# Deploying MLX Model Explorer

The Space and dataset are created **private**, verified end to end, and only then made public.

Prerequisites: `hf auth login` as a member of `mlx-community` with write access, plus the project venv (`uv pip install -r requirements.txt`).

## 1. Verify locally

```bash
.venv/bin/python -m pytest
EXPLORER_SINK=local EXPLORER_FLUSH_SECONDS=20 .venv/bin/uvicorn app.main:app --port 7860
# walk through the UI; events land in .runtime/local_dataset/data/events/
docker build --platform linux/amd64 -t mlx-model-explorer . \
  && docker run --rm -p 7860:7860 -e EXPLORER_SINK=local mlx-model-explorer
```

## 2. Create private repos

```bash
.venv/bin/python scripts/deploy.py status
.venv/bin/python scripts/deploy.py create      # refuses if the Space exists; add --allow-existing-dataset if the dataset was created earlier
```

This creates `mlx-community/mlx-model-explorer-data` (private dataset, card uploaded) and `codelion/mlx-model-explorer` (private Docker Space with `EXPLORER_SINK=hub`, `DATASET_REPO`, `EXPLORER_FLUSH_SECONDS=600`).

> **Why the Space isn't in `mlx-community`:** Hugging Face now requires a Team or Enterprise plan for an organization to run Docker or Gradio Spaces on free CPU (`402 Payment Required`). The dataset can live in the org. Once an org admin has a plan or a hardware grant, deploy there with `SPACE_REPO=mlx-community/mlx-model-explorer scripts/deploy.py create --allow-existing-dataset` followed by `upload`.

## 3. Dataset write token

Create a **fine-grained** token at https://huggingface.co/settings/tokens with *write* permission on `mlx-community/mlx-model-explorer-data` only. Don't use a personal all-access token: anything in a Space secret is available to the running app.

```bash
HF_DATASET_WRITE_TOKEN=hf_xxx .venv/bin/python scripts/deploy.py secret
```

## 4. Test the ingestion path against the private dataset from this machine

```bash
EXPLORER_SINK=hub EXPLORER_DATA_PREFIX=data/_test EXPLORER_FLUSH_SECONDS=30 \
  HF_TOKEN=$(cat ~/.cache/huggingface/token) .venv/bin/uvicorn app.main:app --port 7860
```

## 5. Upload and test the private Space

```bash
.venv/bin/python scripts/deploy.py variable EXPLORER_DATA_PREFIX data/_test   # keep test events separate
.venv/bin/python scripts/deploy.py upload
.venv/bin/python scripts/deploy.py status                                     # wait for RUNNING
```

Open https://huggingface.co/spaces/codelion/mlx-model-explorer while logged in. Run the full flow, then confirm a shard appears under `data/_test/` in the dataset (after the flush interval, or restart the Space to force a flush).

To submit a benchmark to the private Space, use its direct URL and a token:

```bash
python bench/mlx_explorer_bench.py --model mlx-community/Qwen3-0.6B-4bit \
  --submit https://codelion-mlx-model-explorer.hf.space --hf-token $(cat ~/.cache/huggingface/token)
```

## 6. Go public (only after review)

```bash
.venv/bin/python scripts/deploy.py variable EXPLORER_DATA_PREFIX data/events
.venv/bin/python scripts/deploy.py clean-test-data
.venv/bin/python scripts/deploy.py publish --yes-make-public
```

`publish` refuses while `data/_test/` still exists.

## Updating

Change code, run the tests, `docker build` locally, then `scripts/deploy.py upload -m "what changed"`. Events are append-only, so redeploys never touch existing data. On shutdown the app flushes buffered events, and anything that fails to upload is retried from the on-disk spool.

## Kill switch

`scripts/deploy.py variable EXPLORER_SINK off` stops all collection. The UI keeps working and the event endpoint accepts nothing.