feat: deploy training data to zoe root

SDG_BRIDGE_PLAN.md

@@ -8,8 +8,9 @@ scripts/generate_training_data.py
 
 This script reads the generated dictionary artifacts from this project, optionally runs a
 NeMo Data Designer expansion pass on Mimir via SSH, and writes a timestamped raw training
-dataset into the `workspace_Data` pipeline, then registers it in `manifest.llm.json` so the
-Training Monitor picks it up automatically.
+dataset into `/Volumes/Zoe/custom-local-llm/training-data`, then registers it in
+`/Volumes/Zoe/custom-local-llm/manifest.llm.json` so local training tools can point at one
+stable artifact root.
 
 ---
 
@@ -46,10 +47,10 @@ Step 2 (remote — Mimir, optional):             │
          --dataset-name module_controller_intents_<timestamp>
   SCP expanded dataset back
                                                │
-Step 3 (local — workspace_Data):               │
-  Write to data/module_controller_intents_<timestamp>/ ◄──────────────┘
+Step 3 (local — Zoe custom-local-llm):         │
+  Write to training-data/module_controller_intents_<timestamp>/ ◄─────┘
   Patch manifest.llm.json with timestamped entry
-  Training Monitor picks it up as a new Data Source
+  Training tools pick it up from the Zoe artifact root
 ```
 
 ---
@@ -59,7 +60,7 @@ Step 3 (local — workspace_Data):               │
 Every dataset generated by this script is tagged with a **Unix timestamp at generation time**.
 The timestamp is embedded in:
 
-1. **Output directory name:** `data/module_controller_intents_<unix_ts>/`
+1. **Output directory name:** `training-data/module_controller_intents_<unix_ts>/`
 2. **Manifest entry key:** `"module_controller_intents_<unix_ts>"`
 3. **Manifest entry field:** `"generated_at": <unix_ts>`
 4. **Every JSONL record's metadata:** `"generated_at": <unix_ts>`
@@ -167,7 +168,7 @@ columns:
   "redistribution_allowed": false,
   "estimated_tokens": null,
   "status": "generated_local",
-  "local_path": "data/module_controller_intents_<unix_ts>",
+  "local_path": "training-data/module_controller_intents_<unix_ts>",
   "local_format": "jsonl",
   "generator": "mp-ai-module-controller/scripts/generate_training_data.py",
   "sdg_engine": "data-designer",
@@ -191,14 +192,14 @@ columns:
 # Seed only (no NeMo expansion — fast, local, 260 records):
 python3 scripts/generate_training_data.py \
   --dictionary-dir dictionary/ \
-  --output-dir ../workspace_Data/data/ \
-  --manifest ../workspace_Data/manifest.llm.json
+  --output-dir /Volumes/Zoe/custom-local-llm/training-data/ \
+  --manifest /Volumes/Zoe/custom-local-llm/manifest.llm.json
 
 # Full SDG run via Mimir (requires NVIDIA_API_KEY on Mimir):
 python3 scripts/generate_training_data.py \
   --dictionary-dir dictionary/ \
-  --output-dir ../workspace_Data/data/ \
-  --manifest ../workspace_Data/manifest.llm.json \
+  --output-dir /Volumes/Zoe/custom-local-llm/training-data/ \
+  --manifest /Volumes/Zoe/custom-local-llm/manifest.llm.json \
   --sdg-host 100.80.52.47 \
   --sdg-user aaron-pressey \
   --sdg-key ~/.ssh/silma_orson_ed25519 \
@@ -206,6 +207,9 @@ python3 scripts/generate_training_data.py \
 
 # Dry run (prints what would be generated, writes nothing):
 python3 scripts/generate_training_data.py --dry-run
+
+# Woodpecker deploy target:
+scripts/deploy_training_data.sh
 ```
 
 ---