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'''
ostris/ai-toolkit on https://modal.com
Run training with the following command:
modal run run_modal.py --config-file-list-str=/root/ai-toolkit/config/whatever_you_want.yml
'''
import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
import sys
import modal
from dotenv import load_dotenv
# Load the .env file if it exists
load_dotenv()
sys.path.insert(0, "/root/ai-toolkit")
# 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'
# define the volume for storing model outputs, using "creating volumes lazily": https://modal.com/docs/guide/volumes
# you will find your model, samples and optimizer stored in: https://modal.com/storage/your-username/main/flux-lora-models
model_volume = modal.Volume.from_name("flux-lora-models", create_if_missing=True)
# modal_output, due to "cannot mount volume on non-empty path" requirement
MOUNT_DIR = "/root/ai-toolkit/modal_output" # modal_output, due to "cannot mount volume on non-empty path" requirement
# define modal app
image = (
modal.Image.debian_slim(python_version="3.11")
# install required system and pip packages, more about this modal approach: https://modal.com/docs/examples/dreambooth_app
.apt_install("libgl1", "libglib2.0-0")
.pip_install(
"python-dotenv",
"torch",
"diffusers[torch]",
"transformers",
"ftfy",
"torchvision",
"oyaml",
"opencv-python",
"albumentations",
"safetensors",
"lycoris-lora==1.8.3",
"flatten_json",
"pyyaml",
"tensorboard",
"kornia",
"invisible-watermark",
"einops",
"accelerate",
"toml",
"pydantic",
"omegaconf",
"k-diffusion",
"open_clip_torch",
"timm",
"prodigyopt",
"controlnet_aux==0.0.7",
"bitsandbytes",
"hf_transfer",
"lpips",
"pytorch_fid",
"optimum-quanto",
"sentencepiece",
"huggingface_hub",
"peft"
)
)
# mount for the entire ai-toolkit directory
# example: "/Users/username/ai-toolkit" is the local directory, "/root/ai-toolkit" is the remote directory
code_mount = modal.Mount.from_local_dir("/Users/username/ai-toolkit", remote_path="/root/ai-toolkit")
# create the Modal app with the necessary mounts and volumes
app = modal.App(name="flux-lora-training", image=image, mounts=[code_mount], volumes={MOUNT_DIR: model_volume})
# Check if we have DEBUG_TOOLKIT in env
if os.environ.get("DEBUG_TOOLKIT", "0") == "1":
# Set torch to trace mode
import torch
torch.autograd.set_detect_anomaly(True)
import argparse
from toolkit.job import get_job
def print_end_message(jobs_completed, jobs_failed):
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("")
print("========================================")
print("Result:")
if len(completed_string) > 0:
print(f" - {completed_string}")
if len(failure_string) > 0:
print(f" - {failure_string}")
print("========================================")
@app.function(
# request a GPU with at least 24GB VRAM
# more about modal GPU's: https://modal.com/docs/guide/gpu
gpu="A100", # gpu="H100"
# more about modal timeouts: https://modal.com/docs/guide/timeouts
timeout=7200 # 2 hours, increase or decrease if needed
)
def main(config_file_list_str: str, recover: bool = False, name: str = None):
# convert the config file list from a string to a list
config_file_list = config_file_list_str.split(",")
jobs_completed = 0
jobs_failed = 0
print(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, name)
job.config['process'][0]['training_folder'] = MOUNT_DIR
os.makedirs(MOUNT_DIR, exist_ok=True)
print(f"Training outputs will be saved to: {MOUNT_DIR}")
# run the job
job.run()
# commit the volume after training
model_volume.commit()
job.cleanup()
jobs_completed += 1
except Exception as e:
print(f"Error running job: {e}")
jobs_failed += 1
if not recover:
print_end_message(jobs_completed, jobs_failed)
raise e
print_end_message(jobs_completed, jobs_failed)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# require at least 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 a job fails
parser.add_argument(
'-r', '--recover',
action='store_true',
help='Continue running additional jobs even if a job fails'
)
# optional name replacement for config file
parser.add_argument(
'-n', '--name',
type=str,
default=None,
help='Name to replace [name] tag in config file, useful for shared config file'
)
args = parser.parse_args()
# convert list of config files to a comma-separated string for Modal compatibility
config_file_list_str = ",".join(args.config_file_list)
main.call(config_file_list_str=config_file_list_str, recover=args.recover, name=args.name)
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