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⚡ ULTRON Systems // High-Efficiency Autonomous Colab GPU Training Loop
Orchestrates:
- Month-by-month streaming dataset ingestion
- Mixed Precision (FP16) with GradScaler
- Gradient Accumulation for effective batch size
- Auto-resume from Google Drive / local checkpoints
- Real-time VRAM telemetry and directional accuracy metrics
"""
import os
import sys
import gc
import time
import signal
import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from typing import Optional
from config import DatasetConfig, ModelConfig, TrainingConfig
from dataset import StreamingShardManager
from model import OHLCVTransformer
from loss import MultiTaskFinancialLoss
class Trainer:
def __init__(self, data_cfg: DatasetConfig, model_cfg: ModelConfig, train_cfg: TrainingConfig):
self.data_cfg = data_cfg
self.model_cfg = model_cfg
self.train_cfg = train_cfg
# Setup device
self.device = torch.device(train_cfg.device)
print(f"⚡ [ULTRON] Initializing training on compute device: {self.device}")
if self.device.type == "cuda":
print(f"⚡ [ULTRON] GPU: {torch.cuda.get_device_name(0)} | VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
# Determine active checkpoint directory
if os.path.exists("/content/drive/MyDrive"):
self.checkpoint_dir = train_cfg.checkpoint_dir
print(f"⚡ [ULTRON] Google Drive detected. Checkpoints will persist to: {self.checkpoint_dir}")
else:
self.checkpoint_dir = train_cfg.local_checkpoint_dir
print(f"⚡ [ULTRON] Local mode active. Checkpoints will save to: {self.checkpoint_dir}")
os.makedirs(self.checkpoint_dir, exist_ok=True)
# Instantiate Model, Loss, Optimizer, Scaler
self.model = OHLCVTransformer(model_cfg).to(self.device)
self.criterion = MultiTaskFinancialLoss().to(self.device)
self.optimizer = torch.optim.AdamW(
self.model.parameters(),
lr=train_cfg.learning_rate,
weight_decay=train_cfg.weight_decay,
betas=(0.9, 0.98)
)
self.scaler = torch.cuda.amp.GradScaler(enabled=train_cfg.fp16 and self.device.type == "cuda")
self.global_step = 0
self.current_epoch = 0
self.best_dir_acc = 0.0
# Register graceful termination handlers
signal.signal(signal.SIGINT, self._handle_exit)
signal.signal(signal.SIGTERM, self._handle_exit)
# Attempt auto-resume
self._load_checkpoint()
def _get_lr(self, step: int) -> float:
"""Linear warmup followed by cosine decay."""
if step < self.train_cfg.warmup_steps:
return self.train_cfg.learning_rate * float(step) / float(max(1, self.train_cfg.warmup_steps))
import math
progress = float(step - self.train_cfg.warmup_steps) / float(max(1, 10000 - self.train_cfg.warmup_steps))
cosine_decay = 0.5 * (1.0 + math.cos(math.pi * min(1.0, progress)))
return max(self.train_cfg.min_lr, self.train_cfg.learning_rate * cosine_decay)
def _save_checkpoint(self, is_best: bool = False):
state = {
"global_step": self.global_step,
"epoch": self.current_epoch,
"best_dir_acc": self.best_dir_acc,
"model_state": self.model.state_dict(),
"optimizer_state": self.optimizer.state_dict(),
"scaler_state": self.scaler.state_dict(),
"model_cfg": self.model_cfg,
"train_cfg": self.train_cfg
}
latest_path = os.path.join(self.checkpoint_dir, "checkpoint_latest.pt")
torch.save(state, latest_path)
if is_best:
best_path = os.path.join(self.checkpoint_dir, "checkpoint_best.pt")
torch.save(state, best_path)
print(f"🌟 [ULTRON] Saved new BEST model checkpoint to {best_path}")
print(f"💾 [ULTRON] Checkpoint synced (Step {self.global_step}).")
def _load_checkpoint(self):
latest_path = os.path.join(self.checkpoint_dir, "checkpoint_latest.pt")
if os.path.exists(latest_path):
print(f"⚡ [ULTRON] Found existing checkpoint at {latest_path}. Resuming state...")
try:
ckpt = torch.load(latest_path, map_location=self.device)
self.model.load_state_dict(ckpt["model_state"])
self.optimizer.load_state_dict(ckpt["optimizer_state"])
if "scaler_state" in ckpt and self.scaler.is_enabled():
self.scaler.load_state_dict(ckpt["scaler_state"])
self.global_step = ckpt.get("global_step", 0)
self.current_epoch = ckpt.get("epoch", 0)
self.best_dir_acc = ckpt.get("best_dir_acc", 0.0)
print(f"✅ [ULTRON] Resumed successfully from Step {self.global_step} (Epoch {self.current_epoch}).")
except Exception as e:
print(f"⚠️ [ULTRON] Checkpoint load failed: {e}. Starting fresh.")
else:
print("⚡ [ULTRON] No previous checkpoint found. Initializing fresh weights.")
def _handle_exit(self, signum, frame):
print(f"\n⚠️ [ULTRON] Interrupt signal received ({signum}). Securing weights...")
self._save_checkpoint()
sys.exit(0)
def train(self):
print("\n" + "="*70)
print("⚡ ULTRON // CONVOLUTIONAL TRANSFORMER AUTONOMOUS TRAINING ONLINE")
print("="*70)
manager = StreamingShardManager(self.data_cfg)
self.model.train()
for epoch in range(self.current_epoch, self.train_cfg.max_epochs):
self.current_epoch = epoch
print(f"\n🚀 [ULTRON] Beginning Epoch {epoch + 1}/{self.train_cfg.max_epochs}")
# Stream monthly shards one by one
for shard_idx, shard_dataset in enumerate(manager.stream_monthly_shards()):
loader = DataLoader(
shard_dataset,
batch_size=self.train_cfg.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True if self.device.type == "cuda" else False,
drop_last=True
)
shard_loss = 0.0
shard_acc = 0.0
steps_in_shard = 0
t0 = time.time()
self.optimizer.zero_grad()
for batch_idx, (batch_x, batch_y) in enumerate(loader):
batch_x = batch_x.to(self.device, non_blocking=True)
batch_y = batch_y.to(self.device, non_blocking=True)
# Dynamic learning rate schedule
lr = self._get_lr(self.global_step)
for param_group in self.optimizer.param_groups:
param_group["lr"] = lr
# Mixed precision forward pass
with torch.cuda.amp.autocast(enabled=self.train_cfg.fp16 and self.device.type == "cuda"):
pred_returns, pred_logits = self.model(batch_x)
loss, metrics = self.criterion(pred_returns, pred_logits, batch_y)
# Scale loss for gradient accumulation
loss_scaled = loss / self.train_cfg.grad_accum_steps
# Backward pass
self.scaler.scale(loss_scaled).backward()
shard_loss += metrics["total_loss"]
shard_acc += metrics["dir_acc_bce"]
steps_in_shard += 1
# Optimizer step on accumulation boundaries
if (batch_idx + 1) % self.train_cfg.grad_accum_steps == 0:
self.scaler.unscale_(self.optimizer)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad()
self.global_step += 1
# Logging telemetry
if self.global_step % self.train_cfg.log_interval == 0:
vram_gb = torch.cuda.memory_allocated() / 1e9 if self.device.type == "cuda" else 0.0
rate = (self.train_cfg.batch_size * self.train_cfg.grad_accum_steps) / max(1e-4, (time.time() - t0))
print(
f"⚡ [Step {self.global_step:05d}] "
f"Loss: {metrics['total_loss']:.4f} "
f"(Reg: {metrics['reg_loss']:.4f}, Dir: {metrics['dir_loss']:.4f}) | "
f"Dir Acc: {metrics['dir_acc_bce']:.1f}% | "
f"LR: {lr:.2e} | "
f"VRAM: {vram_gb:.2f}GB | "
f"{rate:.1f} win/s"
)
t0 = time.time()
# Periodic checkpointing
if self.global_step % self.train_cfg.save_every_steps == 0:
avg_acc = shard_acc / max(1, steps_in_shard)
is_best = avg_acc > self.best_dir_acc
if is_best:
self.best_dir_acc = avg_acc
self._save_checkpoint(is_best=is_best)
# Cap steps per shard if configured to cycle through years faster
if steps_in_shard >= self.train_cfg.max_steps_per_shard:
break
# Clean up shard memory explicitly
del loader, shard_dataset
if self.device.type == "cuda":
torch.cuda.empty_cache()
gc.collect()
# End of epoch checkpoint
self._save_checkpoint()
print("\n✅ [ULTRON] Training loop complete. Final weights secured.")
def parse_args():
parser = argparse.ArgumentParser(description="ULTRON OHLCV Transformer Trainer")
parser.add_argument("--start_year", type=int, default=2021, help="Start year of shards")
parser.add_argument("--end_year", type=int, default=2025, help="End year of shards")
parser.add_argument("--batch_size", type=int, default=64, help="Batch size per GPU step")
parser.add_argument("--grad_accum", type=int, default=4, help="Gradient accumulation steps")
parser.add_argument("--lr", type=float, default=3e-4, help="Learning rate")
parser.add_argument("--epochs", type=int, default=5, help="Total training epochs")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
data_cfg = DatasetConfig(start_year=args.start_year, end_year=args.end_year)
model_cfg = ModelConfig()
train_cfg = TrainingConfig(
batch_size=args.batch_size,
grad_accum_steps=args.grad_accum,
learning_rate=args.lr,
max_epochs=args.epochs
)
trainer = Trainer(data_cfg, model_cfg, train_cfg)
trainer.train()
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