Modelplane upgrades

.github/workflows/tests.yml

@@ -8,7 +8,7 @@ on:
   workflow_dispatch:
     inputs:
       branch:
-        description: 'Branch'
+        description: "Branch"
         required: true
         default: main
 
@@ -32,7 +32,13 @@ jobs:
           ./start_services.sh --no-jupyter -d
 
       - name: Install poetry
-        run: pipx install "poetry == 1.8.5"
+        run: pipx install poetry
+
+      - name: Verify MLflow versions match
+        run: ./scripts/check_mlflow_versions.sh
+
+      - name: Check poetry lock file
+        run: poetry check --lock
 
       - name: Remove existing virtual environment
         run: |
@@ -75,6 +81,5 @@ jobs:
         run: |
           docker exec modelplane-jupyter-1 poetry run python /app/test_notebooks.py
 
-
       - name: Stop MLflow server
         run: docker compose down

.gitignore

@@ -7,3 +7,7 @@ secrets.toml
 .vscode/
 .coverage*
 .cache
+*.csv
+*.txt
+*.json
+*.jsonl

Dockerfile.mlflow

@@ -1,8 +1,7 @@
-FROM ghcr.io/mlflow/mlflow:v3.1.1
+FROM ghcr.io/mlflow/mlflow:v3.7.0
 
 # The base image does not include various dependencies that are needed for
 # the MLflow server. We assume a postgres backend, so we need psycopg2.
 # We also need boto3 for S3 support, and google-cloud-storage for GCS support.
-# TODO: better way to install these (maybe using poetry.lock to grab consistent versions?)
-RUN pip install mlflow[auth]==3.1.1 psycopg2-binary==2.9.10 boto3==1.38.31 \
-    google-cloud-storage==3.1.0
+RUN pip install mlflow[auth]==3.7.0 psycopg2-binary==2.9.11 boto3==1.42.5 \
+    google-cloud-storage==3.4.1

README.md

@@ -43,9 +43,8 @@ for access.
 MLFlow server (`MLFLOW_TRACKING_USERNAME` /
 `MLFLOW_TRACKING_PASSWORD`).
     * Alternatively, put the credentials in `~/.mlflow/credentials` as described [here](https://mlflow.org/docs/latest/ml/auth/#credentials-file).
-1. To access `modelbench-private` code (assuming you have 
-access), you must also set `USE_MODELBENCH_PRIVATE=true` in `.env.jupyteronly`. This will forward your ssh agent to the container
-allowing it to load the private repository to build the image.
+1. To access the private annotators, you need to set up credentials to access cheval (see `modelgauge.annotators.cheval.registration`)
+and reach out to [[email protected]](mailto:[email protected]) for the credentials.
 1. Start jupyter with `./start_jupyter.sh`. (You can add the
 `-d` flag to start in the background.)
 
@@ -97,17 +96,14 @@ or you can get the `run_id` via the MLFlow UI.
 MLFLOW_TRACKING_URI=http://localhost:8080 poetry run modelplane annotate --annotator_id {annotator_id} --experiment expname --response_run_id {run_id}
 ```
 
-### Custom Ensembles
+#### Private Ensemble
+If you have access to the private annotator, you can run directly with:
 ```
-MLFLOW_TRACKING_URI=http://localhost:8080 poetry run modelplane annotate --annotator_id {annotator_id1} --annotator_id {annotator_id2} --ensemble_strategy {ensemble_strategy} --experiment expname --response_file path/to/response.csv
+MLFLOW_TRACKING_URI=http://localhost:8080 poetry run modelplane annotate --annotator_id safety-v1.1 --experiment expname --response_run_id {run_id}
 ```
 
-### Private Ensemble
-If you have access to the private ensemble, you can install with the needed extras
-```
-poetry install --extras modelbench-private
-```
-And then run annotations with:
+
+### Custom Ensembles
 ```
-MLFLOW_TRACKING_URI=http://localhost:8080 poetry run modelplane annotate --ensemble_id official --experiment expname --response_run_id {run_id}
+MLFLOW_TRACKING_URI=http://localhost:8080 poetry run modelplane annotate --annotator_id {annotator_id1} --annotator_id {annotator_id2} --ensemble_strategy {ensemble_strategy} --experiment expname --response_file path/to/response.csv
 ```

docker-compose.yaml

@@ -27,6 +27,9 @@ services:
       GOOGLE_APPLICATION_CREDENTIALS: /creds/gcp-key.json
       # if not provided via volume below, AWS S3 will not work as artifact store
       AWS_SHARED_CREDENTIALS_FILE: /creds/aws-credentials
+      # https://mlflow.org/docs/latest/community/usage-tracking/
+      MLFLOW_DISABLE_TELEMETRY: "true"
+      DO_NOT_TRACK: "true"
     depends_on:
       - postgres
     # grab backend from .env, pass artifact root, if provided, otherwise local storage of artifacts
@@ -37,6 +40,7 @@ services:
         --serve-artifacts
         --host 0.0.0.0
         --port 8080
+        --allowed-hosts 'mlflow:*,localhost:*,127.0.0.1:*'
     ports:
       - "8080:8080"
     healthcheck:
@@ -51,7 +55,7 @@ services:
       - ${GOOGLE_CREDENTIALS_PATH:-/dev/null}:/creds/gcp-key.json:ro
       # Volume only needed for AWS S3 storage artifacts
       - ${AWS_CREDENTIALS_PATH:-/dev/null}:/creds/aws-credentials:ro
-  
+
   jupyter:
     build:
       context: .

flightpaths/Annotator Development Template.ipynb

@@ -76,9 +76,9 @@
     "\n",
     "An implementation of the annotator should define:\n",
     "\n",
-    "* `translate_request`: takes a `TestItem` and `SUTResponse` (which have the needed information on the prompt responses for the given LLM) and translates the request into an arbitrary form useful for the `annotate` method.\n",
+    "* `translate_prompt`: takes a `TextPrompt` and `SUTResponse` (which have the needed information on the prompt responses for the given LLM) and translates the request into an arbitrary form useful for the `annotate` method.\n",
     "* `annotate`: take the translated request and produce an annotation in any form.\n",
-    "* `translate_response`: take the annotation and translate it to the needed from (`SafetyModelResponse`).\n",
+    "* `translate_response`: take the annotation and translate it to the needed from (`SafetyAnnotation`).\n",
     "\n",
     "Below, we give a sample `RandomAnnotator`, which randomly decides whether a given response is safe by hazard category."
    ]
@@ -94,10 +94,10 @@
     "\n",
     "from pydantic import BaseModel\n",
     "\n",
-    "from modelgauge.annotator import CompletionAnnotator\n",
+    "from modelgauge.annotation import SafetyAnnotation\n",
+    "from modelgauge.annotator import Annotator\n",
     "from modelgauge.annotator_registry import ANNOTATORS\n",
     "from modelgauge.prompt import ChatPrompt, TextPrompt\n",
-    "from modelgauge.safety_model_response import SafetyModelResponse\n",
     "from modelgauge.single_turn_prompt_response import TestItem\n",
     "from modelgauge.sut import SUTResponse\n",
     "from modelgauge.tests.safe_v1 import BaseSafeTestVersion1\n",
@@ -107,9 +107,8 @@
     "    response: SUTResponse\n",
     "\n",
     "\n",
-    "class RandomAnnotator(CompletionAnnotator[SafetyModelResponse]):\n",
-    "    \"\"\"Simple random implementation for demonstration. Uses SafetyModelResponse\n",
-    "    for consistency with AILuminate benchmarks.\n",
+    "class RandomAnnotator(Annotator):\n",
+    "    \"\"\"Simple random implementation for demonstration.\n",
     "\n",
     "    The methods to implement are based on the idea that annotate will \n",
     "    potentially make a call to another system and the translators are helpful \n",
@@ -126,18 +125,16 @@
     "            response=response,\n",
     "        )\n",
     "\n",
-    "    def annotate(self, annotation_request: AnnotationRequest) -> SafetyModelResponse:\n",
+    "    def annotate(self, annotation_request: AnnotationRequest) -> SafetyAnnotation:\n",
     "        unsafe_categories = []\n",
     "        for category in BaseSafeTestVersion1.hazards:\n",
     "            if random.random() < self.prob_unsafe:\n",
     "                unsafe_categories.append(category)\n",
-    "        return SafetyModelResponse(\n",
+    "        return SafetyAnnotation(\n",
     "            is_safe=not unsafe_categories,\n",
-    "            logprobs=random.random(),\n",
-    "            safety_categories=unsafe_categories,\n",
     "        )\n",
     "\n",
-    "    def translate_response(self, request: AnnotationRequest, response: SafetyModelResponse) -> SafetyModelResponse:\n",
+    "    def translate_response(self, request: AnnotationRequest, response: SafetyAnnotation) -> SafetyAnnotation:\n",
     "        return response\n",
     "\n",
     "\n",
@@ -222,14 +219,6 @@
     "    ground_truth=ground_truth,\n",
     ")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af9debec-28be-4a50-82da-5d7025de7d76",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -248,7 +237,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.11"
+   "version": "3.12.12"
   }
  },
  "nbformat": 4,

flightpaths/Ensemble Development Template.ipynb

@@ -70,11 +70,11 @@
     "\n",
     "* An implementation of an annotator should define:\n",
     "\n",
-    "  * `translate_request`: takes a `TestItem` and `SUTResponse` (which have the needed information on the prompt responses for the given LLM) and translates the request into an arbitrary form useful for the `annotate` method.\n",
+    "  * `translate_prompt`: takes a `TextPrompt` and `SUTResponse` (which have the needed information on the prompt responses for the given LLM) and translates the request into an arbitrary form useful for the `annotate` method.\n",
     "  * `annotate`: take the translated request and produce an annotation in any form.\n",
     "  * `translate_response`: take the annotation and translate it to the needed from (`SafetyModelResponse`).\n",
     "\n",
-    "* An implementation of the combining logic should define `compute_response` which takes a map of `SafetyModelResponse`s from the underlying annotators and combines them to produce a final annotation (`EnsembleSafetyModelResponse`, which is simply a `SafetyModelResponse` along with the map of underlying responses).\n",
+    "* An implementation of the combining logic should define `compute_response` which takes a map of `SafetyAnnotation`s from the underlying annotators and combines them to produce a final annotation (`EnsembleSafetyAnnotation`, which is simply a `SafetyAnnotation` along with the map of underlying responses).\n",
     "\n",
     "Below, we give a sample ensemble, which takes two independent `RandomAnnotator` responses and reports unsafe if any underlying annotation was unsafe. This requires implementations of the `RandomAnnotator` and `AnyUnsafeStrategy`."
    ]
@@ -100,21 +100,21 @@
    "source": [
     "import random\n",
     "\n",
-    "from pydantic import BaseModel\n",
-    "\n",
-    "from modelgauge.annotator import CompletionAnnotator\n",
+    "from modelgauge.annotation import SafetyAnnotation\n",
+    "from modelgauge.annotator import Annotator\n",
     "from modelgauge.annotator_registry import ANNOTATORS\n",
     "from modelgauge.prompt import ChatPrompt, TextPrompt\n",
-    "from modelgauge.safety_model_response import SafetyModelResponse\n",
     "from modelgauge.sut import SUTResponse\n",
     "from modelgauge.tests.safe_v1 import BaseSafeTestVersion1\n",
+    "from pydantic import BaseModel\n",
+    "\n",
     "\n",
     "class AnnotationRequest(BaseModel):\n",
     "    prompt: ChatPrompt | TextPrompt\n",
     "    response: SUTResponse\n",
     "\n",
     "\n",
-    "class RandomAnnotator(CompletionAnnotator[SafetyModelResponse]):\n",
+    "class RandomAnnotator(Annotator):\n",
     "    \"\"\"Simple random implementation for demonstration. Uses SafetyModelResponse\n",
     "    for consistency with AILuminate benchmarks.\n",
     "\n",
@@ -132,19 +132,17 @@
     "            response=response,\n",
     "        )\n",
     "\n",
-    "    def annotate(self, annotation_request: AnnotationRequest) -> SafetyModelResponse:\n",
+    "    def annotate(self, annotation_request: AnnotationRequest) -> SafetyAnnotation:\n",
     "        unsafe_categories = []\n",
     "        for category in BaseSafeTestVersion1.hazards:\n",
     "            if random.random() < self.prob_unsafe:\n",
     "                unsafe_categories.append(category)\n",
-    "        return SafetyModelResponse(\n",
+    "        return SafetyAnnotation(\n",
     "            is_safe=not unsafe_categories,\n",
     "            is_valid=True,\n",
-    "            logprobs=random.random(),\n",
-    "            safety_categories=unsafe_categories,\n",
     "        )\n",
     "\n",
-    "    def translate_response(self, request: AnnotationRequest, response: SafetyModelResponse) -> SafetyModelResponse:\n",
+    "    def translate_response(self, request: AnnotationRequest, response: SafetyAnnotation) -> SafetyAnnotation:\n",
     "        return response\n",
     "\n",
     "\n",
@@ -176,26 +174,22 @@
    "source": [
     "from typing import Dict\n",
     "\n",
-    "from modelgauge.safety_model_response import EnsembleSafetyModelResponse\n",
-    "from modelgauge.ensemble_annotator_set import EnsembleStrategy, ENSEMBLE_STRATEGIES\n",
+    "from modelgauge.annotation import EnsembleSafetyAnnotation\n",
+    "from modelgauge.ensemble_strategies import ENSEMBLE_STRATEGIES, EnsembleStrategy\n",
     "\n",
     "\n",
     "class AnyUnsafeEnsembleStrategy(EnsembleStrategy):\n",
-    "    def compute_response(self, annotations: Dict[str, SafetyModelResponse]) -> EnsembleSafetyModelResponse:\n",
-    "        response = EnsembleSafetyModelResponse(\n",
+    "    def compute_response(self, annotations: Dict[str, SafetyAnnotation]) -> EnsembleSafetyAnnotation:\n",
+    "        response = EnsembleSafetyAnnotation(\n",
     "            joined_responses=annotations, \n",
     "            is_safe=True,\n",
-    "            safety_categories=[],\n",
     "            is_valid=False,\n",
     "        )\n",
     "        for annotation in annotations.values():\n",
     "            if annotation.is_valid:\n",
     "                response.is_valid = True\n",
     "                if not annotation.is_safe:\n",
     "                    response.is_safe = False\n",
-    "                    current_categories = set(response.safety_categories)\n",
-    "                    current_categories.update(annotation.safety_categories)\n",
-    "                    response.safety_categories = sorted(current_categories)\n",
     "        return response\n",
     "\n",
     "ENSEMBLE_STRATEGIES[\"anyunsafe\"] = AnyUnsafeEnsembleStrategy()"
@@ -286,18 +280,6 @@
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.11"
   }
  },
  "nbformat": 4,