Create utils.py

utils.py

@@ -0,0 +1,88 @@
+import boto3
+from boto3.s3.transfer import TransferConfig
+from tqdm import tqdm
+import os
+
+def upload_file_to_s3(file_path, bucket_name, s3_prefix):
+    
+
+    class ProgressPercentage(object):
+        def __init__(self, filename):
+            self._filename = filename
+            self._size = float(os.path.getsize(filename))
+            self._seen_so_far = 0
+            self._pbar = tqdm(total=self._size, unit='B', unit_scale=True, desc=f"Uploading {os.path.basename(filename)}")
+
+        def __call__(self, bytes_amount):
+            self._seen_so_far += bytes_amount
+            self._pbar.update(bytes_amount)
+
+    s3_client = boto3.client('s3')
+    file_name = os.path.basename(file_path)
+    s3_path = f"{s3_prefix}/{file_name}"
+    
+    # Configure multipart upload
+    config = TransferConfig(
+        multipart_threshold=1024 * 25,  # 25MB
+        max_concurrency=10,
+        multipart_chunksize=1024 * 25,  # 25MB
+        use_threads=True
+    )
+    
+    try:
+        s3_client.upload_file(
+            file_path, 
+            bucket_name, 
+            s3_path,
+            Config=config,
+            Callback=ProgressPercentage(file_path)
+        )
+        return f"s3://{bucket_name}/{s3_path}"
+    except Exception as e:
+        print(f"Failed to upload {file_path} to S3: {str(e)}")
+        return None
+    
+max_lr = 1e-3
+warmup_steps = 10
+max_steps = 25000
+import math
+def get_lr_lambda(current_step, warmup_steps, max_steps, max_lr):
+    """
+    Learning rate scheduler with:
+    1. Linear warmup
+    2. Cosine decay
+    3. Minimum learning rate of 10% of max_lr
+    """
+    min_lr = max_lr * 0.1  # Minimum learning rate (10% of max_lr)
+    
+    if current_step < warmup_steps:
+        # Linear warmup
+        return max_lr * (current_step + 1) / warmup_steps
+    elif current_step > max_steps:
+        # After max_steps, return minimum learning rate
+        return min_lr
+    else:
+        # Cosine decay between warmup_steps and max_steps
+        decay_ratio = (current_step - warmup_steps) / (max_steps - warmup_steps)
+        coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
+        return min_lr + coeff * (max_lr - min_lr)
+
+
+def plot_lr_schedule():
+    """
+    Helper function to visualize the learning rate schedule
+    """
+    import matplotlib.pyplot as plt
+    steps = list(range(0, max_steps + 100))
+    lrs = [get_lr_lambda(step, warmup_steps, max_steps, max_lr) for step in steps]
+    
+    plt.figure(figsize=(10, 5))
+    plt.plot(steps, lrs)
+    plt.title('Learning Rate Schedule')
+    plt.xlabel('Steps')
+    plt.ylabel('Learning Rate')
+    plt.grid(True)
+    plt.show()
+
+if __name__ == "__main__":
+    plot_lr_schedule()