feat: dependency-free mlflow backend

docs/content/using-tesseracts/advanced.md

@@ -66,7 +66,7 @@ As an alternative to the MLflow setup we provide, you can point your Tesseract t
 $ tesseract serve --env=TESSERACT_MLFLOW_TRACKING_URI="..."  metrics
 ````
 
-Note that if your MLFlow server uses basic auth, you need to populate the `TESSERACT_MLFLOW_TRACKING_USERNAME` and
+Note that if your MLflow server uses basic auth, you need to populate the `TESSERACT_MLFLOW_TRACKING_USERNAME` and
 `TESSERACT_MLFLOW_TRACKING_PASSWORD` for the Tesseract to be able to authenticate to it.
 
 ```bash

examples/metrics/tesseract_requirements.txt

@@ -1,4 +1,2 @@
 # Tesseract requirements file
 # Generated by tesseract 0.9.2.dev16+g7ca45a2.d20250627 on 2025-06-27T11:44:45.333107
-
-mlflow==3.1.1

extra/mlflow/docker-compose-mlflow.yml

@@ -17,15 +17,30 @@ services:
     image: ghcr.io/mlflow/mlflow:latest
     restart: unless-stopped
     user: 1000:1000
-    command: mlflow server --backend-store-uri sqlite:///mlflow-data/mlflow.db --default-artifact-root file:///mlflow-data/mlruns --host 0.0.0.0 --port 5000
+    command: >
+      mlflow server
+      --backend-store-uri sqlite:///mlflow-data/mlflow.db
+      --serve-artifacts
+      --artifacts-destination file:///mlflow-data/mlruns/mlartifacts
+      --host 0.0.0.0
+      --allowed-hosts "mlflow-server:5000,localhost:*"
+      --port 5000
     volumes:
-      - mlflow-data:/mlflow-data
+      - mlflow-data:/mlflow-data:rw
     ports:
-      - "5000:5000"
+      - "5000"
     depends_on:
       mlflow-init:
         condition: service_completed_successfully
+    networks:
+      - mlflow-network
 
 volumes:
   mlflow-data:
     name: mlflow-data
+
+networks:
+  # Use a deterministic network name so we can attach Tesseract
+  # containers to it more easily
+  mlflow-network:
+    name: tesseract-mlflow-server