File size: 2,994 Bytes
f075eda | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | # Deploy — Render (free tier)
This guide walks through deploying `youtube_hate_detector` to [Render](https://render.com)
using the Blueprint (`render.yaml`) at the root of the repo. The Blueprint provisions
two services: a Dockerised FastAPI backend (`signalmod-api`) and a static React/Vite
frontend (`signalmod-ui`).
## Prerequisites
- A free [Render](https://dashboard.render.com/register) account.
- This repository pushed to GitHub (Render reads the Blueprint from the connected branch).
- Credentials for any external services you plan to use:
- `YOUTUBE_API_KEY` — Google Cloud Console → APIs & Services → Credentials.
- `SUPABASE_URL` and `SUPABASE_KEY` — Supabase project settings (optional, only if
persistence is enabled).
## One-shot deploy (Blueprint)
1. Open the Render dashboard → **New** → **Blueprint**.
2. Connect the GitHub account and pick this repository / branch.
3. Render parses `render.yaml` and shows the two services. Click **Apply**.
4. Wait for the first build. The API image takes ~5–8 minutes (frontend build +
Python deps + spaCy/NLTK data).
5. Once the API is live, open **signalmod-api → Environment** and fill in the
secrets that were declared with `sync: false`:
- `YOUTUBE_API_KEY`
- `SUPABASE_URL`
- `SUPABASE_KEY`
6. Trigger a manual redeploy so the API picks up the new env vars.
The static site (`signalmod-ui`) reads `VITE_API_BASE_URL` at build time and points
at `https://signalmod-api.onrender.com`. If you renamed the API service, update that
env var in `render.yaml` and re-apply the Blueprint.
## Important free-tier caveats
- **Cold starts.** Free web services sleep after 15 minutes of inactivity. The first
request after a sleep can take 30–60 seconds while the container restarts and the
model warms up. Subsequent requests are fast.
- **Memory ceiling: 512 MB.** The default production model
(`Meta-Feature Stacking (Production)`) loads BERT and needs roughly 700 MB of RAM,
so it will OOM on free. The Blueprint pins `MODEL_NAME="LR + TF-IDF (Baseline)"`,
which runs comfortably under the limit. If you upgrade to a paid plan with
≥1 GB RAM, you can switch the env var to the meta-stacking model and rebuild the
image with `INSTALL_HF=1`.
- **Build image size.** `render.yaml` builds with the default `INSTALL_HF=0` so the
Docker image stays small (no torch/transformers). Locally, `docker-compose.yml`
still passes `INSTALL_HF=1` for parity with the production model.
## Test the Docker image locally before pushing
```bash
# Build the same image Render will build (free-tier shape — no HF extras)
docker build --build-arg INSTALL_HF=0 -t signalmod-api:render .
# Run it with the same model the Blueprint will use
docker run --rm -p 8000:8000 \
-e MODEL_NAME="LR + TF-IDF (Baseline)" \
-e ENV=production \
signalmod-api:render
# Health check
curl http://localhost:8000/health
```
If `/health` returns `200 OK` locally, the same image will boot on Render.
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