training added, seems to work, need to test it more

src/train.py

@@ -0,0 +1,41 @@
+import torch
+import argparse
+
+import os
+import sys
+
+sys.path.insert(0, './dlas/codes/')
+sys.path.insert(0, './dlas/')
+
+from codes import train as tr
+from utils import util, options as option
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_vit_latent.yml')
+parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
+args = parser.parse_args()
+opt = option.parse(args.opt, is_train=True)
+if args.launcher != 'none':
+    # export CUDA_VISIBLE_DEVICES for running in distributed mode.
+    if 'gpu_ids' in opt.keys():
+        gpu_list = ','.join(str(x) for x in opt['gpu_ids'])
+        os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list
+        print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
+trainer = tr.Trainer()
+
+#### distributed training settings
+if args.launcher == 'none':  # disabled distributed training
+    opt['dist'] = False
+    trainer.rank = -1
+    if len(opt['gpu_ids']) == 1:
+        torch.cuda.set_device(opt['gpu_ids'][0])
+    print('Disabled distributed training.')
+else:
+    opt['dist'] = True
+    init_dist('nccl')
+    trainer.world_size = torch.distributed.get_world_size()
+    trainer.rank = torch.distributed.get_rank()
+    torch.cuda.set_device(torch.distributed.get_rank())
+
+trainer.init(args.opt, opt, args.launcher)
+trainer.do_training()

src/webui.py

@@ -413,6 +413,41 @@ def setup_gradio():
 							inputs=training_settings,
 							outputs=None
 						)
+			with gr.Tab("Train"):
+				with gr.Row():
+					with gr.Column():
+						def get_training_configs():
+							configs = []
+							for i, file in enumerate(sorted(os.listdir(f"./training/"))):
+								if file[-5:] != ".yaml" or file[0] == ".":
+									continue
+								configs.append(f"./training/{file}")
+
+							return configs
+						def update_training_configs():
+							return gr.update(choices=get_training_configs())
+
+						training_configs = gr.Dropdown(label="Training Configuration", choices=get_training_configs())
+						refresh_configs = gr.Button(value="Refresh Configurations")
+						train = gr.Button(value="Train")
+
+					def run_training_proxy( config ):
+						global tts
+						del tts
+
+						import subprocess
+						subprocess.run(["python", "./src/train.py", "-opt", config], env=os.environ.copy(), shell=True)
+						"""
+						from train import train
+						train(config)
+						"""
+
+					refresh_configs.click(update_training_configs,inputs=None,outputs=training_configs)
+					train.click(run_training_proxy,
+						inputs=training_configs,
+						outputs=None
+					)
+
 		with gr.Tab("Settings"):
 			with gr.Row():
 				exec_inputs = []