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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() |