# Argument Outline It looks like there are a lot of arguments. However, most of them are for developers or alrealy set automatically in cluster submitting environment and users do not need to care about them. Here, we divide these arguments into serveral classes according to the scenario that they are used in. For example, the arguments in `common` can be used in all scenes. Some arguments can be only used in certain layers. Some are needed by multi machines training in cluster, etc. √
args local train cluster train local test cluster test
common job
use_gpu
local
config
config_args
num_passes
trainer_count
version
show_layer_stat
traindot_period
test_period
saving_period
show_parameter_stats_period
init_model_path
load_missing_parameter_strategy
saving_period_by_batches
use_old_updater
enable_grad_share
grad_share_block_num
log_error_clipping
log_clipping
save_only_one
start_pass
train/testsave_dir
testing during trainingtest_period
average_test_period
testmodel_list
test_wait
test_pass
predict_output_dir
distribute_test
Auc/PnpairValidationpredict_file
GPUgpu_id
parallel_nn
allow_only_one_model_on_one_gpu
cudnn_dir
cuda_dir
cudnn_conv_workspace_limit_in_mb
RNN beam_size
rnn_use_batch
prev_batch_state
diy_beam_search_prob_so
PServerstart_pserver
pservers
port
port_num
ports_num_for_sparse
nics
rdma_tcp
small_messages
loadsave_parameters_in_pserver
log_period_server
pserver_num_threads
sock_send_buf_size
sock_recv_buf_size
num_gradient_servers
parameter_block_size
parameter_block_size_for_sparse
Async SGDasync_count
async_lagged_ratio_min
async_lagged_ratio_default
Performance Tuninglog_barrier_abstract
log_barrier_lowest_nodes
log_barrier_show_log
check_sparse_distribution_batches
check_sparse_distribution_ratio
check_sparse_distribution_unbalance_degree
check_sparse_distribution_in_pserver
show_check_sparse_distribution_log
Data Providermemory_threshold_on_load_data
RandomNumberseed
thread_local_rand_use_global_seed
UnitTestcheckgrad_eps
Matrix/Vectorenable_parallel_vector