feat: add mlflow as default

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

inject_runtime_pyproject.py

@@ -28,7 +28,6 @@
         "fastapi",
         "httpx",  # required by fastapi test client
         "jsf",
-        "mlflow",
         "numpy",
         "pre-commit",
         "pytest",

tesseract_core/runtime/meta/pyproject.toml

@@ -16,6 +16,7 @@ dependencies = [
     "pybase64<=1.4.3,>=1.4",
     "numpy<=2.3.5,>=1.26",
     "debugpy<=1.8.18,>=1.8.14",
+    "mlflow-skinny>=3.7.0",
 ]
 
 [project.scripts]

tesseract_core/runtime/mpa.py

@@ -16,8 +16,9 @@
 from io import UnsupportedOperation
 from pathlib import Path
 from typing import Any
-from urllib.parse import quote, urlparse
+from urllib.parse import ParseResult, quote, urlparse, urlunparse
 
+import mlflow
 import requests
 
 from tesseract_core.runtime.config import get_config
@@ -129,98 +130,80 @@ class MLflowBackend(BaseBackend):
 
     def __init__(self, base_dir: str | None = None) -> None:
         super().__init__(base_dir)
-        os.environ["GIT_PYTHON_REFRESH"] = (
-            "quiet"  # Suppress potential MLflow git warnings
-        )
-
-        try:
-            import mlflow
-        except ImportError as exc:
-            raise ImportError(
-                "MLflow is required for MLflowBackend but is not installed"
-            ) from exc
 
-        self.mlflow = mlflow
         tracking_uri = MLflowBackend._build_tracking_uri()
-        self._ensure_mlflow_reachable(tracking_uri)
         mlflow.set_tracking_uri(tracking_uri)
+        self._ensure_mlflow_reachable(tracking_uri)
 
     @staticmethod
     def _build_tracking_uri() -> str:
         """Build the MLflow tracking URI with embedded credentials if provided."""
         config = get_config()
         tracking_uri = config.mlflow_tracking_uri
 
-        if not tracking_uri.startswith(("http://", "https://")):
-            # If it's a db file URI, convert to local path
-            tracking_uri = tracking_uri.replace("sqlite:///", "")
-
-            # Relative paths are resolved against the base output path
-            if not Path(tracking_uri).is_absolute():
-                tracking_uri = (Path(get_config().output_path) / tracking_uri).resolve()
-
-            tracking_uri = f"sqlite:///{tracking_uri}"
-        else:
-            username = config.mlflow_tracking_username
-            password = config.mlflow_tracking_password
-
-            if bool(username) != bool(password):
-                raise RuntimeError(
-                    "If one of TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD is defined, "
-                    "both must be defined."
-                )
-
-            if username and password:
-                parsed = urlparse(tracking_uri)
-                # Reconstruct URI with embedded credentials
-                tracking_uri = (
-                    f"{parsed.scheme}://{quote(username)}:{quote(password)}@"
-                    f"{parsed.netloc}{parsed.path}"
-                )
-                if parsed.query:
-                    tracking_uri += f"?{parsed.query}"
-                if parsed.fragment:
-                    tracking_uri += f"#{parsed.fragment}"
-
-        return tracking_uri
+        parsed = urlparse(tracking_uri)
+        if not parsed.scheme:
+            tracking_uri = f"https://{tracking_uri}"
+
+        parsed = urlparse(tracking_uri)
+        if parsed.scheme not in ("http", "https"):
+            raise ValueError(
+                f"MLflow logging only supports accessing MLflow via HTTP/HTTPS (got URI scheme: {parsed.scheme})"
+            )
+
+        username = config.mlflow_tracking_username
+        password = config.mlflow_tracking_password
+
+        if bool(username) != bool(password):
+            raise RuntimeError(
+                "If one of TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD is defined, "
+                "both must be defined."
+            )
+
+        if username and password:
+            # Reconstruct URI with embedded credentials
+            urlparts = parsed._asdict()
+            urlparts["netloc"] = f"{quote(username)}:{quote(password)}@{parsed.netloc}"
+            parsed = ParseResult(**urlparts)
+
+        return urlunparse(parsed)
 
     def _ensure_mlflow_reachable(self, tracking_uri: str) -> None:
         """Check if the MLflow tracking server is reachable."""
-        if tracking_uri.startswith(("http://", "https://")):
-            try:
-                response = requests.get(tracking_uri, timeout=5)
-                response.raise_for_status()
-            except requests.RequestException as e:
-                # Don't expose credentials in error message - use the original URI
-                config = get_config()
-                display_uri = config.mlflow_tracking_uri
-                raise RuntimeError(
-                    f"Failed to connect to MLflow tracking server at {display_uri}. "
-                    "Please make sure an MLflow server is running and TESSERACT_MLFLOW_TRACKING_URI is set correctly, "
-                    "or switch to file-based logging by setting TESSERACT_MLFLOW_TRACKING_URI to an empty string."
-                    "If your MLflow server has authentication enabled, please make sure that"
-                    "TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD are set correctly."
-                ) from e
+        try:
+            response = requests.get(tracking_uri, timeout=5)
+            response.raise_for_status()
+        except requests.RequestException as e:
+            # Don't expose credentials in error message - use the original URI
+            config = get_config()
+            display_uri = config.mlflow_tracking_uri
+            raise RuntimeError(
+                f"Failed to connect to MLflow tracking server at {display_uri}. "
+                "Please make sure an MLflow server is running and TESSERACT_MLFLOW_TRACKING_URI is set correctly, "
+                "or switch to file-based logging by setting TESSERACT_MLFLOW_TRACKING_URI to an empty string."
+                "If your MLflow server has authentication enabled, please make sure that"
+                "TESSERACT_MLFLOW_TRACKING_USERNAME and TESSERACT_MLFLOW_TRACKING_PASSWORD are set correctly."
+            ) from e
 
     def log_parameter(self, key: str, value: Any) -> None:
         """Log a parameter to MLflow."""
-        self.mlflow.log_param(key, value)
+        mlflow.log_param(key, value)
 
     def log_metric(self, key: str, value: float, step: int | None = None) -> None:
         """Log a metric to MLflow."""
-        self.mlflow.log_metric(key, value, step=step)
+        mlflow.log_metric(key, value, step=step)
 
     def log_artifact(self, local_path: str) -> None:
         """Log an artifact to MLflow."""
-        self.mlflow.log_artifact(local_path)
+        mlflow.log_artifact(local_path)
 
     def start_run(self) -> None:
         """Start a new MLflow run."""
-        self.mlflow.start_run()
+        mlflow.start_run()
 
     def end_run(self) -> None:
         """End the current MLflow run."""
-        self.mlflow.end_run()
+        mlflow.end_run()
 
 
 def _create_backend(base_dir: str | None) -> BaseBackend:

tests/conftest.py

@@ -6,13 +6,15 @@
 import random
 import string
 import subprocess
+import time
 from pathlib import Path
 from shutil import copytree
 from textwrap import indent
 from traceback import format_exception
 from typing import Any
 
 import pytest
+import requests
 
 # NOTE: Do NOT import tesseract_core here, as it will cause typeguard to fail
 
@@ -491,3 +493,104 @@ def hacked_get(url, *args, **kwargs):
     monkeypatch.setattr(engine.requests, "get", hacked_get)
 
     yield mock_instance
+
+
+@pytest.fixture(scope="module")
+def mlflow_server():
+    """MLflow server to use in tests."""
+    # Check if docker-compose is available
+    try:
+        result = subprocess.run(
+            ["docker", "compose", "version"],
+            capture_output=True,
+            check=True,
+        )
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        pytest.fail("docker-compose not available")
+
+    # Start MLflow server with unique project name
+    project_name = f"test_mlflow_{int(time.time())}"
+
+    compose_file = (
+        Path(__file__).parent.parent / "extra" / "mlflow" / "docker-compose-mlflow.yml"
+    )
+
+    try:
+        # Start the services
+        subprocess.run(
+            [
+                "docker",
+                "compose",
+                "-f",
+                str(compose_file),
+                "-p",
+                project_name,
+                "up",
+                "-d",
+            ],
+            check=True,
+            capture_output=True,
+        )
+
+        res = subprocess.run(
+            [
+                "docker",
+                "compose",
+                "-f",
+                str(compose_file),
+                "-p",
+                project_name,
+                "ps",
+                "--format",
+                "json",
+            ],
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+        service_data = json.loads(res.stdout)
+        service_port = service_data["Publishers"][0]["PublishedPort"]
+
+        # Note: We don't track containers/volumes here because docker-compose down -v
+        # will handle cleanup automatically in the finally block
+
+        # Wait for MLflow to be ready (with timeout)
+        tracking_uri = f"http://localhost:{service_port}"
+        max_wait = 30  # seconds
+        start_time = time.time()
+
+        while time.time() - start_time < max_wait:
+            try:
+                response = requests.get(tracking_uri, timeout=2)
+                if response.status_code == 200:
+                    break
+            except requests.RequestException:
+                pass
+            time.sleep(1)
+        else:
+            pytest.fail(f"MLflow server did not become ready within {max_wait}s")
+
+        yield tracking_uri
+
+    finally:
+        # Get logs for debugging
+        result = subprocess.run(
+            ["docker", "compose", "-f", str(compose_file), "-p", project_name, "logs"],
+            capture_output=True,
+            text=True,
+        )
+        print(result.stdout)
+        # Stop and remove containers
+        subprocess.run(
+            [
+                "docker",
+                "compose",
+                "-f",
+                str(compose_file),
+                "-p",
+                project_name,
+                "down",
+                "-v",
+            ],
+            capture_output=True,
+        )

tests/endtoend_tests/common.py

@@ -89,7 +89,9 @@ def build_tesseract(
     print_debug_info(result)
     assert result.exit_code == 0, result.exception
 
-    image_tag = json.loads(result.stdout.strip())[0]
+    # Parse the last line of stdout which contains the JSON array of image tags
+    stdout_lines = result.stdout.strip().split("\n")
+    image_tag = json.loads(stdout_lines[-1])[0]
 
     # This raise an error if the image does not exist
     client.images.get(image_tag)