Skip to content

Update arguments parser to support all arugments given to the PL trainer in dice_trainer.py #339

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
LckyLke wants to merge 1 commit into
base: develop
Choose a base branch
from
Draft

Update arguments parser to support all arugments given to the PL trainer in dice_trainer.py #339

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 13 additions & 0 deletions dicee/scripts/run.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,19 @@ def get_default_arguments(description=None):
parser.add_argument("--trainer", type=str, default='PL',
choices=['torchCPUTrainer', 'PL', 'torchDDP', "TP"],
help='PL (pytorch lightning trainer), torchDDP (custom ddp), torchCPUTrainer (custom cpu only), TP (Model Paralelisim)')
# PyTorch Lightning specific arguments
parser.add_argument("--accelerator", type=str, default="auto",
help='PyTorch Lightning accelerator (e.g., "gpu", "cpu", "auto")')
parser.add_argument("--strategy", type=str, default="auto",
help='PyTorch Lightning strategy (e.g., "ddp", "deepspeed_stage_3", "auto")')
parser.add_argument("--num_nodes", type=int, default=1,
help='Number of nodes for distributed training')
parser.add_argument("--precision", type=str, default=None,
help='PyTorch Lightning precision (e.g., "16", "32", "bf16")')
parser.add_argument("--max_step", type=int, default=-1,
help='Maximum number of training steps (-1 for no limit)')
parser.add_argument("--min_steps", type=int, default=None,
help='Minimum number of training steps')
parser.add_argument('--scoring_technique', default="NegSample",
help="Training technique for knowledge graph embedding model",
choices=["AllvsAll", "KvsAll", "1vsAll", "NegSample", "1vsSample", "KvsSample"])
Expand Down
Loading