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''' |
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ostris/ai-toolkit on https://modal.com |
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Run training with the following command: |
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modal run run_modal.py --config-file-list-str=/root/ai-toolkit/config/whatever_you_want.yml |
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''' |
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import os |
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" |
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import sys |
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import modal |
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from dotenv import load_dotenv |
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load_dotenv() |
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sys.path.insert(0, "/root/ai-toolkit") |
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os.environ['DISABLE_TELEMETRY'] = 'YES' |
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model_volume = modal.Volume.from_name("flux-lora-models", create_if_missing=True) |
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MOUNT_DIR = "/root/ai-toolkit/modal_output" |
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image = ( |
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modal.Image.debian_slim(python_version="3.11") |
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.apt_install("libgl1", "libglib2.0-0") |
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.pip_install( |
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"python-dotenv", |
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"torch", |
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"diffusers[torch]", |
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"transformers", |
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"ftfy", |
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"torchvision", |
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"oyaml", |
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"opencv-python", |
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"albumentations", |
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"safetensors", |
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"lycoris-lora==1.8.3", |
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"flatten_json", |
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"pyyaml", |
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"tensorboard", |
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"kornia", |
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"invisible-watermark", |
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"einops", |
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"accelerate", |
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"toml", |
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"pydantic", |
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"omegaconf", |
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"k-diffusion", |
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"open_clip_torch", |
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"timm", |
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"prodigyopt", |
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"controlnet_aux==0.0.7", |
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"bitsandbytes", |
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"hf_transfer", |
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"lpips", |
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"pytorch_fid", |
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"optimum-quanto", |
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"sentencepiece", |
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"huggingface_hub", |
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"peft" |
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) |
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) |
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code_mount = modal.Mount.from_local_dir("/Users/username/ai-toolkit", remote_path="/root/ai-toolkit") |
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app = modal.App(name="flux-lora-training", image=image, mounts=[code_mount], volumes={MOUNT_DIR: model_volume}) |
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if os.environ.get("DEBUG_TOOLKIT", "0") == "1": |
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import torch |
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torch.autograd.set_detect_anomaly(True) |
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import argparse |
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from toolkit.job import get_job |
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def print_end_message(jobs_completed, jobs_failed): |
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failure_string = f"{jobs_failed} failure{'' if jobs_failed == 1 else 's'}" if jobs_failed > 0 else "" |
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completed_string = f"{jobs_completed} completed job{'' if jobs_completed == 1 else 's'}" |
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print("") |
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print("========================================") |
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print("Result:") |
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if len(completed_string) > 0: |
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print(f" - {completed_string}") |
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if len(failure_string) > 0: |
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print(f" - {failure_string}") |
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print("========================================") |
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@app.function( |
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gpu="A100", |
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timeout=7200 |
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) |
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def main(config_file_list_str: str, recover: bool = False, name: str = None): |
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config_file_list = config_file_list_str.split(",") |
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jobs_completed = 0 |
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jobs_failed = 0 |
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print(f"Running {len(config_file_list)} job{'' if len(config_file_list) == 1 else 's'}") |
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for config_file in config_file_list: |
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try: |
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job = get_job(config_file, name) |
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job.config['process'][0]['training_folder'] = MOUNT_DIR |
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os.makedirs(MOUNT_DIR, exist_ok=True) |
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print(f"Training outputs will be saved to: {MOUNT_DIR}") |
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job.run() |
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model_volume.commit() |
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job.cleanup() |
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jobs_completed += 1 |
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except Exception as e: |
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print(f"Error running job: {e}") |
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jobs_failed += 1 |
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if not recover: |
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print_end_message(jobs_completed, jobs_failed) |
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raise e |
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print_end_message(jobs_completed, jobs_failed) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'config_file_list', |
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nargs='+', |
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type=str, |
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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' |
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) |
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parser.add_argument( |
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'-r', '--recover', |
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action='store_true', |
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help='Continue running additional jobs even if a job fails' |
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) |
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parser.add_argument( |
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'-n', '--name', |
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type=str, |
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default=None, |
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help='Name to replace [name] tag in config file, useful for shared config file' |
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) |
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args = parser.parse_args() |
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config_file_list_str = ",".join(args.config_file_list) |
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main.call(config_file_list_str=config_file_list_str, recover=args.recover, name=args.name) |
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