def test_train_for_distributed_scheduler(run_entry_point, verify_hosts,
host_lookup, popen,
distributed_training_env):
host_lookup.return_value = IP_ADDRESS
distributed_training_env.current_host = SCHEDULER
training.train(distributed_training_env)
verify_hosts.assert_called_with(MULTIPLE_HOST_LIST)
scheduler_env = BASE_ENV_VARS.copy()
scheduler_env.update({'DMLC_ROLE': 'scheduler'})
server_env = BASE_ENV_VARS.copy()
server_env.update({'DMLC_ROLE': 'server'})
calls = [
call(MXNET_COMMAND, shell=True, env=scheduler_env),
call(MXNET_COMMAND, shell=True, env=server_env)
]
popen.assert_has_calls(calls)
run_entry_point.assert_called_with(
uri=MODULE_DIR,
user_entry_point=MODULE_NAME,
args=distributed_training_env.to_cmd_args(),
env_vars=distributed_training_env.to_env_vars(),
runner_type=runner.ProcessRunnerType)
def test_train_for_single_machine(run_entry_point,
single_machine_training_env):
training.train(single_machine_training_env)
run_entry_point.assert_called_with(
uri=MODULE_DIR,
user_entry_point=MODULE_NAME,
args=single_machine_training_env.to_cmd_args(),
env_vars=single_machine_training_env.to_env_vars(),
runner_type=runner.ProcessRunnerType)
def test_train_for_single_machine(run_entry_point, download_and_install,
single_machine_training_env):
training.train(single_machine_training_env)
download_and_install.assert_called_with(MODULE_DIR)
run_entry_point.assert_called_with(
MODULE_DIR,
MODULE_NAME,
single_machine_training_env.to_cmd_args(),
single_machine_training_env.to_env_vars(),
runner=framework.runner.ProcessRunnerType)
def test_train_horovod(run_module, single_machine_training_env):
single_machine_training_env.additional_framework_parameters = {
training.LAUNCH_MPI_ENV_NAME: True,
}
training.train(single_machine_training_env)
run_module.assert_called_with(
uri=MODULE_DIR,
user_entry_point=MODULE_NAME,
args=single_machine_training_env.to_cmd_args(),
env_vars=single_machine_training_env.to_env_vars(),
runner_type=runner.MPIRunnerType)
def test_train_for_distributed_worker(run_module, verify_hosts, host_lookup,
popen, distributed_training_env):
host_lookup.return_value = IP_ADDRESS
distributed_training_env.current_host = 'host-2'
training.train(distributed_training_env)
verify_hosts.assert_called_with(MULTIPLE_HOST_LIST)
server_env = BASE_ENV_VARS.copy()
server_env.update({'DMLC_ROLE': 'server'})
popen.assert_called_once_with(MXNET_COMMAND, shell=True, env=server_env)
def test_train_for_distributed_scheduler(run_module, verify_hosts, host_lookup, popen,
distributed_training_env):
host_lookup.return_value = IP_ADDRESS
distributed_training_env.current_host = SCHEDULER
training.train(distributed_training_env)
verify_hosts.assert_called_with(MULTIPLE_HOST_LIST)
scheduler_env = BASE_ENV_VARS.copy()
scheduler_env.update({'DMLC_ROLE': 'scheduler'})
server_env = BASE_ENV_VARS.copy()
server_env.update({'DMLC_ROLE': 'server'})
calls = [call(MXNET_COMMAND, shell=True, env=scheduler_env),
call(MXNET_COMMAND, shell=True, env=server_env)]
popen.assert_has_calls(calls)
def test_train_for_distributed_worker(run_entry_point, download_and_install,
verify_hosts, host_lookup, popen,
distributed_training_env):
host_lookup.return_value = IP_ADDRESS
distributed_training_env.current_host = 'host-2'
training.train(distributed_training_env)
verify_hosts.assert_called_with(MULTIPLE_HOST_LIST)
server_env = BASE_ENV_VARS.copy()
server_env.update({'DMLC_ROLE': 'server'})
popen.assert_called_once_with(MXNET_COMMAND, shell=True, env=server_env)
download_and_install.assert_called_with(MODULE_DIR)
run_entry_point.assert_called_with(
MODULE_DIR,
MODULE_NAME,
distributed_training_env.to_cmd_args(),
distributed_training_env.to_env_vars(),
runner=framework.runner.ProcessRunnerType)
def test_train_for_single_machine(run_module, single_machine_training_env):
training.train(single_machine_training_env)
run_module.assert_called_with(MODULE_DIR,
single_machine_training_env.to_cmd_args(),
single_machine_training_env.to_env_vars(),
MODULE_NAME)
