File size: 4,219 Bytes
d02c2a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | import os
import sys
from dotenv import load_dotenv
# Load the .env file if it exists
load_dotenv()
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = os.getenv("HF_HUB_ENABLE_HF_TRANSFER", "1")
import huggingface_hub.constants
huggingface_hub.constants.MAX_HTTP_DOWNLOAD_SIZE = 200 * 1000 * 1000 * 1000 # 200GB
os.environ["NO_ALBUMENTATIONS_UPDATE"] = "1"
seed = None
if "SEED" in os.environ:
try:
seed = int(os.environ["SEED"])
except ValueError:
print(f"Invalid SEED value: {os.environ['SEED']}. SEED must be an integer.")
sys.path.insert(0, os.getcwd())
# must come before ANY torch or fastai imports
# import toolkit.cuda_malloc
# turn off diffusers telemetry until I can figure out how to make it opt-in
os.environ['DISABLE_TELEMETRY'] = 'YES'
# set torch to trace mode
import torch
# check if we have DEBUG_TOOLKIT in env
if os.environ.get("DEBUG_TOOLKIT", "0") == "1":
torch.autograd.set_detect_anomaly(True)
if seed is not None:
import random
import numpy as np
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
import argparse
from toolkit.job import get_job
from toolkit.accelerator import get_accelerator
from toolkit.print import print_acc, setup_log_to_file
accelerator = get_accelerator()
def print_end_message(jobs_completed, jobs_failed):
if not accelerator.is_main_process:
return
failure_string = f"{jobs_failed} failure{'' if jobs_failed == 1 else 's'}" if jobs_failed > 0 else ""
completed_string = f"{jobs_completed} completed job{'' if jobs_completed == 1 else 's'}"
print_acc("")
print_acc("========================================")
print_acc("Result:")
if len(completed_string) > 0:
print_acc(f" - {completed_string}")
if len(failure_string) > 0:
print_acc(f" - {failure_string}")
print_acc("========================================")
def main():
parser = argparse.ArgumentParser()
# require at lease one config file
parser.add_argument(
'config_file_list',
nargs='+',
type=str,
help='Name of config file (eg: person_v1 for config/person_v1.json/yaml), or full path if it is not in config folder, you can pass multiple config files and run them all sequentially'
)
# flag to continue if failed job
parser.add_argument(
'-r', '--recover',
action='store_true',
help='Continue running additional jobs even if a job fails'
)
# flag to continue if failed job
parser.add_argument(
'-n', '--name',
type=str,
default=None,
help='Name to replace [name] tag in config file, useful for shared config file'
)
parser.add_argument(
'-l', '--log',
type=str,
default=None,
help='Log file to write output to'
)
args = parser.parse_args()
if args.log is not None:
setup_log_to_file(args.log)
config_file_list = args.config_file_list
if len(config_file_list) == 0:
raise Exception("You must provide at least one config file")
jobs_completed = 0
jobs_failed = 0
if accelerator.is_main_process:
print_acc(f"Running {len(config_file_list)} job{'' if len(config_file_list) == 1 else 's'}")
for config_file in config_file_list:
try:
job = get_job(config_file, args.name)
job.run()
job.cleanup()
jobs_completed += 1
except Exception as e:
print_acc(f"Error running job: {e}")
jobs_failed += 1
try:
job.process[0].on_error(e)
except Exception as e2:
print_acc(f"Error running on_error: {e2}")
if not args.recover:
print_end_message(jobs_completed, jobs_failed)
raise e
except KeyboardInterrupt as e:
try:
job.process[0].on_error(e)
except Exception as e2:
print_acc(f"Error running on_error: {e2}")
if not args.recover:
print_end_message(jobs_completed, jobs_failed)
raise e
if __name__ == '__main__':
main()
|