State of the art decentralized optimization library
Project description
Linearly Convergent Decentralized Learning with Arbitrary Communication Compression
How do I run the Code?
A. Install our package:
pip3 install decopt
(A.1) Often get the latest update:
pip3 install decopt --upgrade
B. Get Data:
sh pull_data.sh breast_cancer
c. Run script with default parameters:
python3 driver.py
With different parameters:
python3 driver.py --d 'mnist' --n_cores 10 --algorithms 'ours'
Parameter Options:
parser.add_argument('--d', type=str, default='breast_cancer',
help='Pass data-set')
parser.add_argument('--r', type=str, default=os.path.join(curr_dir, './data/'),
help='Pass data root')
parser.add_argument('--stochastic', type=bool, default=False)
parser.add_argument('--algorithm', type=str, default='ours')
parser.add_argument('--n_cores', type=int, default=9)
parser.add_argument('--topology', type=str, default='ring')
parser.add_argument('--Q', type=int, default=2)
parser.add_argument('--consensus_lr', type=float, default=0.3)
parser.add_argument('--quantization_function', type=str, default='full')
parser.add_argument('--num_bits', type=int, default=2)
parser.add_argument('--fraction_coordinates', type=float, default=0.1)
parser.add_argument('--dropout_p', type=float, default=0.1)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--lr_type', type=str, default='constant')
parser.add_argument('--initial_lr', type=float, default=0.01)
parser.add_argument('--epoch_decay_lr', type=float, default=0.9)
parser.add_argument('--regularizer', type=float, default=0)
parser.add_argument('--estimate', type=str, default='final')
parser.add_argument('--n_proc', type=int, default=3, help='no of parallel processors for Multi-proc')
parser.add_argument('--n_repeat', type=int, default=3, help='no of times repeat exp with diff seed')
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