| ש# Deploy Streamlit app (free – Streamlit Community Cloud) |
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|
| ## 1. Prerequisites |
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| - App code in a **public** GitHub repo (e.g. `solarwine-ai/Baseline`). |
| - A [Streamlit Community Cloud](https://share.streamlit.io) account (sign in with GitHub). |
| - `sensors_wide_sample.csv` committed under `Data/Seymour/` (already in the repo). |
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| ## 2. Deploy steps |
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| 1. Go to **https://share.streamlit.io** and sign in with GitHub. |
| 2. Click **"New app"**. |
| 3. Set: |
| - **Repository:** `solarwine-ai/Baseline` (or your fork). |
| - **Branch:** `main`. |
| - **Main file path:** `app.py`. |
| 4. Click **"Advanced settings"** and set: |
| - **Python version:** 3.11 (or match your local). |
| - Leave **Requirements file** as `requirements.txt` (repo root). |
| 5. Under **Secrets**, add your IMS API token so the app can fetch IMS data: |
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|
| ```toml |
| IMS_API_TOKEN = "your-ims-api-token-here" |
| ``` |
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| Streamlit Cloud injects secrets as environment variables; the app reads `IMS_API_TOKEN` from the environment via `os.environ`. |
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| 6. Click **"Deploy"**. The first build may take a few minutes. |
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| ## 3. How data works on Community Cloud |
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| - **Sensor data (Stage 1):** |
| The full `sensors_wide.csv` (982 MB) is gitignored. Instead, a trimmed |
| `sensors_wide_sample.csv` (~2.7 MB, Stage 1 columns only, growing season May-Sep) |
| is committed. The app automatically falls back to the sample when the full file |
| is absent — no code changes needed. |
|
|
| - **IMS data (Stage 2):** |
| Click **Download IMS 2024–2025** in the sidebar (requires `IMS_API_TOKEN` in |
| Secrets), then **Run Stage 2**. IMS data is fetched at runtime; free-tier memory |
| and CPU limits apply to very large or long-running fetches. |
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|
| - **Secrets:** |
| Never commit `.env` or real tokens. Use only the **Secrets** field in the |
| Streamlit Cloud app settings. |
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| ## 4. Regenerating the sample CSV locally |
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| If you update the full sensor data and need to refresh the sample: |
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| ```bash |
| python -m scripts.create_sample_data |
| ``` |
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| This reads `sensors_wide.csv`, extracts Stage 1 columns for May-Sep, and writes |
| `sensors_wide_sample.csv`. Commit the updated sample. |
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| ## 5. Configuration files |
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| - `.streamlit/config.toml` — sets `headless = true` and light theme for Cloud. |
| - `requirements.txt` — Python dependencies (already in repo root). |
| - No `packages.txt` needed (no OS-level dependencies). |
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| ## 6. Chronos-2 long training (local) |
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| To run LoRA fine-tuning with a large context window until results converge (single run, tuned for 32 GB RAM / 10 CPU cores): |
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|
| ```bash |
| cd /path/to/Baseline |
| PYTHONPATH=. conda run -n solarwine python scripts/run_chronos_long_training.py \ |
| --device cpu \ |
| --context-days 28 \ |
| --num-steps 4000 \ |
| --batch-size 16 |
| ``` |
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| - **Output:** Checkpoints in `outputs/chronos_finetuned_long/`; benchmark row `lora / all` appended to `outputs/chronos_benchmark.csv`; sample plot `outputs/chronos_forecast_sample.png`. |
| - **Convergence:** Chronos does not support resuming LoRA across multiple `fit()` calls; use one large `--num-steps` (e.g. 4000–6000). Training uses built-in validation; adjust `--learning-rate` (default `1e-5`) if needed. |
| - **Resources:** Script sets `OMP_NUM_THREADS` from CPU count; use `--batch-size 8` on lower memory. |
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