tflite.yml

# Configuration file of running tensorflow backend

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

exp_path: $FEDSCALE_HOME/fedscale/cloud

aggregator_entry: aggregation/aggregator_tflite.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: android-tflite           # Generate logs under this folder: log_path/job_name/time_stamp
    - log_path: $FEDSCALE_HOME/benchmark # Path of log files
    - experiment_mode: mobile
    - num_participants: 1                # Number of participants per round, we use K=100 in our paper, large K will be much slower
    - model: linear                      # Need to define the model in tf_aggregator.py
    - learning_rate: 0.01
    - batch_size: 32
    - input_shape: 32 32 3
    - num_classes: 10
    - test_bsz: 16
    - engine: 'tensorflow'