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Working with large files

If you have a very large file that is used in your Python code, it's not recommended to copy and paste the file contents directly into your code.

Instead, it's recommended to either:

  1. Create a new custom code environment that contains the file.
  2. Host the file on an external website and then fetch it in the code at runtime.

The following instructions works for all file types.

Option 1: Custom code environment

This is the recommended way to load in large files because the file will be available instantly without having to fetch it.

Step 1: Create a new custom code environment

  1. Follow these instructions on building a custom environment.
    • Add the large files in the Add files section.
  2. Once the environment is built and selected, read the file into the Python code:
with open("/home/hello.png", "r") as f:
data =

Follow the example in this video that explains how to upload an ANN .h5 file using a custom environment:

Calling functions from an uploaded Python file

You can upload a Python file to your custom environment and use the functions in your Python code! To access the functions from the Python module simply append '/home/' to sys.path and then import all (*) from your module

import sys
from pythonModule import * // import all the functions from the external Python file

def main(inputs):
result = pythonFunction() // use a function

Check out the following short video that explains exactly how to do this!

Option 2: Fetching the file

This option is useful if you do not want to create a new custom code environment, or if you want to use a file that is already hosted on the internet. However, this option will take longer to run the code because it needs to download the file from the internet.

Step 1: Hosting the file

First, you must upload your file to a file-hosting service. You may skip this step if the file is already publicly available on the internet.


Github is a very popular free-to-use website that can host your files.

  1. Go to
  2. Create an account (if you don't already have one).
  3. On the homepage, click on New in the top left corner to create a new repository.
    • Repositories are similar to folders that store your files.
    • You can have more than one repositories. on your Github account.
    • new repo
  4. Create a new repository:
    • Give your repository a name in "Repository name".
    • Select Public.
    • Finally, click on Create repository.
    • new repo
  5. Upload files:
    • If you see this screen, click on uploading an existing file.
    • click upload
    • If you see this screen, click on Add file and then Upload files.
    • click upload
  6. Upload your files:
    • Upload one or more files and then click on Commit changes to submit the files.
    • upload files
  7. Your uploaded files should now show up on the homepage of your repository.
    • Your uploaded files are viewable at this url:<username>/<repo name>/, where <username> is your Github username, and <repo name> is the name of the Gihub repository that your files are in.
    • uploaded files

Step 2: Getting the url

Before you can fetch the file, you need to know the url of the file, which must be publicly accessible. This url can be from any website (does not have to be from Github), the following instructions will use Github.


The url must only contain the file you want, and can not be a webpage that contains the file along with other assets, such as text and images.


To fetch the url of files hosted on Github:

  1. Go to the Github repository with the files.
    • uploaded files
  2. Click on the filename to go to the file. For example, clicking on excel_file.xlsx:
    • excel file
  3. Copy the url of the webpage.
    • For example,
  4. Add ?raw=true to the end of the url, which references the raw data of the file.
    • For example,

You must add ?raw=true to the end of the url for Github files

Step 3: Fetching the file

Once your file can be accessed on the web at a public url, you must first fetch the file into your Python code before you can use it. There are many different functions available depending on the type of file, that will read in a file given an url.

Text files

from urllib.request import urlopen

def main(inputs):
# Fetch file
url = ""
text_file = urlopen(url)

# Read entire file
lines ="utf-8")

return {"lines": lines}

Excel spreadsheet files

import pandas as pd

def main(inputs):
url = ""
dataframe = pd.read_excel(url)

return {"dataframe": dataframe}

Image files

from PIL import Image
from urllib.request import urlopen

def main(inputs):
img1_url = ""
img1 =

img2_url = ""
img2 =

return {"img1": img1, "img2": img2}