InvenTree on Cloud Run — Lab Guide

Overview

Estimated time: 45–90 minutes

InvenTree is an open-source inventory management system — parts, stock locations, suppliers, bills of materials, and purchase and sales orders. This lab takes you through the full operational lifecycle of the InvenTree on Cloud Run module on Google Cloud: deploy it, access and verify it, run it day-to-day, observe it, diagnose common problems, and tear it down.

The lab focuses on operating the Cloud Run module and the Google Cloud platform, not on InvenTree product features. For the complete list of provisioned services and every configuration input (organised by group), see the Configuration Guide — this lab deliberately does not duplicate that detail so it stays accurate over time.

Objectives

By the end of this lab you will be able to:

Deploy the module from the RAD platform and locate the resources it provisions.
Reach the service on the URL InvenTree accepts, and verify it is healthy.
Confirm the two-container revision (web + qcluster worker) and the db-init → migrate job chain.
Perform day-2 operations — inspect, scale, update, and manage the data directory and backups.
Observe the service with Cloud Logging and Cloud Monitoring.
Diagnose and resolve the most common deployment and runtime issues.
Tear the deployment down cleanly.

Prerequisites

Services_GCP (provides the VPC, Cloud SQL, the NFS server, Artifact Registry, and shared service accounts this module depends on). You do not need to deploy this yourself first — the platform automatically detects whether it already exists in the target project and provisions it before this module if not (see Task 1).
A Google Cloud project with billing enabled.
gcloud CLI authenticated: gcloud auth login and gcloud auth application-default login.
Project Owner (or equivalent) IAM on the project.
Bringing your own project? Before the first deploy into it, the deployment confirmation dialog asks you to prove you control it (Get verification code, run the commands it shows as a project Owner, then Verify) and to give the RAD deployment service account the Owner role. A project RAD creates for you needs neither.
Advanced mode for later changes. The create form asks only for the first page of inputs (and, in a project RAD creates for you, little more than the tenant name and region). Every other input in the Configuration Guide — including the scaling, storage and version inputs in the Day-2 tasks — is changed afterwards with Update on the deployment's page after ticking Enable advanced mode, which needs a credit balance that covers the update's estimated build cost (updates never carry a module fee). On a lab environment only an administrator can use Advanced mode.
RAD platform access with permission to deploy modules into the project.

Set these shell variables once; every task below reuses them:

export PROJECT="<your-gcp-project-id>"
export REGION="us-central1"          # the region you deploy into

Task 1 — Deploy the module [Automated]

In the RAD platform, open Solutions → Solution Catalog → RAD modules, then open InvenTree (Cloud Run) from the Platform Modules list, choose Configuration Form under How would you like to configure this deployment? (the form opens on the Conversational Assistant if you hold purchased credits or are a partner or administrator), set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Click Deploy Module, review the estimated cost in the Deployment Confirmation dialog when it appears and click Submit (if the dialog then adds a confirmation step, such as verifying a project you bring, complete it and click Confirm), which opens the deployment status page with real-time logs.
The platform provisions the Cloud Run service (a web container plus a qcluster worker sidecar), a Cloud SQL (MySQL 8.0) database with its password in Secret Manager, two Cloud Storage buckets, builds the custom container image (wrapping inventree/inventree), and runs the initialization chain: db-init (database, user, grants) followed by migrate (Django migrations). First deploys take roughly 20–35 minutes (Cloud SQL creation, the image build and the migrations dominate).

When it completes, discover the resources with name-agnostic filters (so the commands keep working regardless of the deployment suffix). Build the project-number URL — InvenTree rejects the hash-form URL that status.url reports (see Task 2):

SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
  --filter="metadata.name~inventree" --format="value(metadata.name)" --limit=1)
PROJECT_NUMBER=$(gcloud projects describe "$PROJECT" --format="value(projectNumber)")
SERVICE_URL="https://${SERVICE}-${PROJECT_NUMBER}.${REGION}.run.app"
echo "Service: $SERVICE"
echo "URL:     $SERVICE_URL"

The same URL is the deployment's service_url output.

Task 2 — Access & verify [Manual]

Confirm the service is healthy. The root path redirects to the web UI — expect HTTP 302:
```
curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/"   # expect 302
```
See why the URL matters. The hash-form URL Cloud Run also advertises does not match INVENTREE_SITE_URL, and InvenTree answers it with an error (INVE-E7):
```
HASH_URL=$(gcloud run services describe "$SERVICE" \
  --project="$PROJECT" --region="$REGION" --format="value(status.url)")
curl -s -o /dev/null -w "%{http_code}\n" "$HASH_URL/"      # expect 500
```
Hand out $SERVICE_URL (or a custom domain) — never the hash form.

Confirm the revision runs two containers — the web container and the qcluster sidecar:

gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION" \
  --format="value(spec.template.spec.containers[].name)"

Confirm the init chain ran and the schema exists. The migrate job logs the number of tables it found ([migrate] tables present: N) and fails if there are fewer than 10:

gcloud run jobs list --project="$PROJECT" --region="$REGION" --filter="metadata.name~inventree"
gcloud logging read "resource.type=\"cloud_run_job\" AND textPayload:\"[migrate]\"" \
  --project="$PROJECT" --limit=10 --format="value(textPayload)"

Open $SERVICE_URL in a browser. The module creates no InvenTree account (the admin_email input is not used). To sign in, create the first superuser yourself — for example with InvenTree's own INVENTREE_ADMIN_USER / INVENTREE_ADMIN_EMAIL / INVENTREE_ADMIN_PASSWORD settings (see the InvenTree documentation), added through environment_variables and applied with Update. A credential-named key such as INVENTREE_ADMIN_PASSWORD is moved into Secret Manager automatically.

Task 3 — Operate & keep it running (Day-2) [Manual]

Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

Make the data directory persistent. By default InvenTree's data directory (/home/inventree/data: media, plugins, config.yaml, the generated secret key) is on the container's own filesystem and is lost on every cold start. For any deployment you intend to keep, set enable_nfs = true and nfs_mount_path = "/home/inventree/data" and click Update. Both are needed — NFS at the default path mounts a directory InvenTree never uses.
Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (a manual edit would be reverted on the next apply). Set min_instance_count = 1 if scheduled work matters: while the service is scaled to zero the qcluster worker is not running either.
Update the application version by changing application_version to an exact tag in the RAD platform and applying it via Update; a new image builds FROM inventree/inventree:<version> and the migrate job applies any new migrations before the new revision rolls out.

Manage secrets and backups:

gcloud secrets list --project="$PROJECT"
gcloud run jobs list --project="$PROJECT" --region="$REGION"   # init + scheduled backup jobs

Open a database session for inspection or maintenance:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
# User and database are tenant-prefixed — not the bare app name.
DB_USER=$(gcloud sql users list --instance="$INSTANCE" --project="$PROJECT" \
  --format="value(name)" --filter="name~^inventree" --limit=1)
gcloud sql connect "$INSTANCE" --user="$DB_USER" --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from the CLI or the Logs Explorer. Both containers log to the same service; the web container's startup line reads [startup] InvenTree site=… db=…:
```
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50
```
Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".
Monitoring — open the Cloud Run dashboard for the service and review request count, request latency, instance count (scaling behaviour), and CPU / memory utilisation. The module's uptime check is off by default (uptime_check_config.enabled = false); enable it with Update if you want one, then confirm it is green under Monitoring → Uptime checks and review Alerting → Policies.

Task 5 — Troubleshoot & debug [Manual]

Durable techniques for the failure modes you are most likely to hit. These are platform-level diagnostics and do not change with InvenTree releases.

HTTP 500 with INVE-E7 in the logs: the request used a host other than INVENTREE_SITE_URL — almost always the hash-form *.a.run.app URL. Use the project-number URL (service_url output) or a custom domain.
Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors. The entrypoint refuses to start if INVENTREE_SITE_URL/CLOUDRUN_SERVICE_URL or any of DB_IP, DB_NAME, DB_USER, DB_PASSWORD is missing, and names the missing variable. A failure in the qcluster sidecar fails the whole revision too.
```
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
```
Everyone is logged out / uploads vanish after idle: the data directory is not persistent — see Task 3, step 2.
Database Migrations required or missing tables: the migrate job did not complete. Read its execution logs:
```
gcloud run jobs executions list --job="${SERVICE}-db-init" \
  --project="$PROJECT" --region="$REGION"
gcloud run jobs executions list --job="${SERVICE}-migrate" \
  --project="$PROJECT" --region="$REGION"
```
If the schema was left half-applied, re-running migrate fails with Duplicate column name; the database has to be dropped and recreated.
UI renders unstyled: look for WARNING: collectstatic failed in the web container's startup logs.
Background tasks never run: check that enable_background_worker and cpu_always_allocated are both true, and that an instance exists (min_instance_count = 1).
Image build failed: review Cloud Build history for the failed build's log.
403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

Task 6 — Tear down [Automated]

On the Deployments page, open the deployment and click the Trash icon (Delete). Delete runs terraform destroy and is irreversible (the deployment record is retained for history). If a deployment is stuck and the RAD platform can no longer manage it (for example after manual changes that conflict with the Terraform state), use Purge instead (from the same Delete dialog) — it removes the deployment from RAD's records without destroying the cloud resources. This removes everything the module created — the Cloud Run service, the init jobs, the Cloud SQL database, Secret Manager secrets, Cloud Storage buckets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL instance, NFS server, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run (web + `qcluster`), Cloud SQL (MySQL 8.0), buckets, and runs `db-init` → `migrate`
2 — Access & verify	Manual	302 on the project-number URL, 500 on the hash URL; two containers; schema present
3 — Operate	Manual	Persist the data directory on NFS, scale, update version, manage secrets/backups, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics; optional uptime check
5 — Troubleshoot	Manual	Diagnose host mismatch, startup, data-loss, migration, and worker issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

Need RAD to do something it does not do yet? Request it on the roadmap, or vote on what is already there.

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

Task 3 — Operate & keep it running (Day-2) [Manual]​

Task 4 — Observe: Logging & Monitoring [Manual]​

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​