o Meÿ6ã@s*ddlmZdd„ZedkreƒdSdS)é)ÚContextcCsPtƒ}| ¡rddlm}| ¡dSddlm}| |¡}| ¡| ¡dS)a€1 Paddle distribution training entry ``python -m paddle.distributed.launch``. Usage: .. code-block:: bash :name: code-block-bash1 python -m paddle.distributed.launch [-h] [--master MASTER] [--rank RANK] [--log_level LOG_LEVEL] [--nnodes NNODES] [--nproc_per_node NPROC_PER_NODE] [--log_dir LOG_DIR] [--run_mode RUN_MODE] [--job_id JOB_ID] [--devices DEVICES] [--host HOST] [--servers SERVERS] [--trainers TRAINERS] [--trainer_num TRAINER_NUM] [--server_num SERVER_NUM] [--gloo_port GLOO_PORT] [--with_gloo WITH_GLOO] [--max_restart MAX_RESTART] [--elastic_level ELASTIC_LEVEL] [--elastic_timeout ELASTIC_TIMEOUT] training_script ... Base Parameters: - ``--master``: The master/rendezvous server, support http:// and etcd://, default with http://. e.g., ``--master=127.0.0.1:8080``. Default ``--master=None``. - ``--rank``: The rank of the node, can be auto assigned by master. Default ``--rank=-1``. - ``--log_level``: The log level to set for logging.setLevel which can be CRITICAL/ERROR/WARNING/INFO/DEBUG/NOTSET, case insensitive. Default ``--log_level=INFO``. - ``--nnodes``: The number of nodes for a distributed job, it can be a range in elastic mode, e.g., ``--nnodes=2:3``. Default ``--nnodes=1``. - ``--nproc_per_node``: The number of processes to launch on a node. In gpu training, it should be less or equal to the gpus number of you system. e.g., ``--nproc_per_node=8`` - ``--log_dir``: The path for each process's log. e.g., ``--log_dir=output_dir``. Default ``--log_dir=log``. - ``--run_mode``: The run mode of job, can be:collective/ps/ps-heter. e.g., ``--run_mode=ps``. Default ``--run_mode=collective``. - ``--job_id``: The job unique id, it affects the log files' name. e.g., ``--job_id=job1``. Default ``--job_id=default``. - ``--devices``: The selected accelerate devices on nodes, can be gpu/xpu/npu/mlu etc.. e.g., ``--devices=0,1,2,3`` will launch four training processes each bound to one device. - ``training_script``: The full path to the single GPU training program/script to be launched in parallel, followed by all the arguments for the training script. e.g., ``training.py`` - ``training_script_args``: The args of training_script. e.g., ``--lr=0.1`` Collective Parameters: - ``--ips``: [DEPRECATED] Paddle cluster nodes ips, e.g., ``--ips=192.168.0.16,192.168.0.17``. Default ``--ips=127.0.0.1``. Parameter-Server Parameters: - ``--servers``: User defined servers ip:port, e.g., ``--servers="192.168.0.16:6170,192.168.0.17:6170"`` - ``--trainers``: User defined trainers ip:port, e.g., ``--trainers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172"`` - ``--workers``: [DEPRECATED] The same as trainers. - ``--trainer_num``: Number of trainers on each node, can be 0. - ``--worker_num``: [DEPRECATED] The same as trainer_num. - ``--server_num``: Number of servers on each node, can be 0. - ``--heter_workers``: User defined heter workers ip1:port1;ip2:port2, e.g., ``--heter_workers="192.168.0.16:6172;192.168.0.17:6172"`` - ``--heter_worker_num``: Number of heter_workers in each stage (It recommend to set when in the emulated distributed environment using single node) - ``--heter_devices``: Type of heter_device in each stage - ``--gloo_port``: Gloo http Port. Default ``--gloo_port=6767``. - ``--with_gloo``: Using gloo or not. Default ``--with_gloo=0``. Elastic Parameters: - ``--max_restart``: The maximum restart times for an elastic job. Default ``--max_restart=3``. - ``--elastic_level``: The elastic level: -1: disable, 0: failed exit, peers hold, 1: internal restart. Default ``--elastic_level=-1``. - ``--elastic_timeout``: Seconds to wait before elastic job begin to train. Default ``--elastic_timeout=30``. IPU Parameters: IPU distributed launch only requires and allowes three arguments ``--devices``, ``training_script`` and ``training_script_args``. The ``--devices`` is the number of IPU devices. e.g., ``--devices=4`` will launch the training program with four IPU devices. The ``training_script`` is only allowed to set as ``ipu``. The ``training_script_args`` includes arguments required by IPU distributed launch and illustrated as below. ``Examples 10`` has provided a example of paddle.distributed.launch with IPUs. - ``--hosts``: The hosts for IPU distributd training. Each host is able to include multiple processes. - ``--nproc_per_host``: The number of processes launched per host. Each process is able to include multiple replicas. - ``--ipus_per_replica``: The number of IPUs requested per replica. Each replica is able to include multiple IPUs. - ``--ipu_partition``: The partition name of IPU devices. - ``--vipu_server``: The ip of the IPU device manager. - ``training_script``: The full path to the IPU distributed training program/script to be launched in parallel. e.g., ``training.py``. - ``training_script_args``: The args of the IPU distributed training program/script. e.g., ``--lr=0.1``. Returns: - ``None`` Examples 0 (master, ip/port auto detection): .. code-block:: bash :name: code-block-example-bash0 # For training on multi node, run the following command in one of the nodes python -m paddle.distributed.launch --nnodes 2 train.py # Then the following info will be print # Copy the following command to other nodes to run. # -------------------------------------------------------------------------------- # python -m paddle.distributed.launch --master 10.0.0.1:38714 --nnodes 2 train.py # -------------------------------------------------------------------------------- # Follow the instruction above and paste the command in other nodes can launch a multi nodes training job. # There are two ways to launch a job with the same command for multi nodes training # 1) using the following command in every nodes, make sure the ip is one of the training node and the port is available on that node # python -m paddle.distributed.launch --master 10.0.0.1:38714 --nnodes 2 train.py # 2) using the following command in every nodes with a independent etcd service # python -m paddle.distributed.launch --master etcd://10.0.0.1:2379 --nnodes 2 train.py # This functionality works will for both collective and ps mode and even with other arguments. Examples 1 (collective, single node): .. code-block:: bash :name: code-block-example-bash1 # For training on single node using 4 gpus. python -m paddle.distributed.launch --devices=0,1,2,3 train.py --lr=0.01 Examples 2 (collective, multi node): .. code-block:: bash :name: code-block-example-bash2 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 # On 192.168.0.16: python -m paddle.distributed.launch --devices=0,1,2,3 --master=192.168.0.16:8090 train.py --lr=0.01 # On 192.168.0.17: python -m paddle.distributed.launch --devices=0,1,2,3 --master=192.168.0.16:8090 train.py --lr=0.01 Examples 3 (ps, cpu, single node): .. code-block:: bash :name: code-block-example-bash3 # To simulate distributed environment using single node, e.g., 2 servers and 4 workers. python -m paddle.distributed.launch --server_num=2 --worker_num=4 train.py --lr=0.01 Examples 4 (ps, cpu, multi node): .. code-block:: bash :name: code-block-example-bash4 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server and 2 workers. # On 192.168.0.16: python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 # On 192.168.0.17: python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 # Or with master, the following command run 2 server and 2 trainer on each node. python -m paddle.distributed.launch --master 192.168.0.16:9090 --server_num=2 --trainer_num=2 --nnodes 2 train.py Examples 5 (ps, gpu, single node): .. code-block:: bash :name: code-block-example-bash5 # To simulate distributed environment using single node, e.g., 2 servers and 4 workers, each worker use single gpu. export CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch --server_num=2 --worker_num=4 train.py --lr=0.01 Examples 6 (ps, gpu, multi node): .. code-block:: bash :name: code-block-example-bash6 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server and 2 workers. # On 192.168.0.16: export CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 # On 192.168.0.17: export CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01 Examples 7 (ps-heter, cpu + gpu, single node): .. code-block:: bash :name: code-block-example-bash7 # To simulate distributed environment using single node, e.g., 2 servers and 4 workers, two workers use gpu, two workers use cpu. export CUDA_VISIBLE_DEVICES=0,1 python -m paddle.distributed.launch --server_num=2 --worker_num=2 --heter_worker_num=2 train.py --lr=0.01 Examples 8 (ps-heter, cpu + gpu, multi node): .. code-block:: bash :name: code-block-example-bash8 # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server, 1 gpu worker, 1 cpu worker. # On 192.168.0.16: export CUDA_VISIBLE_DEVICES=0 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.17:6171" --heter_workers="192.168.0.16:6172,192.168.0.17:6172" train.py --lr=0.01 # On 192.168.0.17: export CUDA_VISIBLE_DEVICES=0 python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.17:6171" --heter_workers="192.168.0.16:6172,192.168.0.17:6172" train.py --lr=0.01 Examples 9 (elastic): .. code-block:: bash :name: code-block-example-bash9 # With the following command, the job will begin to run immediately if 4 nodes are ready, # or it will run after elastic_timeout if only 2 or 3 nodes ready python -m paddle.distributed.launch --master etcd://10.0.0.1:2379 --nnodes 2:4 train.py # once the number of nodes changes between 2:4 during training, the strategy holds Examples 10 (ipu): .. code-block:: bash :name: code-block-example-bash10 # With the following command, the job will begin to run the distributhed program with IPUs # Require `devices` as the number of IPUs # Require `training_script` to be set as `ipu` # Require `training_script_args` as the arguments of IPU distributed training instead of the arguments of the training program/script # Please Check the `IPU Parameters` for details python -m paddle.distributed.launch --devices 4 ipu --hosts=localhost --nproc_per_host=2 --ipus_per_replica=1 --ipu_partition=pod16 --vipu_server=127.0.0.1 train.py é)Úlaunchr)Ú controllersN) rZis_legacy_modeZpaddle.distributed.fleetrÚrÚinitÚrunÚfinalize)ÚctxrrÚc©r úND:\Projects\ConvertPro\env\Lib\site-packages\paddle/distributed/launch/main.pyrsy     rÚ__main__N)ÚcontextrrÚ__name__r r r r Ús  ÿ