FedScale/benchmark/configs/taobao_dlrm/taobao_dlrm.yml at 6ddbe38ff24e35b468aa5c7ac10962090c17d37b · SymbioticLab/FedScale · GitHub

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# Configuration file of FAR training experiment

# ========== Cluster configuration ==========
# ip address of the parameter server (need 1 GPU process)
ps_ip: 127.0.0.1

# ip address of each worker:# of available gpus process on each gpu in this node
# Note that if we collocate ps and worker on same GPU, then we need to decrease this number of available processes on that GPU by 1
# E.g., master node has 4 available processes, then 1 for the ps, and worker should be set to: worker:3
worker_ips:
    - 127.0.0.1:[2]

exp_path: $FEDSCALE_HOME/fedscale/cloud

# Entry function of executor and aggregator under $exp_path
executor_entry: execution/executor.py

aggregator_entry: aggregation/aggregator.py

auth:
    ssh_user: ""
    ssh_private_key: ~/.ssh/id_rsa

# cmd to run before we can indeed run FAR (in order)
setup_commands:
    - source $HOME/anaconda3/bin/activate fedscale

# ========== Additional job configuration ==========
# Default parameters are specified in config_parser.py, wherein more description of the parameter can be found

job_conf:
    - job_name: taobao # change job name
    - log_path: $FEDSCALE_HOME/benchmark
    - wandb_token: 4221994eb764b3c6244c61a8c6bxxxxxx # add wandb api
    - task: recommendation # change task name
    - num_participants: 50
    - data_set: taobao # modify data_set
    - data_dir: $FEDSCALE_HOME/benchmark/dataset/taobao # modify data_set path
    # Delete data_map_file
    - device_conf_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_device_capacity
    - device_avail_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_behave_trace
    - model: dlrm # Modify the used model
    - eval_interval: 5
    - rounds: 1000
    - filter_less: 21
    - num_loaders: 2
    - local_steps: 5
    - learning_rate: 0.01
    - batch_size: 256
    - test_bsz: 256
    - use_cuda: False
    - save_checkpoint: False
    # Add new training parameters
    - n_rows: 200000
    - sparse_feature_number: 200000 200000 200000 200000 200000 200000 200000 200000 200000 200000 200000 200000
    - sparse_feature_dim: 16
    - dense_feature_dim: 10
    - bot_layer_sizes: 122 64 16
    - top_layer_sizes: 512 256 1
    - num_field: 7