### Host CSV Import Tool with Django: Structured Data Imports

Deploy a csv import tool built with Django on Ample using the bulk import staging pattern. Compute runs the app in an isolated microVM behind a public HTTPS URL, a managed PostgreSQL 16 database is auto-provisioned and injected as DATABASE_URL, a private S3-compatible bucket holds objects with credentials delivered as encrypted environment variables. Verified on Django: a CSV staged as an object, row-level validation with per-row errors recorded, and a restart-safe import keyed by import id (imported=2 rejected=1 restart=ok). Not separately tested: your import format, mapping and conflict rules; treat the csv import tool-specific behavior as your application code.

#### Summary
- **Framework**: Django  
- **Workload**: CSV import tool  
- **Release Status**: Published  
- **Support Status**: Verified  
- **Execution Status**: Ready

#### Resource Requirements
- **Compute**  
- **Postgres**  
- **S3-compatible object storage**  
  
#### Infrastructure Requirements
1. **Compute**:  
   - **Status**: Verified  
   - **Summary**: Apps run in isolated x86_64 Firecracker microVMs that auto-pause when idle and wake on request.
2. **Postgres**:  
   - **Status**: Verified  
   - **Summary**: Managed PostgreSQL 16 runs in its own microVM and is auto-provisioned when an app needs a database.
3. **S3-compatible object storage**:  
   - **Status**: Verified  
   - **Summary**: Buckets are S3-compatible with issued credentials.

#### Prerequisites
- A Django project that builds and starts with the documented commands
- A PostgreSQL driver reading DATABASE_URL at runtime
- A bucket from `ample bucket create` with its credentials passed as encrypted S3_* environment variables
- An Ample account token with `servers:write`, `databases:read`, `buckets:read`

#### Tested Configuration
- **Template**: python-3.12  
- **Runtime**: python  
- **Size**: s-1vcpu-1gb  
- **Install Command**: `python3 -m pip install --target .ample/python -r requirements.txt`  
- **Start Command**: `PYTHONPATH=.ample/python:${PYTHONPATH:-} python3 run.py`

#### Workflow Steps
1. **Build and start**  
   - Command: `pip install into .ample/python from requirements.txt, then waitress serving project.wsgi from run.py reading PORT on the python-3.12 template.`
2. **Implement the pattern on PostgreSQL**  
   - Explanation: The fixture's module implements bulk import staging.
3. **Wire object storage**  
   - Explanation: Create the bucket(s) and pass endpoint, region, bucket and keys as --env values.
4. **Deploy**  
   - Command: `ample deploy . --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=...`
5. **Verify**  
   - Command: `ample logs --kind build`

#### Examples
- **Django Pattern Fixture**: Multi-pattern Django app whose bulk import staging module was checked live.

#### Success Checks
1. **App responds on its public URL**  
   - Kind: http_get  
   - Path: `/`  
   - Expect: `ample canary django patterns`
2. **Bulk-import-staging self-test**  
   - Kind: http_get  
   - Path: `/p/bulk-import-staging`  
   - Expect: `imported=2 rejected=1 restart=ok`

#### Limitations
- Verified on the python-3.12 template at s-1vcpu-1gb with the example fixture.

#### Cost Estimate
- **Currency**: USD  
- **Monthly Amount**: 10.0  
- **Note**: Always-on monthly price of the tested sizes; buckets are allocation-priced per quota.

#### Next Actions
1. **Browse the catalog index**  
   - Method: GET  
   - Relative Path: `/v1/catalog` 
2. **Search published recipes by intent**  
   - Method: POST  
   - Relative Path: `/v1/catalog/search`
3. **Prepare a side-effect-free deployment plan**  
   - Method: POST  
   - Relative Path: `/v1/catalog/plan`
4. **Read existing agent authentication setup**  
   - Method: GET  
   - Relative Path: `/mcp/setup`
5. **Browse CSV import tool**  
   - Method: GET  
   - Relative Path: `/v1/catalog/nodes/workload:csv-import-tool`