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Add download help message (#274)

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Sebastian Raschka 2024-07-19 06:29:29 -07:00 committed by GitHub
parent 06ed31f347
commit d0f3b034d8
6 changed files with 259 additions and 35 deletions
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45
appendix-E/01_main-chapter-code/gpt_download.py
View File

@ -5,7 +5,9 @@
import os
import requests
import urllib.request
# import requests
import json
import numpy as np
import tensorflow as tf
@ -42,6 +44,46 @@ def download_and_load_gpt2(model_size, models_dir):
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
try:
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
except urllib.error.HTTPError:
s = (
f"The specified URL ({url}) is incorrect, the internet connection cannot be established,"
"\nor the requested file is temporarily unavailable.\nPlease visit the following website"
" for help: https://github.com/rasbt/LLMs-from-scratch/discussions/273")
print(s)
# Alternative way using `requests`
"""
def download_file(url, destination):
# Send a GET request to download the file in streaming mode
response = requests.get(url, stream=True)
@ -68,6 +110,7 @@ def download_file(url, destination):
for chunk in response.iter_content(block_size):
progress_bar.update(len(chunk)) # Update progress bar
file.write(chunk) # Write the chunk to the file
"""
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):

69
ch05/01_main-chapter-code/gpt_download.py
View File

@ -44,6 +44,45 @@ def download_and_load_gpt2(model_size, models_dir):
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
try:
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
except urllib.error.HTTPError:
s = (
f"The specified URL ({url}) is incorrect, the internet connection cannot be established,"
"\nor the requested file is temporarily unavailable.\nPlease visit the following website"
" for help: https://github.com/rasbt/LLMs-from-scratch/discussions/273")
print(s)
# Alternative way using `requests`
"""
def download_file(url, destination):
# Send a GET request to download the file in streaming mode
@ -74,36 +113,6 @@ def download_file(url, destination):
"""
def download_file(url, destination):
# Send a GET request to download the file
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
# Initialize parameters dictionary with empty blocks for each layer
params = {"blocks": [{} for _ in range(settings["n_layer"])]}

45
ch06/01_main-chapter-code/gpt_download.py
View File

@ -5,7 +5,9 @@
import os
import requests
import urllib.request
# import requests
import json
import numpy as np
import tensorflow as tf
@ -42,6 +44,46 @@ def download_and_load_gpt2(model_size, models_dir):
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
try:
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
except urllib.error.HTTPError:
s = (
f"The specified URL ({url}) is incorrect, the internet connection cannot be established,"
"\nor the requested file is temporarily unavailable.\nPlease visit the following website"
" for help: https://github.com/rasbt/LLMs-from-scratch/discussions/273")
print(s)
# Alternative way using `requests`
"""
def download_file(url, destination):
# Send a GET request to download the file in streaming mode
response = requests.get(url, stream=True)
@ -68,6 +110,7 @@ def download_file(url, destination):
for chunk in response.iter_content(block_size):
progress_bar.update(len(chunk)) # Update progress bar
file.write(chunk) # Write the chunk to the file
"""
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):

45
ch06/02_bonus_additional-experiments/gpt_download.py
View File

@ -5,7 +5,9 @@
import os
import requests
import urllib.request
# import requests
import json
import numpy as np
import tensorflow as tf
@ -42,6 +44,46 @@ def download_and_load_gpt2(model_size, models_dir):
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
try:
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
except urllib.error.HTTPError:
s = (
f"The specified URL ({url}) is incorrect, the internet connection cannot be established,"
"\nor the requested file is temporarily unavailable.\nPlease visit the following website"
" for help: https://github.com/rasbt/LLMs-from-scratch/discussions/273")
print(s)
# Alternative way using `requests`
"""
def download_file(url, destination):
# Send a GET request to download the file in streaming mode
response = requests.get(url, stream=True)
@ -68,6 +110,7 @@ def download_file(url, destination):
for chunk in response.iter_content(block_size):
progress_bar.update(len(chunk)) # Update progress bar
file.write(chunk) # Write the chunk to the file
"""
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):

45
ch06/03_bonus_imdb-classification/gpt_download.py
View File

@ -5,7 +5,9 @@
import os
import requests
import urllib.request
# import requests
import json
import numpy as np
import tensorflow as tf
@ -42,6 +44,46 @@ def download_and_load_gpt2(model_size, models_dir):
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
try:
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
except urllib.error.HTTPError:
s = (
f"The specified URL ({url}) is incorrect, the internet connection cannot be established,"
"\nor the requested file is temporarily unavailable.\nPlease visit the following website"
" for help: https://github.com/rasbt/LLMs-from-scratch/discussions/273")
print(s)
# Alternative way using `requests`
"""
def download_file(url, destination):
# Send a GET request to download the file in streaming mode
response = requests.get(url, stream=True)
@ -68,6 +110,7 @@ def download_file(url, destination):
for chunk in response.iter_content(block_size):
progress_bar.update(len(chunk)) # Update progress bar
file.write(chunk) # Write the chunk to the file
"""
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):

45
ch07/01_main-chapter-code/gpt_download.py
View File

@ -5,7 +5,9 @@
import os
import requests
import urllib.request
# import requests
import json
import numpy as np
import tensorflow as tf
@ -42,6 +44,46 @@ def download_and_load_gpt2(model_size, models_dir):
return settings, params
def download_file(url, destination):
# Send a GET request to download the file
try:
with urllib.request.urlopen(url) as response:
# Get the total file size from headers, defaulting to 0 if not present
file_size = int(response.headers.get("Content-Length", 0))
# Check if file exists and has the same size
if os.path.exists(destination):
file_size_local = os.path.getsize(destination)
if file_size == file_size_local:
print(f"File already exists and is up-to-date: {destination}")
return
# Define the block size for reading the file
block_size = 1024 # 1 Kilobyte
# Initialize the progress bar with total file size
progress_bar_description = os.path.basename(url) # Extract filename from URL
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
# Open the destination file in binary write mode
with open(destination, "wb") as file:
# Read the file in chunks and write to destination
while True:
chunk = response.read(block_size)
if not chunk:
break
file.write(chunk)
progress_bar.update(len(chunk)) # Update progress bar
except urllib.error.HTTPError:
s = (
f"The specified URL ({url}) is incorrect, the internet connection cannot be established,"
"\nor the requested file is temporarily unavailable.\nPlease visit the following website"
" for help: https://github.com/rasbt/LLMs-from-scratch/discussions/273")
print(s)
# Alternative way using `requests`
"""
def download_file(url, destination):
# Send a GET request to download the file in streaming mode
response = requests.get(url, stream=True)
@ -68,6 +110,7 @@ def download_file(url, destination):
for chunk in response.iter_content(block_size):
progress_bar.update(len(chunk)) # Update progress bar
file.write(chunk) # Write the chunk to the file
"""
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):