Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,24 @@
+FROM python:3.11-slim
+
+RUN useradd -m -u 1000 user
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
+USER user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    PYTHONUNBUFFERED=1
+
+WORKDIR $HOME/app
+
+RUN pip install --no-cache-dir --upgrade pip \
+    && pip install --no-cache-dir "mlflow[auth]>=2.14,<4"
+
+COPY --chown=user start.sh $HOME/app/start.sh
+RUN chmod +x $HOME/app/start.sh
+
+EXPOSE 7860
+
+CMD ["/home/user/app/start.sh"]

README.md

@@ -1,10 +1,30 @@
 ---
-title: Mlflow
-emoji: 🏆
+title: MLflow Tracking
+emoji: 📈
 colorFrom: blue
-colorTo: blue
+colorTo: indigo
 sdk: docker
+app_port: 7860
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MLflow Tracking (Mach1Corp)
+
+Docker Space that runs an [MLflow](https://mlflow.org/) tracking server for MOSS emotion LoRA experiments.
+
+## Persistence
+
+`Mach1Corp/mlflow-bucket` is mounted at `/data`:
+
+- Backend store: `sqlite:////data/mlflow.db`
+- Artifacts: `/data/mlartifacts`
+
+## Client usage
+
+```bash
+export MLFLOW_TRACKING_URI=https://mach1corp-mlflow.hf.space
+export MLFLOW_TRACKING_USERNAME=mlflow
+export MLFLOW_TRACKING_PASSWORD=<secret>
+```
+
+Then point training at that URI (`--mlflow-tracking-uri` or the env var).

start.sh

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+#!/usr/bin/env bash
+set -euo pipefail
+
+DATA_DIR="${MLFLOW_DATA_DIR:-/data}"
+mkdir -p "${DATA_DIR}/mlartifacts" "${DATA_DIR}/auth"
+
+AUTH_INI="${DATA_DIR}/auth/basic_auth.ini"
+AUTH_DB="${DATA_DIR}/auth/basic_auth.db"
+
+USERNAME="${MLFLOW_TRACKING_USERNAME:-mlflow}"
+PASSWORD="${MLFLOW_TRACKING_PASSWORD:-mlflow}"
+
+cat > "${AUTH_INI}" <<EOF
+[mlflow]
+default_permission = READ
+database_uri = sqlite:///${AUTH_DB}
+admin_username = ${USERNAME}
+admin_password = ${PASSWORD}
+authorization_function = mlflow.server.auth:authenticate_request_basic_auth
+EOF
+
+export MLFLOW_AUTH_CONFIG_PATH="${AUTH_INI}"
+
+exec mlflow server \
+  --host 0.0.0.0 \
+  --port 7860 \
+  --backend-store-uri "sqlite:///${DATA_DIR}/mlflow.db" \
+  --default-artifact-root "${DATA_DIR}/mlartifacts" \
+  --app-name basic-auth \
+  --allowed-hosts '*'