiple machines

      • Multiple machines in the same network

      • Multiple machines managed with slurm

  • Test

    • Test with your PC

    • Test with multiple GPUs

    • Test with multiple machines

      • Multiple machines in the same network

      • Multiple machines managed with slurm

Training

Training 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, MMAction2 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 a path is specified, resume from it, while if not specified, 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.
--seed	Random seed.
--diff-rank-seed	Whether or not set different seeds for different ranks.
--deterministic	Whether to set deterministic options for CUDNN backend.
--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. Defaults to none.

Training with multiple GPUs

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

bash tools/dist_train.sh ${CONFIG} ${GPUS} [PY_ARGS]

ARGS	Description
CONFIG	The path to the config file.
GPUS	The number of GPUs to be used.
[PYARGS]	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 following command:

PORT=29666 bash tools/dist_train.sh ${CONFIG} ${GPUS} [PY_ARGS]

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

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

Training 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

The following extra environment variables need to be specified to train or test models with multiple machines:

ENV_VARS	Description
NNODES	The total number of machines. Defaults to 1.
NODE_RANK	The index of the local machine. Defaults to 0.
PORT	The communication port, it should be the same in all machines. Defaults to 29500.
MASTER_ADDR	The IP address of the master machine, it should be the same in all machines. Defaults to 127.0.0.1.

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

Multiple machines managed with slurm

If you run MMAction2 on a cluster managed with slurm, you can use the script slurm_train.sh.

[ENV_VARS] bash tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG} [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	The path to the config file.
[PYARGS]	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. Defaults to 8.
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.

Test

Test with your PC

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

Here is the full usage of the script:

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

Note

By default, MMAction2 prefers GPU to CPU. If you want to test 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/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [ARGS]

ARGS	Description
CONFIG_FILE	The path to the config file.
CHECKPOINT_FILE	The path to the checkpoint file (It can be a http link)
--work-dir WORK_DIR	The directory to save the file containing evaluation metrics. Defaults to a folder with the same name of the config file under ./work_dirs.
--dump DUMP	The path to dump all outputs of the model for offline evaluation.
--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.
--show-dir SHOW_DIR	The directory to save the result visualization images.
--show	Visualize the prediction result in a window.
--interval INTERVAL	The interval of samples to visualize. Defaults to 1.
--wait-time WAIT_TIME	The display time of every window (in seconds). Defaults to 2.
--launcher {none,pytorch,slurm,mpi}	Options for job launcher. Defaults to none.

Test with multiple GPUs

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

bash tools/dist_test.sh ${CONFIG} ${CHECKPOINT} ${GPUS} [PY_ARGS]

ARGS	Description
CONFIG	The path to the config file.
CHECKPOINT	The path to the checkpoint file (It can be a http link)
GPUS	The number of GPUs to be used.
[PYARGS]	The other optional arguments of tools/test.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 following command:

PORT=29666 bash tools/dist_test.sh ${CONFIG} ${CHECKPOINT} ${GPUS} [PY_ARGS]

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

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

Test with multiple machines

Multiple machines in the same network

If you launch a test 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_test.sh $CONFIG $CHECKPOINT $GPUS

On the second machine:

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

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

ENV_VARS	Description
NNODES	The total number of machines. Defaults to 1.
NODE_RANK	The index of the local machine. Defaults to 0.
PORT	The communication port, it should be the same in all machines. Defaults to 29500.
MASTER_ADDR	The IP address of the master machine, it should be the same in all machines. Defaults to 127.0.0.1.

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

Multiple machines managed with slurm

If you run MMAction2 on a cluster managed with slurm, you can use the script slurm_test.sh.

[ENV_VARS] bash tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG} ${CHECKPOINT} [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	The path to the config file.
CHECKPOINT	The path to the checkpoint file (It can be a http link)
[PYARGS]	The other optional arguments of tools/test.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. Defaults to 8.
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.