---
id: deadline-job-attachments
version: "0.1.3"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# deadline-job-attachments — Library that enables file transfers between workstations and AWS Deadline Cloud using Amazon S3 buckets as content-addressed storage.

License: permissive · Maintenance: active · Downloads: 91.4K/mo

## What it is and what it does

This library handles file transfers between your local workstations and AWS Deadline Cloud by leveraging Amazon S3 as a content-addressed storage backend. Instead of uploading the same file multiple times, the library tracks file content and uploads only new or modified files, reducing bandwidth and storage costs. It abstracts away the complexity of managing S3 uploads and retrievals, presenting a simpler interface for job attachment workflows.

The package is designed for developers building applications on AWS Deadline Cloud who need to reliably move job-related files (assets, scripts, dependencies) to and from the cloud. It runs on Python 3.8 through 3.14 across Linux, Windows, and macOS, and depends on boto3 for AWS integration, psutil for system monitoring, and xxhash for content hashing.

Use it for:

- Upload job assets and dependencies to AWS Deadline Cloud before submitting rendering or compute jobs
- Retrieve completed job outputs and results from cloud storage back to local workstations
- Manage file versioning and deduplication in S3 without manually tracking content hashes
- Integrate file transfer logic into custom AWS Deadline Cloud job submission pipelines

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Transfers files between workstations and AWS Deadline Cloud using Amazon S3 as content-addressed storage, avoiding redundant uploads of identical files.

Yes, if you are building on AWS Deadline Cloud and need to move files between workstations and the cloud. The library is actively maintained, has no known vulnerabilities, low install friction, and permissive licensing. It is still in initial development (version 0.1.3), so expect potential API changes in minor version bumps; review the changelog before upgrading in production.

## Install

pip install deadline-job-attachments
uv add deadline-job-attachments
poetry add deadline-job-attachments

## Installing deadline-job-attachments

Before you install: Low friction install with five runtime dependencies (boto3, psutil, pywin32, typing-extensions, xxhash). Active maintenance with a release 30 days ago; repository is current and not archived.

License in practice: Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions; suitable for most deployment contexts.

Quickstart:

pip install deadline-job-attachments

import deadline_job_attachments
# Use the library to configure and manage file transfers to AWS Deadline Cloud

Requires Python 3.8 through 3.14 and a Linux, Windows, or macOS operating system; AWS credentials and an S3 bucket configured in your AWS account are needed for actual file transfers.

Verify before relying:

- Whether the library provides a high-level API or requires low-level S3 management
- Performance characteristics and file size limits for typical job attachment workflows
- Integration requirements with existing AWS Deadline Cloud job submission tools

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 91.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

AWS Deadline Cloud file transfer, job attachments S3 storage, content-addressed file upload, AWS Deadline job files, S3 file synchronization, Deadline Cloud integration, workstation to cloud file transfer, aws-integration, file-transfer, content-addressing

[View on SkillFed](https://skillfed.io/packages/deadline-job-attachments) · [View on PyPI](https://pypi.org/project/deadline-job-attachments/)