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Transferring programmatically between an S3 bucket and CLIMB-BIG-DATA notebook server (using python)#

Creating an environment#

In the terminal, create a new environment called s3training and activate it:

conda create --solver libmamba -y -n s3training ipykernel
conda activate s3training

Once this is complete, you should see the environment available as a notebook in the Launcher window (File > New Launcher)

conda activate s3training

Click the tile and open a new Jupyter notebook. You should see s3training in as the name of the kernel in the top right of the notebook.

conda activate s3training

Uploading and downloading a file to S3#

Let's create a test file to up load to a bucket. In the Jupyter notebook, create a new cell and add the following:

!source ~/.bashrc
!echo "Bless this mess" > s3test.txt

This creates a text file called s3test.txt with the text "Bless this mess".


The ! allows us to run a shell command in the notebook. You could also do this in the terminal directly.

We will need to boto3 and dotenv Python module which we can install in a new cell with

pip install boto3 python-dotenv

Create another cell and add the following script. This will upload the file s3test.txt to the bucket. You will need have a bucket already created in Bryn. If you don't have one, you can create one in the Bryn dashboard. The script below has MY OWN bucket 'quadram-bioinfo-training' as the BUCKET_NAME, you will need to change this to the name of YOUR bucket.

import boto3
import os 
from dotenv import load_dotenv

dotenv_path = '/home/jovyan/.s3cfg' # ~/.s3cfg is the default location for the s3 config file

BUCKET_NAME = 'quadram-bioinfo-training'

def upload_file_to_s3(file_name, bucket):
    object = s3.Object(bucket, file_name)
    object.put(Body=open(file_name, 'rb'))

def download_file_from_s3(key, bucket, output_path):
    object = s3.Object(bucket, key)
    with open(output_path, 'w') as f:

# Create a resource using your S3 credentials
s3 = boto3.resource('s3',
  endpoint_url = '',
  aws_access_key_id = os.environ.get("access_key"),
  aws_secret_access_key = os.environ.get("secret_key")

# Upload our file to the bucket 
upload_file_to_s3('s3test.txt', BUCKET_NAME)

# Let's print the output of the bucket
my_bucket = s3.Bucket(BUCKET_NAME)    
for my_bucket_object in my_bucket.objects.all():

# Let's download our file from the bucket to another file
download_file_from_s3('s3test.txt', BUCKET_NAME, 's3_backagain.txt')

This Python code uses the boto3 library to interact with CLIMB's S3, which is a cloud storage service. The code defines two functions: upload_file_to_s3() and download_file_from_s3(). The upload_file_to_s3() function uploads a file to the specified S3 bucket, while the download_file_from_s3() function downloads a file from the specified S3 bucket to a local file.

The code creates a resource object that connects to the S3 service using the specified endpoint URL and access keys. The os.environ.get() function is used to retrieve the values of the access key and secret key from the environment variables. These keys are used to authenticate the connection to the S3 service.

The code then uploads a file called s3test.txt to the specified S3 bucket using the upload_file_to_s3() function. It then prints the keys of all objects in the bucket using the my_bucket.objects.all() function, to show the file has been uploaded;.

Finally, the code downloads the s3test.txt file from the S3 bucket to a local file called s3_backagain.txt using the download_file_from_s3() function.

As the s3_backagain.txt file, is just a copy of the original s3test.txt with a different name, it should contain the text "Bless this mess". i.e.

!cat 's3_backagain.txt'

Should show

Bless this mess

Reading directly from S3#

You can read directly with the s3_client object, without having to download the file to your notebook. For example, to read the file we just uploaded:

object = s3.Object(BUCKET_NAME, 's3test.txt')

Should return

Bless this mess