Skip to main content

doppler-colab

Seamlessly and securely inject Doppler secrets into Google Colab interactive environments.

When working in ephemeral environments like Google Colab, managing secrets securely is a challenge. doppler-colab natively integrates with Colab's built-in Secrets management to securely fetch your environment variables from the Doppler API and inject them silently into os.environ.

No CLI dependencies, no leaking print statements, and downstream libraries instantly work.

Release

Installation

Install the package directly inside your Colab notebook:

!pip install -q doppler-colab

Setup

  1. Generate a Service Token inside your Doppler dashboard. (Service tokens enforce scoping and ensure you safely access the correct environment).
  2. Open your Google Colab notebook.
  3. Click the 🔑 Secrets icon on the left sidebar.
  4. Add a new secret with the name DOPPLER_TOKEN and paste in your Service Token (dp.st...).
  5. Ensure the "Notebook access" toggle is explicitly switched ON next to the token, enabling read-access for your environment.

(Fallback: If you are not in Colab, or are on Colab Enterprise the package will automatically check os.environ["DOPPLER_TOKEN"] as a fallback).

Usage

Method 1: Python API

Invoke the package manually to fetch and load your secrets:

import doppler_colab
doppler_colab.load()

# ✅ Successfully injected 14 secrets from Doppler [Project: your-project] into the environment.

Method 2: IPython Cell Magic

For a cleaner interactive workflow, use the %doppler_load magic command at the top of your cells:

import doppler_colab
%doppler_load

Secure by Default

  • Silent Payloads: doppler-colab will never print the returned payload or tokens. You only receive a safe confirmation of the number of imported parameters.
  • Service Token Enforcement: doppler-colab enforces the use of scoped Service Tokens (dp.st.*). Personal tokens, CLI tokens, and other token types are rejected with a clear error message.

Disclaimer & Acknowledgements

Please note: This is a community-driven project and is not an official Doppler product, nor is it officially endorsed by Doppler.

This refactoring and adaptation to Google Colab would not have been possible without the foundational work of the original authors at Doppler on the python-doppler-env package.

For bug reports or feature requests specifically related to this Colab adaptation, please create an issue on this repository.

Release files for doppler-colab 0.4.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for doppler-colab 0.4.1
File Size Uploaded
doppler_colab-0.4.1.tar.gz 16.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for doppler-colab 0.4.1
File Interpreter ABI Platform
doppler_colab-0.4.1-py3-none-any.whl Python 3 none any Details

Total release size: 29.0 kB

Release files / doppler_colab-0.4.1.tar.gz

Download URL doppler_colab-0.4.1.tar.gz
Size 16.6 kB
Tags Source
SHA-256 checksum
How to use checksums
a0e9ffe4c8c8ecfebf2e650ce293106761f7af5afc9c393e175874f9554f7b72
BLAKE2b-256 checksum
How to use checksums
2e62280b8eba5d894abd8dccbf2197c0e71b7c428fe6c932cd702ec66c88412c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Apr 3, 2026.

Transparency log

Release files / doppler_colab-0.4.1-py3-none-any.whl

Download URL doppler_colab-0.4.1-py3-none-any.whl
Size 12.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
afd334121e434c02b496129c559793ca16c359df5b2d23c6f9970cbd9cdcfdf8
BLAKE2b-256 checksum
How to use checksums
0fd20f99fbf3c8055a52796efa82f410f58684c22d1491968051e7cdefa4a749
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Apr 3, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.4.1 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page