Professional training: asset library using Django

Summary

A asset library blueprint for professional training built with Django on Ample. Domain schema: course_groups (certification, cohort, start_at, end_at): certification cohorts; memberships (course_group_id, participant_email, employer, role): participants and trainers; sessions (course_group_id, starts_at, module, location_or_link): training sessions; materials (course_group_id, module, object_key, visibility): course materials and exam guides; uploads (id, owner_reference, object_key, content_type, size, uploaded_at): validated uploads linked to their owner. Public information: certification catalog, cohort dates, trainer credentials. Kept out of scope until handling is reviewed: exam results, employer billing, participant identity. Exam results and certifications are personal records; only cohort logistics and materials are modeled. Technical basis verified on Django: content-type and size validation that rejects disallowed files, an object written to a private bucket and a row linking the record to the object key (reject=ok upload=ok linked=ok).

Infrastructure requirements

• Compute: verified (Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request; sizes are the priced VM sizes.)
• Postgres: verified (Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database and no DATABASE_URL is supplied.)
• S3-compatible object storage: verified (Buckets are S3-compatible with issued credentials; PutObject and GetObject are verified by canary. Other S3 operations are not verified.)

Prerequisites

• A Django project (pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template)
• A PostgreSQL driver reading DATABASE_URL (auto-provisioned when omitted)
• Bucket credentials from ample bucket create passed as encrypted S3_* environment variables
• A review of which professional training data classes may be handled at all; this blueprint models public information only

Exact tested configuration

• template: python-3.12
• runtime: python
• size: s-1vcpu-1gb
• install: python3 -m pip install --target .ample/python -r requirements.txt
• start: PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py

Steps

1. Model the professional training domain. Create the tables course_groups, memberships, sessions, materials, uploads. Certification cohorts lives in course_groups; keep the sensitive classes (exam results, employer billing, participant identity) out of this schema.

2. Workflow step 1. Model assets linked to their owning group or workspace

3. Workflow step 2. Validate uploads and store them in the private bucket; link each object to its row

4. Workflow step 3. Authorize downloads by membership

5. Workflow step 4. Deploy and verify rejection of disallowed files and a linked upload

6. Deploy. Run the synchronous deploy once and read the result. Re-running with no change is a no-op.

   ample deploy . --name <app-name> --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
   

7. Verify. Run the pattern self-test(s) from the example (/p/browser-file-uploads) and your own acceptance checks for the professional training workflow.

   ample logs <deployment_id> --kind build
   

Tested examples

• Django pattern fixture (tests/deploy-canaries/django-patterns): Verified browser file uploads basis for this blueprint.

Success checks

• app responds on its public URL (/ on the live URL, expect ample canary django patterns)
• browser-file-uploads self-test from the example (/p/browser-file-uploads on the live URL, expect see the pattern fixture checks)

Limitations

• The technical basis (browser file uploads on Django) was verified with the pattern fixture; the professional training schema and workflow are an original design for this blueprint and were not executed as a separate application.
• No health, financial, privacy or other compliance claim is made. Exam results and certifications are personal records; only cohort logistics and materials are modeled.
• Public content and synthetic examples only until actual data-handling requirements have been reviewed.
• Verified on the python-3.12 template at s-1vcpu-1gb; region, request-duration limits and other sizes are unknown or unverified.
• Managed PostgreSQL 16 only; extensions, connection limits and backup or restore procedures are not verified.
• PutObject and GetObject with path-style addressing are verified; other S3 operations and CDN cache rules are not.

Cost estimate

Estimated 10.00 USD per month (size prices from pricing.toml at build revision a1b8c38919e59cd035ebabaced73cf84ece24371).

• app server x1 s-1vcpu-1gb: 5.00 USD
• managed PostgreSQL database x1 s-1vcpu-1gb: 5.00 USD

Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.

Verification evidence

• canary_run on 2026-09-20T03:31:44Z at revision 03b6402b1b3e19178985f08ea4033804521d85d4-dirty (CLI b75e104): Django pattern fixture deployed on Ample (pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template); the browser file uploads checks passed: content-type and size validation that rejects disallowed files, an object written to a private bucket and a row linking the record to the object key (reject=ok upload=ok linked=ok). The blueprint's Professional training schema and workflow below build on that verified basis and were not separately executed. (expires 2027-03-19T03:31:44Z)

Last verified: 2026-09-20T03:31:44Z

Execution binding

MCP tool ample_deploy (registry mcp:ample_deploy), schema hash 876465fce906da0c observed 2026-09-21T03:19:59.461917+00:00 at revision 49962bcade4f, binding state current, required scopes: servers:write, databases:read, buckets:read.