Initial scaffold: LLM Trainer Dashboard

Full-stack app with FastAPI backend (SSH/paramiko, pipeline streaming,
GPU stats, xterm.js terminal, Ollama model manager) and React + Tailwind
frontend (8 panels: Connection, Documents, Pipeline, QA Pairs, Training,
Terminal, Models, Config). Docker Compose included.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/pipeline.py

@@ -0,0 +1,73 @@
+# ──────────────────────────────────────────────────────────────────────────────
+#  Pipeline paths & command builders
+#  These match the remote Ubuntu server layout from LLM_TRAINER_APP_SCOPE.md
+# ──────────────────────────────────────────────────────────────────────────────
+
+SDK_BIN = (
+    "/home/tocmo0nlord/miniconda3/envs/synthetic-data/bin/synthetic-data-kit"
+)
+CONFIG_PATH = "/opt/synthetic/synthetic-data-kit/config.yaml"
+DATA_BASE   = "/opt/synthetic/synthetic-data-kit/data"
+
+STAGE_DIRS = {
+    "input":     f"{DATA_BASE}/input",
+    "parsed":    f"{DATA_BASE}/parsed",
+    "generated": f"{DATA_BASE}/generated",
+    "curated":   f"{DATA_BASE}/curated",
+    "final":     f"{DATA_BASE}/final",
+}
+
+TRAIN_SCRIPT = "/opt/synthetic/train.py"
+OUTPUT_BASE  = "/opt/synthetic/output"
+
+
+def _sdk(subcommand: str, *args) -> str:
+    return f"{SDK_BIN} --config {CONFIG_PATH} {subcommand} {' '.join(args)}"
+
+
+def ingest_cmd(input_file: str) -> str:
+    return _sdk("ingest", f"'{input_file}'", "-o", STAGE_DIRS["parsed"])
+
+
+def create_cmd(parsed_file: str, num_pairs: int = 50, pair_type: str = "qa") -> str:
+    return _sdk(
+        "create", f"'{parsed_file}'",
+        "-o", STAGE_DIRS["generated"],
+        "--type", pair_type,
+        "--num-pairs", str(num_pairs),
+    )
+
+
+def curate_cmd(generated_file: str, output_file: str, threshold: float = 7.0) -> str:
+    return _sdk(
+        "curate", f"'{generated_file}'",
+        "-o", f"'{output_file}'",
+        "--threshold", str(threshold),
+    )
+
+
+def save_as_cmd(curated_file: str, output_file: str, fmt: str = "jsonl") -> str:
+    return _sdk(
+        "save-as", f"'{curated_file}'",
+        "-f", fmt,
+        "-o", f"'{output_file}'",
+    )
+
+
+def train_cmd(
+    model_name: str,
+    dataset_path: str,
+    output_dir: str = OUTPUT_BASE,
+    num_epochs: int = 3,
+    batch_size: int = 2,
+    learning_rate: float = 2e-4,
+) -> str:
+    return (
+        f"python3 {TRAIN_SCRIPT} "
+        f"--model '{model_name}' "
+        f"--dataset '{dataset_path}' "
+        f"--output '{output_dir}' "
+        f"--epochs {num_epochs} "
+        f"--batch-size {batch_size} "
+        f"--lr {learning_rate}"
+    )