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Runtime error
burtenshaw
commited on
Commit
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29272e4
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Parent(s):
54cffe3
first commit
Browse files- .python-version +1 -0
- app.py +916 -0
- pyproject.toml +54 -0
- requirements.txt +182 -0
- uv.lock +0 -0
.python-version
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3.11
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app.py
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@@ -0,0 +1,916 @@
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1 |
+
"""
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2 |
+
AutoTrain Gradio MCP Server - All-in-One
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3 |
+
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4 |
+
This single Gradio app:
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5 |
+
1. Provides a web interface for managing AutoTrain jobs
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6 |
+
2. Automatically exposes MCP tools at /gradio_api/mcp/sse
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7 |
+
3. Handles all AutoTrain operations directly (no FastAPI needed)
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8 |
+
"""
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9 |
+
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10 |
+
import os
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import json
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12 |
+
import time
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+
import uuid
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14 |
+
import threading
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+
from datetime import datetime
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16 |
+
from typing import List, Dict, Any
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17 |
+
import socket
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18 |
+
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19 |
+
import gradio as gr
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20 |
+
import pandas as pd
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21 |
+
import wandb
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22 |
+
from autotrain.project import AutoTrainProject
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23 |
+
from autotrain.params import (
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24 |
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LLMTrainingParams,
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25 |
+
TextClassificationParams,
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26 |
+
ImageClassificationParams,
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27 |
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)
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28 |
+
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29 |
+
# Simple JSON-based storage (replace with SQLite if needed)
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30 |
+
RUNS_FILE = "training_runs.json"
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31 |
+
WANDB_PROJECT = os.environ.get("WANDB_PROJECT", "autotrain-mcp")
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32 |
+
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33 |
+
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34 |
+
def load_runs() -> List[Dict[str, Any]]:
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35 |
+
"""Load training runs from JSON file"""
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36 |
+
if os.path.exists(RUNS_FILE):
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37 |
+
try:
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38 |
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with open(RUNS_FILE, "r") as f:
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39 |
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return json.load(f)
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40 |
+
except (json.JSONDecodeError, IOError):
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41 |
+
return []
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42 |
+
return []
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43 |
+
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44 |
+
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45 |
+
def save_runs(runs: List[Dict[str, Any]]):
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46 |
+
"""Save training runs to JSON file"""
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47 |
+
with open(RUNS_FILE, "w") as f:
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48 |
+
json.dump(runs, f, indent=2)
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49 |
+
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50 |
+
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51 |
+
def get_status_emoji(status: str) -> str:
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52 |
+
"""Get emoji for training status"""
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53 |
+
emoji_map = {
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54 |
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"pending": "⏳",
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55 |
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"running": "🏃",
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56 |
+
"completed": "✅",
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57 |
+
"failed": "❌",
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58 |
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"cancelled": "⏹️",
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59 |
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}
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60 |
+
return emoji_map.get(status.lower(), "❓")
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61 |
+
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62 |
+
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63 |
+
def create_autotrain_params(
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64 |
+
task: str,
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65 |
+
base_model: str,
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66 |
+
project_name: str,
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67 |
+
dataset_path: str,
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68 |
+
epochs: int,
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69 |
+
batch_size: int,
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70 |
+
learning_rate: float,
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71 |
+
**kwargs,
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72 |
+
):
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73 |
+
"""Create AutoTrain parameter object based on task type"""
|
74 |
+
common_params = {
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75 |
+
"model": base_model,
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76 |
+
"project_name": project_name,
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77 |
+
"data_path": dataset_path,
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78 |
+
"train_split": kwargs.get("train_split", "train"),
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79 |
+
"valid_split": kwargs.get("valid_split"),
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80 |
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"epochs": epochs,
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81 |
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"batch_size": batch_size,
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82 |
+
"lr": learning_rate,
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83 |
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"log": "wandb",
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84 |
+
# Required defaults
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85 |
+
"warmup_ratio": 0.1,
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86 |
+
"gradient_accumulation": 1,
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87 |
+
"optimizer": "adamw_torch",
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88 |
+
"scheduler": "linear",
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89 |
+
"weight_decay": 0.01,
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90 |
+
"max_grad_norm": 1.0,
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91 |
+
"seed": 42,
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92 |
+
"logging_steps": 10,
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93 |
+
"auto_find_batch_size": False,
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94 |
+
"mixed_precision": "no",
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95 |
+
"save_total_limit": 1,
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96 |
+
"eval_strategy": "epoch",
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97 |
+
}
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98 |
+
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99 |
+
if task == "text-classification":
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100 |
+
return TextClassificationParams(
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101 |
+
**common_params,
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102 |
+
text_column=kwargs.get("text_column", "text"),
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103 |
+
target_column=kwargs.get("target_column", "label"),
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104 |
+
max_seq_length=kwargs.get("max_seq_length", 128),
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105 |
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early_stopping_patience=3,
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106 |
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early_stopping_threshold=0.01,
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107 |
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)
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108 |
+
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109 |
+
elif task.startswith("llm-"):
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110 |
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trainer_map = {
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111 |
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"llm-sft": "sft",
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112 |
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"llm-dpo": "dpo",
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113 |
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"llm-orpo": "orpo",
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114 |
+
"llm-reward": "reward",
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115 |
+
}
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116 |
+
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117 |
+
return LLMTrainingParams(
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118 |
+
**{
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119 |
+
k: v
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120 |
+
for k, v in common_params.items()
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121 |
+
if k not in ["early_stopping_patience", "early_stopping_threshold"]
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122 |
+
},
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123 |
+
text_column=kwargs.get("text_column", "messages"),
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124 |
+
block_size=kwargs.get("block_size", 2048),
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125 |
+
peft=kwargs.get("use_peft", True),
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126 |
+
quantization=kwargs.get("quantization", "int4"),
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127 |
+
trainer=trainer_map[task],
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128 |
+
chat_template="tokenizer",
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129 |
+
# LLM-specific defaults
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130 |
+
add_eos_token=True,
|
131 |
+
model_max_length=2048,
|
132 |
+
padding="right",
|
133 |
+
use_flash_attention_2=False,
|
134 |
+
disable_gradient_checkpointing=False,
|
135 |
+
target_modules="all-linear",
|
136 |
+
merge_adapter=False,
|
137 |
+
lora_r=16,
|
138 |
+
lora_alpha=32,
|
139 |
+
lora_dropout=0.05,
|
140 |
+
model_ref=None,
|
141 |
+
dpo_beta=0.1,
|
142 |
+
max_prompt_length=512,
|
143 |
+
max_completion_length=1024,
|
144 |
+
prompt_text_column="prompt",
|
145 |
+
rejected_text_column="rejected",
|
146 |
+
unsloth=False,
|
147 |
+
distributed_backend="accelerate",
|
148 |
+
)
|
149 |
+
|
150 |
+
elif task == "image-classification":
|
151 |
+
return ImageClassificationParams(
|
152 |
+
**common_params,
|
153 |
+
image_column=kwargs.get("image_column", "image"),
|
154 |
+
target_column=kwargs.get("target_column", "label"),
|
155 |
+
)
|
156 |
+
|
157 |
+
else:
|
158 |
+
raise ValueError(f"Unsupported task type: {task}")
|
159 |
+
|
160 |
+
|
161 |
+
def run_training_background(run_id: str, params: Any, backend: str):
|
162 |
+
"""Run training job in background thread"""
|
163 |
+
runs = load_runs()
|
164 |
+
|
165 |
+
# Update status to running
|
166 |
+
for run in runs:
|
167 |
+
if run["run_id"] == run_id:
|
168 |
+
run["status"] = "running"
|
169 |
+
run["started_at"] = datetime.utcnow().isoformat()
|
170 |
+
break
|
171 |
+
save_runs(runs)
|
172 |
+
|
173 |
+
try:
|
174 |
+
# Initialize W&B
|
175 |
+
wandb_run = wandb.init(
|
176 |
+
project=WANDB_PROJECT,
|
177 |
+
name=f"{params.project_name}-{int(time.time())}",
|
178 |
+
tags=["autotrain", "mcp"],
|
179 |
+
config={
|
180 |
+
"base_model": params.model,
|
181 |
+
"dataset": params.data_path,
|
182 |
+
"epochs": params.epochs,
|
183 |
+
"batch_size": params.batch_size,
|
184 |
+
"learning_rate": params.lr,
|
185 |
+
"backend": backend,
|
186 |
+
},
|
187 |
+
)
|
188 |
+
|
189 |
+
wandb_url = (
|
190 |
+
wandb_run.url if wandb_run.url else f"https://wandb.ai/{WANDB_PROJECT}"
|
191 |
+
)
|
192 |
+
|
193 |
+
# Update with W&B URL
|
194 |
+
runs = load_runs()
|
195 |
+
for run in runs:
|
196 |
+
if run["run_id"] == run_id:
|
197 |
+
run["wandb_url"] = wandb_url
|
198 |
+
break
|
199 |
+
save_runs(runs)
|
200 |
+
|
201 |
+
# Create and start AutoTrain project
|
202 |
+
project = AutoTrainProject(params=params, backend=backend, process=True)
|
203 |
+
job_id = project.create()
|
204 |
+
|
205 |
+
print(f"Training started for run {run_id} with job ID: {job_id}")
|
206 |
+
|
207 |
+
# For demo purposes, simulate training completion after a short delay
|
208 |
+
time.sleep(10) # In real implementation, monitor actual training
|
209 |
+
|
210 |
+
# Update status to completed
|
211 |
+
runs = load_runs()
|
212 |
+
for run in runs:
|
213 |
+
if run["run_id"] == run_id:
|
214 |
+
run["status"] = "completed"
|
215 |
+
run["completed_at"] = datetime.utcnow().isoformat()
|
216 |
+
break
|
217 |
+
save_runs(runs)
|
218 |
+
|
219 |
+
wandb.finish()
|
220 |
+
|
221 |
+
except Exception as e:
|
222 |
+
print(f"Training failed for run {run_id}: {str(e)}")
|
223 |
+
|
224 |
+
# Update status to failed
|
225 |
+
runs = load_runs()
|
226 |
+
for run in runs:
|
227 |
+
if run["run_id"] == run_id:
|
228 |
+
run["status"] = "failed"
|
229 |
+
run["error_message"] = str(e)
|
230 |
+
run["completed_at"] = datetime.utcnow().isoformat()
|
231 |
+
break
|
232 |
+
save_runs(runs)
|
233 |
+
|
234 |
+
if wandb.run:
|
235 |
+
wandb.finish()
|
236 |
+
|
237 |
+
|
238 |
+
# MCP Tool Functions (these automatically become MCP tools)
|
239 |
+
def start_training_job(
|
240 |
+
task: str = "text-classification",
|
241 |
+
project_name: str = "test-project",
|
242 |
+
base_model: str = "distilbert-base-uncased",
|
243 |
+
dataset_path: str = "imdb",
|
244 |
+
epochs: str = "1",
|
245 |
+
batch_size: str = "8",
|
246 |
+
learning_rate: str = "2e-5",
|
247 |
+
backend: str = "local",
|
248 |
+
) -> str:
|
249 |
+
"""
|
250 |
+
Start a new AutoTrain training job.
|
251 |
+
|
252 |
+
Args:
|
253 |
+
task: Type of training task (text-classification, llm-sft,
|
254 |
+
llm-dpo, llm-orpo, image-classification)
|
255 |
+
project_name: Name for the training project
|
256 |
+
base_model: Base model from Hugging Face Hub
|
257 |
+
(e.g., distilbert-base-uncased)
|
258 |
+
dataset_path: Dataset path or HF dataset name (e.g., imdb)
|
259 |
+
epochs: Number of training epochs (default: 3)
|
260 |
+
batch_size: Training batch size (default: 16)
|
261 |
+
learning_rate: Learning rate for training (default: 2e-5)
|
262 |
+
backend: Training backend to use (default: local)
|
263 |
+
|
264 |
+
Returns:
|
265 |
+
Status message with run ID and details
|
266 |
+
"""
|
267 |
+
try:
|
268 |
+
# Convert string parameters
|
269 |
+
epochs_int = int(epochs)
|
270 |
+
batch_size_int = int(batch_size)
|
271 |
+
learning_rate_float = float(learning_rate)
|
272 |
+
|
273 |
+
# Generate run ID
|
274 |
+
run_id = str(uuid.uuid4())
|
275 |
+
|
276 |
+
# Create run record
|
277 |
+
run_data = {
|
278 |
+
"run_id": run_id,
|
279 |
+
"project_name": project_name,
|
280 |
+
"task": task,
|
281 |
+
"base_model": base_model,
|
282 |
+
"dataset_path": dataset_path,
|
283 |
+
"status": "pending",
|
284 |
+
"created_at": datetime.utcnow().isoformat(),
|
285 |
+
"updated_at": datetime.utcnow().isoformat(),
|
286 |
+
"config": {
|
287 |
+
"task": task,
|
288 |
+
"epochs": epochs_int,
|
289 |
+
"batch_size": batch_size_int,
|
290 |
+
"learning_rate": learning_rate_float,
|
291 |
+
"backend": backend,
|
292 |
+
},
|
293 |
+
}
|
294 |
+
|
295 |
+
# Save to storage
|
296 |
+
runs = load_runs()
|
297 |
+
runs.append(run_data)
|
298 |
+
save_runs(runs)
|
299 |
+
|
300 |
+
# Create AutoTrain parameters
|
301 |
+
params = create_autotrain_params(
|
302 |
+
task=task,
|
303 |
+
base_model=base_model,
|
304 |
+
project_name=project_name,
|
305 |
+
dataset_path=dataset_path,
|
306 |
+
epochs=epochs_int,
|
307 |
+
batch_size=batch_size_int,
|
308 |
+
learning_rate=learning_rate_float,
|
309 |
+
)
|
310 |
+
|
311 |
+
# Start training in background
|
312 |
+
thread = threading.Thread(
|
313 |
+
target=run_training_background, args=(run_id, params, backend)
|
314 |
+
)
|
315 |
+
thread.daemon = True
|
316 |
+
thread.start()
|
317 |
+
|
318 |
+
return f"""✅ Training job submitted successfully!
|
319 |
+
|
320 |
+
Run ID: {run_id}
|
321 |
+
Project: {project_name}
|
322 |
+
Task: {task}
|
323 |
+
Model: {base_model}
|
324 |
+
Dataset: {dataset_path}
|
325 |
+
|
326 |
+
Configuration:
|
327 |
+
• Epochs: {epochs}
|
328 |
+
• Batch Size: {batch_size}
|
329 |
+
• Learning Rate: {learning_rate}
|
330 |
+
• Backend: {backend}
|
331 |
+
|
332 |
+
🔗 Monitor progress:
|
333 |
+
• Gradio UI: http://localhost:7860
|
334 |
+
• W&B tracking will be available once training starts
|
335 |
+
|
336 |
+
💡 Use get_training_runs() to check status"""
|
337 |
+
|
338 |
+
except Exception as e:
|
339 |
+
return f"❌ Error submitting job: {str(e)}"
|
340 |
+
|
341 |
+
|
342 |
+
def get_training_runs(limit: str = "20", status: str = "") -> str:
|
343 |
+
"""
|
344 |
+
Get list of training runs with their status and details.
|
345 |
+
|
346 |
+
Args:
|
347 |
+
limit: Maximum number of runs to return (default: 20)
|
348 |
+
status: Filter by run status (pending, running, completed,
|
349 |
+
failed, cancelled)
|
350 |
+
|
351 |
+
Returns:
|
352 |
+
Formatted list of training runs with status and links
|
353 |
+
"""
|
354 |
+
try:
|
355 |
+
runs = load_runs()
|
356 |
+
|
357 |
+
# Filter by status if provided
|
358 |
+
if status:
|
359 |
+
runs = [run for run in runs if run.get("status") == status]
|
360 |
+
|
361 |
+
# Apply limit
|
362 |
+
runs = runs[-int(limit) :]
|
363 |
+
|
364 |
+
if not runs:
|
365 |
+
return "No training runs found. Start a new training job to see it here!"
|
366 |
+
|
367 |
+
runs_text = f"📊 Training Runs (showing {len(runs)}):\n\n"
|
368 |
+
|
369 |
+
for run in reversed(runs): # Show newest first
|
370 |
+
status_emoji = get_status_emoji(run["status"])
|
371 |
+
|
372 |
+
# Format run display with line break
|
373 |
+
run_display = (
|
374 |
+
f"{status_emoji} **{run['project_name']}** ({run['run_id'][:8]}...)"
|
375 |
+
)
|
376 |
+
runs_text += f"{run_display}\n"
|
377 |
+
runs_text += f" Task: {run['task']}\n"
|
378 |
+
runs_text += f" Model: {run['base_model']}\n"
|
379 |
+
runs_text += f" Status: {run['status'].title()}\n"
|
380 |
+
runs_text += f" Created: {run['created_at']}\n"
|
381 |
+
|
382 |
+
if run.get("wandb_url"):
|
383 |
+
runs_text += f" 🔗 W&B: {run['wandb_url']}\n"
|
384 |
+
|
385 |
+
if run.get("error_message"):
|
386 |
+
runs_text += f" ❌ Error: {run['error_message']}\n"
|
387 |
+
|
388 |
+
runs_text += "\n"
|
389 |
+
|
390 |
+
return runs_text
|
391 |
+
|
392 |
+
except Exception as e:
|
393 |
+
return f"❌ Error fetching runs: {str(e)}"
|
394 |
+
|
395 |
+
|
396 |
+
def get_run_details(run_id: str) -> str:
|
397 |
+
"""
|
398 |
+
Get detailed information about a specific training run.
|
399 |
+
|
400 |
+
Args:
|
401 |
+
run_id: ID of the training run (can be partial ID)
|
402 |
+
|
403 |
+
Returns:
|
404 |
+
Detailed run information including config and status
|
405 |
+
"""
|
406 |
+
try:
|
407 |
+
runs = load_runs()
|
408 |
+
|
409 |
+
# Find run by full or partial ID
|
410 |
+
found_run = None
|
411 |
+
for run in runs:
|
412 |
+
if run["run_id"] == run_id or run["run_id"].startswith(run_id):
|
413 |
+
found_run = run
|
414 |
+
break
|
415 |
+
|
416 |
+
if not found_run:
|
417 |
+
return f"❌ Training run {run_id} not found"
|
418 |
+
|
419 |
+
run = found_run
|
420 |
+
details_text = f"""📋 Training Run Details
|
421 |
+
|
422 |
+
**Run ID:** {run["run_id"]}
|
423 |
+
**Project:** {run["project_name"]}
|
424 |
+
**Task:** {run["task"]}
|
425 |
+
**Model:** {run["base_model"]}
|
426 |
+
**Dataset:** {run["dataset_path"]}
|
427 |
+
**Status:** {run["status"].title()}
|
428 |
+
|
429 |
+
**Timestamps:**
|
430 |
+
• Created: {run["created_at"]}
|
431 |
+
• Updated: {run.get("updated_at", "N/A")}"""
|
432 |
+
|
433 |
+
if run.get("started_at"):
|
434 |
+
details_text += f"\n• Started: {run['started_at']}"
|
435 |
+
if run.get("completed_at"):
|
436 |
+
details_text += f"\n• Completed: {run['completed_at']}"
|
437 |
+
|
438 |
+
if run.get("wandb_url"):
|
439 |
+
details_text += f"\n\n🔗 **W&B Dashboard:** {run['wandb_url']}"
|
440 |
+
|
441 |
+
if run.get("error_message"):
|
442 |
+
details_text += f"\n\n❌ **Error:** {run['error_message']}"
|
443 |
+
|
444 |
+
if run.get("config"):
|
445 |
+
config = run["config"]
|
446 |
+
details_text += "\n\n⚙️ **Training Configuration:**"
|
447 |
+
details_text += f"\n• Epochs: {config.get('epochs')}"
|
448 |
+
details_text += f"\n• Batch Size: {config.get('batch_size')}"
|
449 |
+
details_text += f"\n• Learning Rate: {config.get('learning_rate')}"
|
450 |
+
details_text += f"\n• Backend: {config.get('backend')}"
|
451 |
+
|
452 |
+
return details_text
|
453 |
+
|
454 |
+
except Exception as e:
|
455 |
+
return f"❌ Error fetching run details: {str(e)}"
|
456 |
+
|
457 |
+
|
458 |
+
def get_task_recommendations(
|
459 |
+
task: str = "text-classification", dataset_size: str = "medium"
|
460 |
+
) -> str:
|
461 |
+
"""
|
462 |
+
Get training recommendations for a specific task type.
|
463 |
+
|
464 |
+
Args:
|
465 |
+
task: Task type (text-classification, llm-sft, image-classification)
|
466 |
+
dataset_size: Size of dataset (small, medium, large)
|
467 |
+
|
468 |
+
Returns:
|
469 |
+
Recommended models, parameters, and best practices
|
470 |
+
"""
|
471 |
+
recommendations = {
|
472 |
+
"text-classification": {
|
473 |
+
"models": ["distilbert-base-uncased", "bert-base-uncased", "roberta-base"],
|
474 |
+
"params": {"batch_size": 16, "learning_rate": 2e-5, "epochs": 3},
|
475 |
+
"backends": ["local", "spaces-t4-small"],
|
476 |
+
"notes": [
|
477 |
+
"Good for sentiment analysis",
|
478 |
+
"Works well with IMDB, AG News datasets",
|
479 |
+
],
|
480 |
+
},
|
481 |
+
"llm-sft": {
|
482 |
+
"models": [
|
483 |
+
"microsoft/DialoGPT-medium",
|
484 |
+
"HuggingFaceTB/SmolLM2-1.7B-Instruct",
|
485 |
+
],
|
486 |
+
"params": {"batch_size": 1, "learning_rate": 1e-5, "epochs": 3},
|
487 |
+
"backends": ["spaces-t4-medium", "spaces-a10g-large"],
|
488 |
+
"notes": ["Use PEFT for efficiency", "Ensure proper chat formatting"],
|
489 |
+
},
|
490 |
+
"image-classification": {
|
491 |
+
"models": ["google/vit-base-patch16-224", "microsoft/resnet-50"],
|
492 |
+
"params": {"batch_size": 32, "learning_rate": 2e-5, "epochs": 5},
|
493 |
+
"backends": ["local", "spaces-t4-small"],
|
494 |
+
"notes": ["Ensure images are preprocessed", "Works with CIFAR, ImageNet"],
|
495 |
+
},
|
496 |
+
}
|
497 |
+
|
498 |
+
rec = recommendations.get(
|
499 |
+
task,
|
500 |
+
{
|
501 |
+
"models": [],
|
502 |
+
"params": {},
|
503 |
+
"backends": ["local"],
|
504 |
+
"notes": ["No specific recommendations available"],
|
505 |
+
},
|
506 |
+
)
|
507 |
+
|
508 |
+
rec_text = f"""🎯 Training Recommendations for {task.title()} \
|
509 |
+
({dataset_size} dataset)
|
510 |
+
|
511 |
+
**Recommended Models:**
|
512 |
+
{chr(10).join(f"• {model}" for model in rec["models"])}
|
513 |
+
|
514 |
+
**Recommended Parameters:**
|
515 |
+
{chr(10).join(f"• {k}: {v}" for k, v in rec["params"].items())}
|
516 |
+
|
517 |
+
**Backend Suggestions:**
|
518 |
+
{chr(10).join(f"• {backend}" for backend in rec["backends"])}
|
519 |
+
|
520 |
+
**Best Practices:**
|
521 |
+
{chr(10).join(f"• {note}" for note in rec["notes"])}"""
|
522 |
+
|
523 |
+
return rec_text
|
524 |
+
|
525 |
+
|
526 |
+
def get_system_status(random_string: str = "") -> str:
|
527 |
+
"""
|
528 |
+
Get AutoTrain system status and capabilities.
|
529 |
+
|
530 |
+
Returns:
|
531 |
+
System status, available tasks, backends, and statistics
|
532 |
+
"""
|
533 |
+
try:
|
534 |
+
runs = load_runs()
|
535 |
+
|
536 |
+
# Calculate stats
|
537 |
+
total_runs = len(runs)
|
538 |
+
running_runs = len([r for r in runs if r.get("status") == "running"])
|
539 |
+
completed_runs = len([r for r in runs if r.get("status") == "completed"])
|
540 |
+
failed_runs = len([r for r in runs if r.get("status") == "failed"])
|
541 |
+
|
542 |
+
available_tasks = [
|
543 |
+
"text-classification",
|
544 |
+
"llm-sft",
|
545 |
+
"llm-dpo",
|
546 |
+
"llm-orpo",
|
547 |
+
"image-classification",
|
548 |
+
]
|
549 |
+
|
550 |
+
available_backends = [
|
551 |
+
"local",
|
552 |
+
"spaces-t4-small",
|
553 |
+
"spaces-t4-medium",
|
554 |
+
"spaces-a10g-large",
|
555 |
+
"spaces-a10g-small",
|
556 |
+
"spaces-a100-large",
|
557 |
+
"spaces-l4x1",
|
558 |
+
"spaces-l4x4",
|
559 |
+
]
|
560 |
+
|
561 |
+
status_text = f"""🚀 AutoTrain Gradio MCP Server - System Status
|
562 |
+
|
563 |
+
**Server Status:** Running
|
564 |
+
**Total Runs:** {total_runs}
|
565 |
+
**Active Runs:** {running_runs}
|
566 |
+
**Completed Runs:** {completed_runs}
|
567 |
+
**Failed Runs:** {failed_runs}
|
568 |
+
|
569 |
+
**Available Tasks:** {len(available_tasks)}
|
570 |
+
{chr(10).join(f" • {task}" for task in available_tasks)}
|
571 |
+
|
572 |
+
**Available Backends:** {len(available_backends)}
|
573 |
+
{chr(10).join(f" • {backend}" for backend in available_backends[:10])}
|
574 |
+
{
|
575 |
+
f" ... and {len(available_backends) - 10} more"
|
576 |
+
if len(available_backends) > 10
|
577 |
+
else ""
|
578 |
+
}
|
579 |
+
|
580 |
+
💡 **Access Points:**
|
581 |
+
• Gradio UI: http://localhost:7860
|
582 |
+
• MCP Server: http://localhost:7860/gradio_api/mcp/sse
|
583 |
+
• MCP Schema: http://localhost:7860/gradio_api/mcp/schema
|
584 |
+
|
585 |
+
🛠️ **W&B Integration:**
|
586 |
+
• Project: {WANDB_PROJECT}
|
587 |
+
• Set WANDB_PROJECT environment variable to customize"""
|
588 |
+
|
589 |
+
return status_text
|
590 |
+
|
591 |
+
except Exception as e:
|
592 |
+
return f"❌ Error getting system status: {str(e)}"
|
593 |
+
|
594 |
+
|
595 |
+
def refresh_data(random_string: str = "") -> str:
|
596 |
+
"""Refresh data for UI updates"""
|
597 |
+
return "Data refreshed successfully"
|
598 |
+
|
599 |
+
|
600 |
+
def load_initial_data(random_string: str = "") -> str:
|
601 |
+
"""Load initial data for the application"""
|
602 |
+
return "Initial data loaded successfully"
|
603 |
+
|
604 |
+
|
605 |
+
# Web UI Functions
|
606 |
+
def fetch_runs_for_ui():
|
607 |
+
"""Fetch runs for the web interface table"""
|
608 |
+
try:
|
609 |
+
runs = load_runs()
|
610 |
+
|
611 |
+
if not runs:
|
612 |
+
return pd.DataFrame(
|
613 |
+
{
|
614 |
+
"Status": [],
|
615 |
+
"Project": [],
|
616 |
+
"Task": [],
|
617 |
+
"Model": [],
|
618 |
+
"Created": [],
|
619 |
+
"W&B Link": [],
|
620 |
+
"Run ID": [],
|
621 |
+
}
|
622 |
+
)
|
623 |
+
|
624 |
+
data = []
|
625 |
+
for run in reversed(runs): # Newest first
|
626 |
+
wandb_link = ""
|
627 |
+
if run.get("wandb_url"):
|
628 |
+
wandb_link = (
|
629 |
+
f'<a href="{run["wandb_url"]}" target="_blank">View W&B</a>'
|
630 |
+
)
|
631 |
+
|
632 |
+
data.append(
|
633 |
+
{
|
634 |
+
"Status": f"{get_status_emoji(run['status'])} {run['status'].title()}",
|
635 |
+
"Project": run["project_name"],
|
636 |
+
"Task": run["task"].replace("-", " ").title(),
|
637 |
+
"Model": run["base_model"],
|
638 |
+
"Created": run["created_at"][:16].replace("T", " "),
|
639 |
+
"W&B Link": wandb_link,
|
640 |
+
"Run ID": run["run_id"][:8] + "...",
|
641 |
+
}
|
642 |
+
)
|
643 |
+
|
644 |
+
return pd.DataFrame(data)
|
645 |
+
|
646 |
+
except Exception as e:
|
647 |
+
return pd.DataFrame({"Error": [f"Failed to fetch runs: {str(e)}"]})
|
648 |
+
|
649 |
+
|
650 |
+
def submit_training_job_ui(
|
651 |
+
task,
|
652 |
+
project_name,
|
653 |
+
base_model,
|
654 |
+
dataset_path,
|
655 |
+
epochs,
|
656 |
+
batch_size,
|
657 |
+
learning_rate,
|
658 |
+
backend,
|
659 |
+
):
|
660 |
+
"""Submit training job from web UI"""
|
661 |
+
if not all([task, project_name, base_model, dataset_path]):
|
662 |
+
return "❌ Please fill in all required fields", fetch_runs_for_ui()
|
663 |
+
|
664 |
+
result = start_training_job(
|
665 |
+
task=task,
|
666 |
+
project_name=project_name,
|
667 |
+
base_model=base_model,
|
668 |
+
dataset_path=dataset_path,
|
669 |
+
epochs=str(epochs),
|
670 |
+
batch_size=str(batch_size),
|
671 |
+
learning_rate=str(learning_rate),
|
672 |
+
backend=backend,
|
673 |
+
)
|
674 |
+
|
675 |
+
return result, fetch_runs_for_ui()
|
676 |
+
|
677 |
+
|
678 |
+
# Create Gradio Interface
|
679 |
+
with gr.Blocks(
|
680 |
+
title="AutoTrain Gradio MCP Server",
|
681 |
+
theme=gr.themes.Soft(),
|
682 |
+
css="""
|
683 |
+
.gradio-container {
|
684 |
+
max-width: 1200px !important;
|
685 |
+
}
|
686 |
+
""",
|
687 |
+
) as app:
|
688 |
+
gr.Markdown("""
|
689 |
+
# 🚀 AutoTrain Gradio MCP Server
|
690 |
+
|
691 |
+
**All-in-One Solution:** Web UI + MCP Server + AutoTrain Integration
|
692 |
+
|
693 |
+
• **Web Interface**: Manage training jobs through this UI
|
694 |
+
• **MCP Server**: AI assistants can use tools at `http://localhost:7860/gradio_api/mcp/sse`
|
695 |
+
• **Direct Integration**: No FastAPI needed - everything runs in Gradio
|
696 |
+
""")
|
697 |
+
|
698 |
+
with gr.Tabs():
|
699 |
+
# Dashboard Tab
|
700 |
+
with gr.Tab("📊 Dashboard"):
|
701 |
+
with gr.Row():
|
702 |
+
with gr.Column(scale=3):
|
703 |
+
gr.Markdown("## Training Runs")
|
704 |
+
refresh_btn = gr.Button("🔄 Refresh", variant="secondary")
|
705 |
+
runs_table = gr.Dataframe(
|
706 |
+
value=fetch_runs_for_ui(), interactive=False
|
707 |
+
)
|
708 |
+
|
709 |
+
with gr.Column(scale=1):
|
710 |
+
gr.Markdown("## Quick Stats")
|
711 |
+
stats = gr.Textbox(
|
712 |
+
value=get_system_status(), interactive=False, lines=15
|
713 |
+
)
|
714 |
+
|
715 |
+
# Start Training Tab
|
716 |
+
with gr.Tab("🏃 Start Training"):
|
717 |
+
gr.Markdown("## Submit New Training Job")
|
718 |
+
|
719 |
+
with gr.Row():
|
720 |
+
with gr.Column():
|
721 |
+
task_dropdown = gr.Dropdown(
|
722 |
+
choices=[
|
723 |
+
"text-classification",
|
724 |
+
"llm-sft",
|
725 |
+
"llm-dpo",
|
726 |
+
"llm-orpo",
|
727 |
+
"image-classification",
|
728 |
+
],
|
729 |
+
label="Task Type",
|
730 |
+
value="text-classification",
|
731 |
+
)
|
732 |
+
|
733 |
+
project_name = gr.Textbox(
|
734 |
+
label="Project Name", placeholder="my-training-project"
|
735 |
+
)
|
736 |
+
|
737 |
+
base_model = gr.Textbox(
|
738 |
+
label="Base Model", placeholder="distilbert-base-uncased"
|
739 |
+
)
|
740 |
+
|
741 |
+
dataset_path = gr.Textbox(label="Dataset Path", placeholder="imdb")
|
742 |
+
|
743 |
+
with gr.Column():
|
744 |
+
epochs = gr.Slider(1, 20, value=3, step=1, label="Epochs")
|
745 |
+
batch_size = gr.Slider(1, 128, value=16, step=1, label="Batch Size")
|
746 |
+
learning_rate = gr.Number(value=2e-5, label="Learning Rate")
|
747 |
+
backend = gr.Dropdown(
|
748 |
+
choices=["local", "spaces-t4-small", "spaces-a10g-large"],
|
749 |
+
label="Backend",
|
750 |
+
value="local",
|
751 |
+
)
|
752 |
+
|
753 |
+
submit_btn = gr.Button("🚀 Start Training", variant="primary", size="lg")
|
754 |
+
submit_output = gr.Textbox(label="Status", interactive=False, lines=10)
|
755 |
+
|
756 |
+
# MCP Info Tab
|
757 |
+
with gr.Tab("🔗 MCP Integration"):
|
758 |
+
gr.Markdown(f"""
|
759 |
+
## MCP Server Information
|
760 |
+
|
761 |
+
This Gradio app automatically serves as an MCP server.
|
762 |
+
|
763 |
+
**MCP Endpoint:** `http://localhost:7860/gradio_api/mcp/sse`
|
764 |
+
**MCP Schema:** `http://localhost:7860/gradio_api/mcp/schema`
|
765 |
+
|
766 |
+
### Available MCP Tools:
|
767 |
+
|
768 |
+
- `start_training_job` - Submit new training jobs
|
769 |
+
- `get_training_runs` - List all runs with status
|
770 |
+
- `get_run_details` - Get detailed run information
|
771 |
+
- `delete_training_run` - Delete training runs
|
772 |
+
- `get_task_recommendations` - Get training recommendations
|
773 |
+
- `get_system_status` - Check system status
|
774 |
+
|
775 |
+
### Claude Desktop Configuration:
|
776 |
+
|
777 |
+
```json
|
778 |
+
{{
|
779 |
+
"mcpServers": {{
|
780 |
+
"autotrain": {{
|
781 |
+
"url": "http://localhost:7860/gradio_api/mcp/sse"
|
782 |
+
}}
|
783 |
+
}}
|
784 |
+
}}
|
785 |
+
```
|
786 |
+
|
787 |
+
### Current Stats:
|
788 |
+
|
789 |
+
Total Runs: {len(load_runs())}
|
790 |
+
W&B Project: {WANDB_PROJECT}
|
791 |
+
""")
|
792 |
+
|
793 |
+
# MCP Tools Tab
|
794 |
+
with gr.Tab("🔧 MCP Tools"):
|
795 |
+
gr.Markdown("## MCP Tool Testing Interface")
|
796 |
+
gr.Markdown("These tools are exposed via MCP for Claude Desktop")
|
797 |
+
|
798 |
+
gr.Interface(
|
799 |
+
fn=get_system_status,
|
800 |
+
inputs=[],
|
801 |
+
outputs=gr.Textbox(label="System Status"),
|
802 |
+
title="get_system_status",
|
803 |
+
description="Get AutoTrain system status and capabilities",
|
804 |
+
)
|
805 |
+
|
806 |
+
gr.Interface(
|
807 |
+
fn=get_training_runs,
|
808 |
+
inputs=[
|
809 |
+
gr.Textbox(label="limit", value="20"),
|
810 |
+
gr.Textbox(label="status", value=""),
|
811 |
+
],
|
812 |
+
outputs=gr.Textbox(label="Training Runs"),
|
813 |
+
title="get_training_runs",
|
814 |
+
description="Get list of training runs with status",
|
815 |
+
)
|
816 |
+
|
817 |
+
gr.Interface(
|
818 |
+
fn=start_training_job,
|
819 |
+
inputs=[
|
820 |
+
gr.Textbox(label="task", value="text-classification"),
|
821 |
+
gr.Textbox(label="project_name", value="test-project"),
|
822 |
+
gr.Textbox(label="base_model", value="distilbert-base-uncased"),
|
823 |
+
gr.Textbox(label="dataset_path", value="imdb"),
|
824 |
+
gr.Textbox(label="epochs", value="1"),
|
825 |
+
gr.Textbox(label="batch_size", value="8"),
|
826 |
+
gr.Textbox(label="learning_rate", value="2e-5"),
|
827 |
+
gr.Textbox(label="backend", value="local"),
|
828 |
+
],
|
829 |
+
outputs=gr.Textbox(label="Training Job Result"),
|
830 |
+
title="start_training_job",
|
831 |
+
description="Start a new AutoTrain training job",
|
832 |
+
)
|
833 |
+
|
834 |
+
gr.Interface(
|
835 |
+
fn=get_run_details,
|
836 |
+
inputs=gr.Textbox(
|
837 |
+
label="run_id", placeholder="Enter run ID or first 8 chars"
|
838 |
+
),
|
839 |
+
outputs=gr.Textbox(label="Run Details"),
|
840 |
+
title="get_run_details",
|
841 |
+
description="Get detailed information about a training run",
|
842 |
+
)
|
843 |
+
|
844 |
+
gr.Interface(
|
845 |
+
fn=get_task_recommendations,
|
846 |
+
inputs=[
|
847 |
+
gr.Textbox(label="task", value="text-classification"),
|
848 |
+
gr.Textbox(label="dataset_size", value="medium"),
|
849 |
+
],
|
850 |
+
outputs=gr.Textbox(label="Recommendations"),
|
851 |
+
title="get_task_recommendations",
|
852 |
+
description="Get training recommendations for a task",
|
853 |
+
)
|
854 |
+
|
855 |
+
# Event handlers with proper function names (not lambda)
|
856 |
+
def refresh_data():
|
857 |
+
return fetch_runs_for_ui(), get_system_status()
|
858 |
+
|
859 |
+
def load_initial_data():
|
860 |
+
return fetch_runs_for_ui(), get_system_status()
|
861 |
+
|
862 |
+
refresh_btn.click(
|
863 |
+
fn=refresh_data,
|
864 |
+
outputs=[runs_table, stats],
|
865 |
+
)
|
866 |
+
|
867 |
+
submit_btn.click(
|
868 |
+
fn=submit_training_job_ui,
|
869 |
+
inputs=[
|
870 |
+
task_dropdown,
|
871 |
+
project_name,
|
872 |
+
base_model,
|
873 |
+
dataset_path,
|
874 |
+
epochs,
|
875 |
+
batch_size,
|
876 |
+
learning_rate,
|
877 |
+
backend,
|
878 |
+
],
|
879 |
+
outputs=[submit_output, runs_table],
|
880 |
+
)
|
881 |
+
|
882 |
+
# Load initial data
|
883 |
+
app.load(
|
884 |
+
fn=load_initial_data,
|
885 |
+
outputs=[runs_table, stats],
|
886 |
+
)
|
887 |
+
|
888 |
+
|
889 |
+
# Helper to find an available port
|
890 |
+
def _find_available_port(start_port: int = 7860, max_tries: int = 20) -> int:
|
891 |
+
"""Return the first available port starting from `start_port`."""
|
892 |
+
port = start_port
|
893 |
+
for _ in range(max_tries):
|
894 |
+
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
|
895 |
+
try:
|
896 |
+
s.bind(("0.0.0.0", port))
|
897 |
+
return port # Port is free
|
898 |
+
except OSError:
|
899 |
+
port += 1 # Try next port
|
900 |
+
# If no port found, let OS pick one
|
901 |
+
return 0
|
902 |
+
|
903 |
+
|
904 |
+
if __name__ == "__main__":
|
905 |
+
chosen_port = int(os.environ.get("GRADIO_SERVER_PORT", "7860"))
|
906 |
+
try:
|
907 |
+
chosen_port = _find_available_port(chosen_port)
|
908 |
+
except Exception:
|
909 |
+
# Fallback to OS-assigned port if something goes wrong
|
910 |
+
chosen_port = 0
|
911 |
+
|
912 |
+
app.launch(
|
913 |
+
server_name="0.0.0.0",
|
914 |
+
server_port=chosen_port,
|
915 |
+
mcp_server=True, # Enable MCP server functionality
|
916 |
+
)
|
pyproject.toml
ADDED
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[project]
|
2 |
+
name = "autotrain-gradio-mcp"
|
3 |
+
version = "0.1.0"
|
4 |
+
description = "AutoTrain Gradio MCP Server - All-in-One Solution"
|
5 |
+
authors = [
|
6 |
+
{name = "AutoTrain Team", email = "autotrain@example.com"}
|
7 |
+
]
|
8 |
+
readme = "README.md"
|
9 |
+
requires-python = ">=3.10"
|
10 |
+
dependencies = [
|
11 |
+
# Core dependencies
|
12 |
+
"gradio[mcp]>=5.0.0",
|
13 |
+
"autotrain-advanced>=0.8.0",
|
14 |
+
"pandas>=2.0.0",
|
15 |
+
"wandb>=0.16.0",
|
16 |
+
|
17 |
+
# MCP and async support
|
18 |
+
"httpx>=0.25.0",
|
19 |
+
"aiofiles>=23.0.0",
|
20 |
+
|
21 |
+
# Data handling
|
22 |
+
"datasets>=2.0.0",
|
23 |
+
"torch>=2.0.0",
|
24 |
+
"transformers>=4.30.0",
|
25 |
+
|
26 |
+
# Optional ML frameworks
|
27 |
+
"accelerate>=0.20.0",
|
28 |
+
"peft>=0.4.0",
|
29 |
+
"bitsandbytes>=0.41.0",
|
30 |
+
]
|
31 |
+
|
32 |
+
[project.optional-dependencies]
|
33 |
+
dev = [
|
34 |
+
"pytest>=7.0.0",
|
35 |
+
"black>=23.0.0",
|
36 |
+
"flake8>=6.0.0",
|
37 |
+
"mypy>=1.0.0",
|
38 |
+
]
|
39 |
+
|
40 |
+
[build-system]
|
41 |
+
requires = ["setuptools>=65.0", "wheel"]
|
42 |
+
build-backend = "setuptools.build_meta"
|
43 |
+
|
44 |
+
[project.scripts]
|
45 |
+
autotrain-gradio = "autotrain_gradio:main"
|
46 |
+
|
47 |
+
[tool.black]
|
48 |
+
line-length = 88
|
49 |
+
target-version = ['py310']
|
50 |
+
|
51 |
+
[tool.mypy]
|
52 |
+
python_version = "3.10"
|
53 |
+
warn_return_any = true
|
54 |
+
warn_unused_configs = true
|
requirements.txt
ADDED
@@ -0,0 +1,182 @@
|
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|
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|
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|
|
|
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|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# This file was autogenerated by uv via the following command:
|
2 |
+
# uv export --format requirements-txt --no-hashes
|
3 |
+
-e .
|
4 |
+
absl-py==2.3.0
|
5 |
+
accelerate==1.2.1
|
6 |
+
aiofiles==23.2.1
|
7 |
+
aiohappyeyeballs==2.6.1
|
8 |
+
aiohttp==3.12.9
|
9 |
+
aiosignal==1.3.2
|
10 |
+
albucore==0.0.21
|
11 |
+
albumentations==1.4.23
|
12 |
+
alembic==1.16.1
|
13 |
+
annotated-types==0.7.0
|
14 |
+
anyio==4.9.0
|
15 |
+
async-timeout==5.0.1 ; python_full_version < '3.11'
|
16 |
+
attrs==25.3.0
|
17 |
+
audioop-lts==0.2.1 ; python_full_version >= '3.13'
|
18 |
+
authlib==1.4.0
|
19 |
+
bitsandbytes==0.45.0
|
20 |
+
brotli==1.1.0 ; platform_python_implementation == 'CPython'
|
21 |
+
brotlicffi==1.1.0.0 ; platform_python_implementation == 'PyPy'
|
22 |
+
cachetools==6.0.0
|
23 |
+
certifi==2025.4.26
|
24 |
+
cffi==1.17.1
|
25 |
+
charset-normalizer==3.4.2
|
26 |
+
click==8.2.1
|
27 |
+
colorama==0.4.6 ; sys_platform == 'win32' or platform_system == 'Windows'
|
28 |
+
colorlog==6.9.0
|
29 |
+
contourpy==1.3.2
|
30 |
+
cryptography==44.0.0
|
31 |
+
cycler==0.12.1
|
32 |
+
datasets==3.2.0
|
33 |
+
dill==0.3.8
|
34 |
+
einops==0.8.0
|
35 |
+
eval-type-backport==0.2.2
|
36 |
+
evaluate==0.4.3
|
37 |
+
exceptiongroup==1.3.0 ; python_full_version < '3.11'
|
38 |
+
fastapi==0.115.6
|
39 |
+
ffmpy==0.6.0
|
40 |
+
filelock==3.18.0
|
41 |
+
fonttools==4.58.1
|
42 |
+
frozenlist==1.6.2
|
43 |
+
fsspec==2024.9.0
|
44 |
+
gitdb==4.0.12
|
45 |
+
gitpython==3.1.44
|
46 |
+
gradio>=5.33.0
|
47 |
+
gradio-client==1.7.0
|
48 |
+
greenlet==3.2.3 ; (python_full_version < '3.14' and platform_machine == 'AMD64') or (python_full_version < '3.14' and platform_machine == 'WIN32') or (python_full_version < '3.14' and platform_machine == 'aarch64') or (python_full_version < '3.14' and platform_machine == 'amd64') or (python_full_version < '3.14' and platform_machine == 'ppc64le') or (python_full_version < '3.14' and platform_machine == 'win32') or (python_full_version < '3.14' and platform_machine == 'x86_64')
|
49 |
+
grpcio==1.72.1
|
50 |
+
h11==0.16.0
|
51 |
+
hf-transfer==0.1.9
|
52 |
+
httpcore==1.0.9
|
53 |
+
httpx==0.28.1
|
54 |
+
huggingface-hub==0.27.0
|
55 |
+
idna==3.10
|
56 |
+
inflate64==1.0.3
|
57 |
+
ipadic==1.0.0
|
58 |
+
itsdangerous==2.2.0
|
59 |
+
jinja2==3.1.6
|
60 |
+
jiwer==3.0.5
|
61 |
+
joblib==1.4.2
|
62 |
+
kiwisolver==1.4.8
|
63 |
+
lightning-utilities==0.14.3
|
64 |
+
loguru==0.7.3
|
65 |
+
mako==1.3.10
|
66 |
+
markdown==3.8
|
67 |
+
markdown-it-py==3.0.0
|
68 |
+
markupsafe==2.1.5
|
69 |
+
matplotlib==3.10.3
|
70 |
+
mdurl==0.1.2
|
71 |
+
mpmath==1.3.0
|
72 |
+
multidict==6.4.4
|
73 |
+
multiprocess==0.70.16
|
74 |
+
multivolumefile==0.2.3
|
75 |
+
networkx==3.4.2 ; python_full_version < '3.11'
|
76 |
+
networkx==3.5 ; python_full_version >= '3.11'
|
77 |
+
nltk==3.9.1
|
78 |
+
numpy==2.2.6
|
79 |
+
nvidia-cublas-cu12==12.6.4.1 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
80 |
+
nvidia-cuda-cupti-cu12==12.6.80 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
81 |
+
nvidia-cuda-nvrtc-cu12==12.6.77 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
82 |
+
nvidia-cuda-runtime-cu12==12.6.77 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
83 |
+
nvidia-cudnn-cu12==9.5.1.17 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
84 |
+
nvidia-cufft-cu12==11.3.0.4 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
85 |
+
nvidia-cufile-cu12==1.11.1.6 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
86 |
+
nvidia-curand-cu12==10.3.7.77 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
87 |
+
nvidia-cusolver-cu12==11.7.1.2 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
88 |
+
nvidia-cusparse-cu12==12.5.4.2 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
89 |
+
nvidia-cusparselt-cu12==0.6.3 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
90 |
+
nvidia-ml-py==12.535.161
|
91 |
+
nvidia-nccl-cu12==2.26.2 ; platform_machine != 'aarch64' and platform_system == 'Linux'
|
92 |
+
nvidia-nvjitlink-cu12==12.6.85 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
93 |
+
nvidia-nvtx-cu12==12.6.77 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
94 |
+
nvitop==1.3.2
|
95 |
+
opencv-python-headless==4.11.0.86
|
96 |
+
optuna==4.1.0
|
97 |
+
orjson==3.10.18
|
98 |
+
packaging==24.2
|
99 |
+
pandas==2.2.3
|
100 |
+
peft==0.14.0
|
101 |
+
pillow==11.0.0
|
102 |
+
platformdirs==4.3.8
|
103 |
+
propcache==0.3.1
|
104 |
+
protobuf==6.31.1
|
105 |
+
psutil==7.0.0
|
106 |
+
py7zr==0.22.0
|
107 |
+
pyarrow==20.0.0
|
108 |
+
pybcj==1.0.6
|
109 |
+
pycocotools==2.0.8
|
110 |
+
pycparser==2.22
|
111 |
+
pycryptodomex==3.23.0
|
112 |
+
pydantic==2.10.4
|
113 |
+
pydantic-core==2.27.2
|
114 |
+
pydub==0.25.1
|
115 |
+
pygments==2.19.1
|
116 |
+
pyngrok==7.2.1
|
117 |
+
pyparsing==3.2.3
|
118 |
+
pyppmd==1.1.1
|
119 |
+
python-dateutil==2.9.0.post0
|
120 |
+
python-multipart==0.0.20
|
121 |
+
pytz==2025.2
|
122 |
+
pyyaml==6.0.2
|
123 |
+
pyzstd==0.17.0
|
124 |
+
rapidfuzz==3.13.0
|
125 |
+
regex==2024.11.6
|
126 |
+
requests==2.32.3
|
127 |
+
rich==14.0.0
|
128 |
+
rouge-score==0.1.2
|
129 |
+
ruff==0.11.13 ; sys_platform != 'emscripten'
|
130 |
+
sacremoses==0.1.1
|
131 |
+
safehttpx==0.1.6
|
132 |
+
safetensors==0.5.3
|
133 |
+
scikit-learn==1.6.0
|
134 |
+
scipy==1.15.3
|
135 |
+
semantic-version==2.10.0
|
136 |
+
sentence-transformers==3.3.1
|
137 |
+
sentencepiece==0.2.0
|
138 |
+
sentry-sdk==2.29.1
|
139 |
+
seqeval==1.2.2
|
140 |
+
setproctitle==1.3.6
|
141 |
+
setuptools==80.9.0
|
142 |
+
shellingham==1.5.4 ; sys_platform != 'emscripten'
|
143 |
+
simsimd==6.4.7
|
144 |
+
six==1.17.0
|
145 |
+
smmap==5.0.2
|
146 |
+
sniffio==1.3.1
|
147 |
+
sqlalchemy==2.0.41
|
148 |
+
starlette==0.41.3
|
149 |
+
stringzilla==3.12.5
|
150 |
+
sympy==1.14.0
|
151 |
+
tensorboard==2.18.0
|
152 |
+
tensorboard-data-server==0.7.2
|
153 |
+
termcolor==3.1.0
|
154 |
+
texttable==1.7.0
|
155 |
+
threadpoolctl==3.6.0
|
156 |
+
tiktoken==0.8.0
|
157 |
+
timm==1.0.12
|
158 |
+
tokenizers==0.21.1
|
159 |
+
tomli==2.2.1 ; python_full_version < '3.11'
|
160 |
+
tomlkit==0.13.3
|
161 |
+
torch==2.7.1
|
162 |
+
torchmetrics==1.6.0
|
163 |
+
torchvision==0.22.1
|
164 |
+
tqdm==4.67.1
|
165 |
+
transformers==4.48.0
|
166 |
+
triton==3.3.1 ; platform_machine == 'x86_64' and platform_system == 'Linux'
|
167 |
+
trl==0.13.0
|
168 |
+
typer==0.16.0 ; sys_platform != 'emscripten'
|
169 |
+
typing-extensions==4.14.0
|
170 |
+
tzdata==2025.2
|
171 |
+
urllib3==2.4.0
|
172 |
+
uvicorn==0.34.0
|
173 |
+
wandb==0.20.1
|
174 |
+
websockets==14.2
|
175 |
+
werkzeug==3.1.3
|
176 |
+
win32-setctime==1.2.0 ; sys_platform == 'win32'
|
177 |
+
windows-curses==2.4.1 ; platform_system == 'Windows'
|
178 |
+
xgboost==2.1.3
|
179 |
+
xxhash==3.5.0
|
180 |
+
yarl==1.20.0
|
181 |
+
git+https://github.com/huggingface/autotrain-advanced.git
|
182 |
+
|
uv.lock
ADDED
The diff for this file is too large to render.
See raw diff
|
|