s3c

s3contents

A S3 backed ContentsManager implementation for Jupyter

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S3Contents

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An S3 and GCS backed ContentsManager implementation for Jupyter.

It aims to a be a transparent, drop-in replacement for Jupyter standard filesystem-backed storage system. With this implementation of a Jupyter Contents Manager you can save all your notebooks, regular files, directories structure directly to a S3/GCS bucket, this could be on AWS/GCP or a self hosted S3 API compatible like minio.

Prerequisites

Write access (valid credentials) to an S3/GCS bucket, this could be on AWS/GCP or a self hosted S3 like minio.

Installation

pip install s3contents

Jupyter config

Edit ~/.jupyter/jupyter_notebook_config.py based on the backend you want to based on the examples below. Replace credentials as needed.

AWS S3

from s3contents import S3ContentsManager

c = get_config()

# Tell Jupyter to use S3ContentsManager for all storage.
c.NotebookApp.contents_manager_class = S3ContentsManager
c.S3ContentsManager.access_key_id = "{{ AWS Access Key ID / IAM Access Key ID }}"
c.S3ContentsManager.secret_access_key = "{{ AWS Secret Access Key / IAM Secret Access Key }}"
c.S3ContentsManager.session_token = "{{ AWS Session Token / IAM Session Token }}"
c.S3ContentsManager.bucket = "{{ S3 bucket name }}"

# Optional settings:
c.S3ContentsManager.prefix = "this/is/a/prefix/on/the/s3/bucket"
c.S3ContentsManager.sse = "AES256"
c.S3ContentsManager.signature_version = "s3v4"
c.S3ContentsManager.init_s3_hook = init_function  # See AWS key refresh

Example for play.minio.io:9000:

from s3contents import S3ContentsManager

c = get_config()

# Tell Jupyter to use S3ContentsManager for all storage.
c.NotebookApp.contents_manager_class = S3ContentsManager
c.S3ContentsManager.access_key_id = "Q3AM3UQ867SPQQA43P2F"
c.S3ContentsManager.secret_access_key = "zuf+tfteSlswRu7BJ86wekitnifILbZam1KYY3TG"
c.S3ContentsManager.endpoint_url = "http://play.minio.io:9000"
c.S3ContentsManager.bucket = "s3contents-demo"
c.S3ContentsManager.prefix = "notebooks/test"

AWS EC2 role auth setup

It also possible to use IAM Role-based access to the S3 bucket from an Amazon EC2 instance.

To do that just leave access_key_id and secret_access_key set to their default values (None), and ensure that the EC2 instance has an IAM role which provides sufficient permissions for the bucket and the operations necessary.

AWS key refresh

The optional init_s3_hook configuration can be used to enable AWS key rotation (described here and here) as follows:

from s3contents import S3ContentsManager
from botocore.credentials import RefreshableCredentials
from botocore.session import get_session
import botocore
import boto3
from configparser import ConfigParser

def refresh_external_credentials():
    config = ConfigParser()
    config.read('/home/jovyan/.aws/credentials')
    return {
        "access_key": config['default']['aws_access_key_id'],
        "secret_key": config['default']['aws_secret_access_key'],
        "token": config['default']['aws_session_token'],
        "expiry_time": config['default']['aws_expiration']
    }

session_credentials = RefreshableCredentials.create_from_metadata(
        metadata = refresh_external_credentials(),
        refresh_using = refresh_external_credentials,
        method = 'custom-refreshing-key-file-reader'
)

def make_key_refresh_boto3(this_s3contents_instance):
    refresh_session =  get_session() # from botocore.session
    refresh_session._credentials = session_credentials
    my_s3_session =  boto3.Session(botocore_session=refresh_session)
    this_s3contents_instance.boto3_session = my_s3_session

# Tell Jupyter to use S3ContentsManager for all storage.
c.NotebookApp.contents_manager_class = S3ContentsManager

c.S3ContentsManager.init_s3_hook = make_key_refresh_boto3

GCP Cloud Storage

from s3contents import GCSContentsManager

c = get_config(

c.NotebookApp.contents_manager_class = GCSContentsManager
c.GCSContentsManager.project = "{{ your-project }}"
c.GCSContentsManager.token = "~/.config/gcloud/application_default_credentials.json"
c.GCSContentsManager.bucket = "{{ GCP bucket name }}"

Note that the file ~/.config/gcloud/application_default_credentials.json assumes a posix system when you did gcloud init

Access local files

To access local file as well as remote files in S3 you can use hybridcontents.

First install it:

pip install hybridcontents

Use a configuration similar to this:

from s3contents import S3ContentsManager
from hybridcontents import HybridContentsManager
from notebook.services.contents.largefilemanager import LargeFileManager

c = get_config()

c.NotebookApp.contents_manager_class = HybridContentsManager

c.HybridContentsManager.manager_classes = {
    # Associate the root directory with an S3ContentsManager.
    # This manager will receive all requests that don"t fall under any of the
    # other managers.
    "": S3ContentsManager,
    # Associate /directory with a LargeFileManager.
    "local_directory": LargeFileManager,
}

c.HybridContentsManager.manager_kwargs = {
    # Args for root S3ContentsManager.
    "": {
        "access_key_id": "{{ AWS Access Key ID / IAM Access Key ID }}",
        "secret_access_key": "{{ AWS Secret Access Key / IAM Secret Access Key }}",
        "bucket": "{{ S3 bucket name }}",
    },
    # Args for the LargeFileManager mapped to /directory
    "local_directory": {
        "root_dir": "/Users/danielfrg/Downloads",
    },
}

File Save Hooks

If you want to use pre/post file save hooks here are some examples.

A pre_save_hook is written in the exact same way as normal, operating on the file in local storage before commiting it to the object store.

def scrub_output_pre_save(model, **kwargs):
    """scrub output before saving notebooks"""
    # only run on notebooks
    if model["type"] != "notebook":
        return
    # only run on nbformat v4
    if model["content"]["nbformat"] != 4:
        return

    for cell in model["content"]["cells"]:
        if cell["cell_type"] != "code":
            continue
        cell["outputs"] = []
        cell["execution_count"] = None

c.S3ContentsManager.pre_save_hook = scrub_output_pre_save

A post_save_hook instead operates on the file in object storage, because of this it is useful to use the file methods on the contents_manager for data manipulation. In addition, one must use the following function signature (unique to s3contents):

def make_html_post_save(model, s3_path, contents_manager, **kwargs):
    """
    convert notebooks to HTML after saving via nbconvert
    """
    from nbconvert import HTMLExporter

    if model["type"] != "notebook":
        return

    content, _format = contents_manager.fs.read(s3_path, format="text")
    my_notebook = nbformat.reads(content, as_version=4)

    html_exporter = HTMLExporter()
    html_exporter.template_name = "classic"

    (body, resources) = html_exporter.from_notebook_node(my_notebook)

    base, ext = os.path.splitext(s3_path)
    contents_manager.fs.write(path=(base + ".html"), content=body, format=_format)

c.S3ContentsManager.post_save_hook = make_html_post_save

Notes

While there are some implementations of this already: (s3nb or s3drive), I wasn't able to make them work in newer versions of Jupyter Notebook. This aims to be a more tested version and it's based on PGContents.

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