File size: 5,862 Bytes
1ba389d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
'''

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)