Default to CPU-only version of torch. Improve docs.

.gitignore

@@ -1,3 +1,4 @@
+dyff-outputs/
 models/
 venv/
 **/__pycache__

Dockerfile

@@ -8,8 +8,11 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip setuptools wheel
 
 WORKDIR /app/
 
-COPY requirements.txt ./
-RUN python3 -m pip install --no-cache-dir -r ./requirements.txt
+COPY requirements.cpu.txt ./
+RUN python3 -m pip install --no-cache-dir -r ./requirements.cpu.txt
+
+COPY requirements.torch.cpu.txt ./
+RUN python3 -m pip install --no-cache-dir -r ./requirements.torch.cpu.txt
 
 COPY app ./app
 COPY models ./models

README.md

@@ -8,18 +8,26 @@ FastAPI service for serving ML models over HTTP. Comes with ResNet-18 for image
 
 ## Quick Start
 
+**Install `uv`:**
+https://docs.astral.sh/uv/getting-started/installation/
+
 **Local development:**
 ```bash
 # Install dependencies
-python -m venv .venv
-source .venv/bin/activate
-pip install -r requirements.txt
+make setup
+source venv/bin/activate
 
 # Download the example model
-bash scripts/model_download.bash
+make download
 
 # Run it
-uvicorn main:app --reload
+make serve
+```
+
+In a second terminal:
+```bash
+# Process an example input
+./prompt.sh cat.json
 ```
 
 Server runs on `http://127.0.0.1:8000`. Check `/docs` for the interactive API documentation.
@@ -27,16 +35,13 @@ Server runs on `http://127.0.0.1:8000`. Check `/docs` for the interactive API do
 **Docker:**
 ```bash
 # Build
-docker build -t ml-inference-service:test .
+make docker-build
 
 # Run
-docker run -d --name ml-inference-test -p 8000:8000 ml-inference-service:test
+make docker-run
 
 # Check logs
-docker logs -f ml-inference-test
-
-# Stop
-docker stop ml-inference-test && docker rm ml-inference-test
+docker logs -f safe-challenge-2025/example-submission
 ```
 
 ## Testing the API
@@ -56,18 +61,18 @@ curl -X POST http://localhost:8000/predict \
 Example response:
 ```json
 {
-  "prediction": "tiger cat",
-  "confidence": 0.394,
-  "predicted_label": 282,
-  "model": "microsoft/resnet-18",
-  "mediaType": "image/jpeg"
+  "logprobs": [-0.859380304813385,-1.2701971530914307,-2.1918208599090576,-1.69235098361969],
+  "localizationMask": {
+    "mediaType":"image/png",
+    "data":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAKDAQAAAAD9Fl5AAAAAu0lEQVR4nO3NsREAMAgDMWD/nZMVKEwn1T5/FQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMCl3g5f+HC24TRhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAj70gwKsTlmdBwAAAABJRU5ErkJggg=="
+  }
 }
 ```
 
 ## Project Structure
 
 ```
-ml-inference-service/
+example-submission/
 ├── main.py                      # Entry point
 ├── app/
 │   ├── core/
@@ -75,7 +80,7 @@ ml-inference-service/
 │   │   └── logging.py           # Logging setup
 │   ├── api/
 │   │   ├── models.py            # Request/response schemas
-│   │   ├── controllers.py       # Business logic
+│   │   ├── controllers.py       # <= IMPLEMENT YOUR DETECTOR HERE
 │   │   └── routes/
 │   │       └── prediction.py    # POST /predict
 │   └── services/
@@ -88,9 +93,14 @@ ml-inference-service/
 │   ├── model_download.bash
 │   ├── generate_test_datasets.py
 │   └── test_datasets.py
-├── Dockerfile                   # Multi-stage build
+├── Dockerfile
 ├── .env.example                 # Environment config template
-└── requirements.txt
+├── cat.json                     # An example /predict request object
+├── makefile
+├── requirements.in              
+├── requirements.txt
+├── response.json               # An example /predict response object
+└──
 ```
 
 The key design decision here is that `app/core/app.py` consolidates everything—config, dependency injection, lifecycle, and the app factory. This avoids the mess of managing global state across multiple files.

app/api/models.py

@@ -1,18 +1,70 @@
 """
 Pydantic models for request/response validation.
 """
+
+from __future__ import annotations
+
+import base64
 import enum
-from typing import Optional
+import io
+import typing
+from typing import Literal, Optional
 
+import numpy as np
 import pydantic
+from PIL import Image
 
-from dyff.schema.base import int32
+if typing.TYPE_CHECKING:
+    from numpy.typing import NDArray
 
 
 class ImageData(pydantic.BaseModel):
     """Image data model for base64 encoded images."""
-    mediaType: str
-    data: str
+    mediaType: str = pydantic.Field(
+        description="The IETF Media Type (MIME type) of the data"
+    )
+    data: str = pydantic.Field(
+        description="A base64 string encoding of the data.",
+        # Canonical base64 encoding
+        # https://stackoverflow.com/a/64467300/3709935
+        pattern=r"^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/][AQgw]==|[A-Za-z0-9+/]{2}[AEIMQUYcgkosw048]=)?$",
+    )
+
+
+class BinaryMask(pydantic.BaseModel):
+    """A bit mask indicating which pixels are manipulated / synthesized. A
+    pixel value of ``0`` means "no detection", and a value of ``1`` means
+    "detection".
+    
+    The mask data must be encoded in PNG format, so that typical mask data is
+    compressed effectively. The PNG encoding **should** use "bilevel" mode for
+    maximum compactness. You can use the ``BinaryMask.from_numpy()``
+    function to convert a 0-1 numpy array to a BinaryMask.
+    """
+    mediaType: Literal["image/png"] = pydantic.Field(
+        description="The IETF Media Type (MIME type) of the data."
+    )
+    data: str = pydantic.Field(
+        description="A base64 string encoding of the data.",
+        # Canonical base64 encoding
+        # https://stackoverflow.com/a/64467300/3709935
+        pattern=r"^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/][AQgw]==|[A-Za-z0-9+/]{2}[AEIMQUYcgkosw048]=)?$",
+    )
+
+    @staticmethod
+    def from_numpy(mask: NDArray[np.uint8]) -> BinaryMask:
+        """Convert a 0-1 numpy array to a BinaryMask.
+
+        The numpy data must be in row-major order. That means the first
+        dimension corresponds to **height** and the second dimension corresponds
+        to **width**.
+        """
+        # Convert to "L" (grayscale) then "1" (bilevel) for compact binary representation
+        mask_img = Image.fromarray(mask * 255, mode="L").convert("1", dither=None)
+        mask_img_buffer = io.BytesIO()
+        mask_img.save(mask_img_buffer, format="png")
+        mask_data = base64.b64encode(mask_img_buffer.getbuffer()).decode("utf-8")
+        return BinaryMask(mediaType="image/png", data=mask_data)
 
 
 class ImageRequest(pydantic.BaseModel):
@@ -27,36 +79,6 @@ class Labels(enum.IntEnum):
     LocallySynthesized = 3
 
 
-class LocalizationMask(pydantic.BaseModel):
-    """A bit mask indicating which pixels are manipulated / synthesized.
-
-    A bit value of ``1`` means that the model believes the corresponding pixel
-    has been edited or synthesized (i.e., its label would be non-zero).
-    A bit value of ``0`` means that the model believes the pixel is unaltered.
-
-    The mask ``.width`` and ``.height`` should be the same as the input image.
-    Extra bits at the end of ``.bitsRowMajor`` after the first
-    ``width * height`` bits are **ignored**; for simplicity/efficiency,
-    you should encode your bit mask into a byte array and not worry if the
-    final byte isn't "full", then convert the byte array to base64.
-    """
-
-    width: int32() = pydantic.Field(
-        description="The width of the mask."
-    )
-
-    height: int32() = pydantic.Field(
-        description="The height of the mask."
-    )
-    
-    bitsRowMajor: str = pydantic.Field(
-        description="A base64 string encoding the bit mask in row-major order.",
-        # Canonical base64 encoding
-        # https://stackoverflow.com/a/64467300/3709935
-        pattern=r"^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/][AQgw]==|[A-Za-z0-9+/]{2}[AEIMQUYcgkosw048]=)?$",
-    )
-
-
 class PredictionResponse(pydantic.BaseModel):
     """Response model for synthetic image classification results.
     
@@ -79,7 +101,7 @@ class PredictionResponse(pydantic.BaseModel):
         max_length=4,
     )
 
-    localizationMask: Optional[LocalizationMask] = pydantic.Field(
+    localizationMask: Optional[BinaryMask] = pydantic.Field(
         description="A bit mask localizing predicted edits. Models that are"
         " not capable of localization may omit this field. It may also be"
         " omitted if the predicted label is ``0`` or ``1``, in which case the"

app/services/inference.py

@@ -5,13 +5,14 @@ import os
 import random
 from io import BytesIO
 
+import numpy as np
 import torch
 from PIL import Image
 from transformers import AutoImageProcessor, ResNetForImageClassification  # type: ignore[import-untyped]
 
 from app.core.logging import logger
 from app.services.base import InferenceService
-from app.api.models import ImageRequest, Labels, LocalizationMask, PredictionResponse
+from app.api.models import BinaryMask, ImageRequest, Labels, PredictionResponse
 
 
 class ResNetInferenceService(InferenceService[ImageRequest, PredictionResponse]):
@@ -70,17 +71,22 @@ class ResNetInferenceService(InferenceService[ImageRequest, PredictionResponse])
         with torch.no_grad():
             logits = self.model(**inputs).logits.squeeze()   # pyright: ignore
 
-        # Convert to expected output format. This is for demonstration purposes
+        # Convert the ImageNet output vector of dimension 1000 to the expected
+        # output format. This is for demonstration purposes
         # and obviously will not perform well on the actual task.
         logprobs = torch.nn.functional.log_softmax(logits[:len(Labels)]).tolist()
-        mask_bytes = random.randbytes((width*height + 7) // 8)
-        mask_bits = base64.b64encode(mask_bytes).decode("utf-8")
+
+        # Dummy localization mask: a rectangle approximately in the middle
+        x = image.width // 3
+        y = image.height // 3
+        # Row-major order
+        mask = np.zeros((image.height, image.width), dtype=np.uint8)
+        mask[y:(2*y), x:(2*x)] = 1
+        mask_obj = BinaryMask.from_numpy(mask)
 
         return PredictionResponse(
             logprobs=logprobs,
-            localizationMask=LocalizationMask(
-                width=width, height=height, bitsRowMajor=mask_bits
-            )
+            localizationMask=mask_obj,
         )
 
     @property

makefile

@@ -1,15 +1,50 @@
+VENV ?= venv
+PYTHON ?= $(VENV)/bin/python3
+UVICORN ?= $(VENV)/bin/uvicorn
+DOCKER ?= docker
+IMAGE ?= safe-challenge-2025/example-submission
+GID ?= $(shell id -g)
+UID ?= $(shell id -u)
+
+.PHONY: setup
+setup: $(VENV)/requirements.cpu.txt
+
 .PHONY: download
 download:
 	bash scripts/model_download.bash
 
-.PHONY: build
-build:
-	docker build -t safe-challenge-2025/example-submission:latest .
+.PHONY: serve
+serve:
+	$(UVICORN) main:app
+
+.PHONY: docker-build
+docker-build:
+	docker build -t $(IMAGE) .
+
+.PHONY: docker-run
+docker-run:
+	docker run --rm -it -p 8000:8000 $(IMAGE)
+
+.PHONY: compile
+compile:
+	uv pip compile --python-version 3.12 --upgrade -o requirements.torch.cpu.txt.tmp requirements.torch.cpu.in
+	echo "--index-url https://download.pytorch.org/whl/cpu" > requirements.torch.cpu.txt
+	grep -e '^torch' requirements.torch.cpu.txt.tmp >> requirements.torch.cpu.txt
+	uv pip compile --python-version 3.12 --upgrade -o requirements.cpu.txt requirements.cpu.in
+
+requirements.cpu.txt: requirements.in requirements.torch.cpu.txt | $(VENV)
+	uv pip compile --python-version 3.12 --upgrade -o requirements.cpu.txt requirements.cpu.in
+
+requirements.torch.cpu.txt: requirements.torch.cpu.in | $(VENV)
+	uv pip compile --python-version 3.12 --upgrade -o requirements.torch.cpu.txt.tmp requirements.torch.cpu.in
+	echo "--index-url https://download.pytorch.org/whl/cpu" > requirements.torch.cpu.txt
+	cat requirements.torch.cpu.txt.tmp | grep '^torch' >> requirements.torch.cpu.txt
 
-.PHONY: run
-run:
-	docker run -d --name example-submission -p 8000:8000 safe-challenge-2025/example-submission:latest
+$(VENV)/requirements.cpu.txt: requirements.cpu.txt | $(VENV)
+	VIRTUAL_ENV=$(VENV) uv pip install -r requirements.cpu.txt
+	VIRTUAL_ENV=$(VENV) uv pip install -r requirements.torch.cpu.txt
+	cp -f requirements.cpu.txt $(VENV)/requirements.cpu.txt
+	cp -f requirements.torch.cpu.txt $(VENV)/requirements.torch.cpu.txt
 
-.PHONY: stop
-stop:
-	docker stop example-submission && docker rm example-submission
+$(VENV):
+	uv venv $(VENV)

requirements.cpu.in

@@ -0,0 +1 @@
+-r requirements.in

requirements.cpu.txt

@@ -0,0 +1,458 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile --python-version 3.12 -o requirements.cpu.txt requirements.cpu.in
+absl-py==2.3.1
+    # via dyff-client
+annotated-doc==0.0.4
+    # via fastapi
+annotated-types==0.7.0
+    # via pydantic
+anyio==4.11.0
+    # via
+    #   httpx
+    #   jupyter-server
+    #   starlette
+    #   watchfiles
+argon2-cffi==25.1.0
+    # via jupyter-server
+argon2-cffi-bindings==25.1.0
+    # via argon2-cffi
+arrow==1.4.0
+    # via isoduration
+asttokens==3.0.1
+    # via stack-data
+async-lru==2.0.5
+    # via jupyterlab
+attrs==25.4.0
+    # via
+    #   jsonschema
+    #   referencing
+azure-core==1.36.0
+    # via dyff-client
+babel==2.17.0
+    # via jupyterlab-server
+beautifulsoup4==4.14.2
+    # via
+    #   dyff-audit
+    #   nbconvert
+bleach==6.3.0
+    # via nbconvert
+canonicaljson==2.0.0
+    # via dyff-schema
+certifi==2025.11.12
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+cffi==2.0.0
+    # via argon2-cffi-bindings
+charset-normalizer==3.4.4
+    # via requests
+click==8.3.1
+    # via
+    #   -r requirements.in
+    #   uvicorn
+comm==0.2.3
+    # via ipykernel
+debugpy==1.8.17
+    # via ipykernel
+decorator==5.2.1
+    # via ipython
+defusedxml==0.7.1
+    # via nbconvert
+dnspython==2.8.0
+    # via email-validator
+dyff-audit==0.16.1
+    # via -r requirements.in
+dyff-client==0.23.5
+    # via
+    #   -r requirements.in
+    #   dyff-audit
+dyff-schema==0.39.1
+    # via
+    #   -r requirements.in
+    #   dyff-audit
+    #   dyff-client
+email-validator==2.3.0
+    # via dyff-schema
+executing==2.2.1
+    # via stack-data
+fastapi==0.121.2
+    # via -r requirements.in
+fastjsonschema==2.21.2
+    # via nbformat
+filelock==3.20.0
+    # via
+    #   huggingface-hub
+    #   transformers
+fqdn==1.5.1
+    # via jsonschema
+fsspec==2025.10.0
+    # via huggingface-hub
+google-i18n-address==3.1.1
+    # via dyff-schema
+h11==0.16.0
+    # via
+    #   httpcore
+    #   uvicorn
+hf-xet==1.2.0
+    # via huggingface-hub
+httpcore==1.0.9
+    # via httpx
+httptools==0.7.1
+    # via uvicorn
+httpx==0.28.1
+    # via
+    #   dyff-client
+    #   jupyterlab
+huggingface-hub==0.36.0
+    # via
+    #   tokenizers
+    #   transformers
+hypothesis==6.148.1
+    # via
+    #   dyff-schema
+    #   hypothesis-jsonschema
+hypothesis-jsonschema==0.23.1
+    # via dyff-schema
+idna==3.11
+    # via
+    #   anyio
+    #   email-validator
+    #   httpx
+    #   jsonschema
+    #   requests
+ipykernel==7.1.0
+    # via jupyterlab
+ipython==9.7.0
+    # via ipykernel
+ipython-pygments-lexers==1.1.1
+    # via ipython
+isodate==0.7.2
+    # via dyff-client
+isoduration==20.11.0
+    # via jsonschema
+jedi==0.19.2
+    # via ipython
+jinja2==3.1.6
+    # via
+    #   jupyter-server
+    #   jupyterlab
+    #   jupyterlab-server
+    #   nbconvert
+json5==0.12.1
+    # via jupyterlab-server
+jsonpath-ng==1.7.0
+    # via
+    #   dyff-client
+    #   dyff-schema
+jsonpointer==3.0.0
+    # via jsonschema
+jsonschema==4.25.1
+    # via
+    #   hypothesis-jsonschema
+    #   jupyter-events
+    #   jupyterlab-server
+    #   nbformat
+jsonschema-specifications==2025.9.1
+    # via jsonschema
+jupyter-client==8.6.3
+    # via
+    #   ipykernel
+    #   jupyter-server
+    #   nbclient
+jupyter-core==5.9.1
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+    #   jupyterlab
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+jupyter-events==0.12.0
+    # via jupyter-server
+jupyter-lsp==2.3.0
+    # via jupyterlab
+jupyter-server==2.17.0
+    # via
+    #   jupyter-lsp
+    #   jupyterlab
+    #   jupyterlab-server
+    #   notebook
+    #   notebook-shim
+jupyter-server-terminals==0.5.3
+    # via jupyter-server
+jupyterlab==4.4.10
+    # via notebook
+jupyterlab-pygments==0.3.0
+    # via nbconvert
+jupyterlab-server==2.28.0
+    # via
+    #   jupyterlab
+    #   notebook
+lark==1.3.1
+    # via rfc3987-syntax
+lxml==6.0.2
+    # via dyff-audit
+markupsafe==3.0.3
+    # via
+    #   jinja2
+    #   nbconvert
+matplotlib-inline==0.2.1
+    # via
+    #   ipykernel
+    #   ipython
+mistune==3.1.4
+    # via nbconvert
+nbclient==0.10.2
+    # via nbconvert
+nbconvert==7.16.6
+    # via
+    #   dyff-audit
+    #   jupyter-server
+nbformat==5.10.4
+    # via
+    #   dyff-audit
+    #   jupyter-server
+    #   nbclient
+    #   nbconvert
+nest-asyncio==1.6.0
+    # via ipykernel
+notebook==7.4.7
+    # via dyff-audit
+notebook-shim==0.2.4
+    # via
+    #   jupyterlab
+    #   notebook
+numpy==1.26.4
+    # via
+    #   -r requirements.in
+    #   dyff-audit
+    #   dyff-client
+    #   dyff-schema
+    #   pandas
+    #   transformers
+packaging==25.0
+    # via
+    #   huggingface-hub
+    #   ipykernel
+    #   jupyter-events
+    #   jupyter-server
+    #   jupyterlab
+    #   jupyterlab-server
+    #   nbconvert
+    #   transformers
+pandas==2.3.3
+    # via
+    #   -r requirements.in
+    #   dyff-audit
+    #   dyff-client
+pandocfilters==1.5.1
+    # via nbconvert
+parso==0.8.5
+    # via jedi
+pexpect==4.9.0
+    # via ipython
+pillow==12.0.0
+    # via -r requirements.in
+platformdirs==4.5.0
+    # via jupyter-core
+ply==3.11
+    # via jsonpath-ng
+prometheus-client==0.23.1
+    # via jupyter-server
+prompt-toolkit==3.0.52
+    # via ipython
+psutil==7.1.3
+    # via ipykernel
+ptyprocess==0.7.0
+    # via
+    #   pexpect
+    #   terminado
+pure-eval==0.2.3
+    # via stack-data
+pyarrow==22.0.0
+    # via
+    #   -r requirements.in
+    #   dyff-audit
+    #   dyff-client
+    #   dyff-schema
+pycparser==2.23
+    # via cffi
+pydantic==2.12.4
+    # via
+    #   -r requirements.in
+    #   dyff-audit
+    #   dyff-client
+    #   dyff-schema
+    #   fastapi
+    #   pydantic-settings
+pydantic-core==2.41.5
+    # via pydantic
+pydantic-settings==2.12.0
+    # via -r requirements.in
+pygments==2.19.2
+    # via
+    #   ipython
+    #   ipython-pygments-lexers
+    #   nbconvert
+python-dateutil==2.9.0.post0
+    # via
+    #   arrow
+    #   jupyter-client
+    #   pandas
+python-dotenv==1.2.1
+    # via
+    #   -r requirements.in
+    #   pydantic-settings
+    #   uvicorn
+python-json-logger==4.0.0
+    # via jupyter-events
+python-multipart==0.0.20
+    # via -r requirements.in
+pytz==2025.2
+    # via pandas
+pyyaml==6.0.3
+    # via
+    #   huggingface-hub
+    #   jupyter-events
+    #   transformers
+    #   uvicorn
+pyzmq==27.1.0
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+referencing==0.37.0
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+    #   jupyter-events
+regex==2025.11.3
+    # via transformers
+requests==2.32.5
+    # via
+    #   -r requirements.in
+    #   azure-core
+    #   google-i18n-address
+    #   huggingface-hub
+    #   jupyterlab-server
+    #   transformers
+rfc3339-validator==0.1.4
+    # via
+    #   jsonschema
+    #   jupyter-events
+rfc3986-validator==0.1.1
+    # via
+    #   jsonschema
+    #   jupyter-events
+rfc3987-syntax==1.1.0
+    # via jsonschema
+rpds-py==0.29.0
+    # via
+    #   jsonschema
+    #   referencing
+ruamel-yaml==0.18.16
+    # via dyff-audit
+ruamel-yaml-clib==0.2.15
+    # via ruamel-yaml
+safetensors==0.6.2
+    # via transformers
+send2trash==1.8.3
+    # via jupyter-server
+setuptools==80.9.0
+    # via jupyterlab
+six==1.17.0
+    # via
+    #   python-dateutil
+    #   rfc3339-validator
+sniffio==1.3.1
+    # via anyio
+sortedcontainers==2.4.0
+    # via hypothesis
+soupsieve==2.8
+    # via beautifulsoup4
+stack-data==0.6.3
+    # via ipython
+starlette==0.49.3
+    # via fastapi
+terminado==0.18.1
+    # via
+    #   jupyter-server
+    #   jupyter-server-terminals
+tinycss2==1.4.0
+    # via bleach
+tokenizers==0.22.1
+    # via transformers
+tornado==6.5.2
+    # via
+    #   ipykernel
+    #   jupyter-client
+    #   jupyter-server
+    #   jupyterlab
+    #   notebook
+    #   terminado
+tqdm==4.67.1
+    # via
+    #   dyff-client
+    #   huggingface-hub
+    #   transformers
+traitlets==5.14.3
+    # via
+    #   ipykernel
+    #   ipython
+    #   jupyter-client
+    #   jupyter-core
+    #   jupyter-events
+    #   jupyter-server
+    #   jupyterlab
+    #   matplotlib-inline
+    #   nbclient
+    #   nbconvert
+    #   nbformat
+transformers==4.57.1
+    # via -r requirements.in
+typing-extensions==4.15.0
+    # via
+    #   anyio
+    #   azure-core
+    #   beautifulsoup4
+    #   fastapi
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   referencing
+    #   starlette
+    #   typing-inspection
+typing-inspection==0.4.2
+    # via
+    #   pydantic
+    #   pydantic-settings
+tzdata==2025.2
+    # via
+    #   arrow
+    #   pandas
+uri-template==1.3.0
+    # via jsonschema
+urllib3==2.5.0
+    # via requests
+uvicorn==0.38.0
+    # via -r requirements.in
+uvloop==0.22.1
+    # via uvicorn
+watchfiles==1.1.1
+    # via uvicorn
+wcwidth==0.2.14
+    # via prompt-toolkit
+webcolors==25.10.0
+    # via jsonschema
+webencodings==0.5.1
+    # via
+    #   bleach
+    #   tinycss2
+websocket-client==1.9.0
+    # via jupyter-server
+websockets==15.0.1
+    # via
+    #   dyff-client
+    #   uvicorn

requirements.in

@@ -1,20 +1,23 @@
-dyff
+dyff-audit
+dyff-client
+dyff-schema>=0.39.1
+
+click
 
 # Web framework
-fastapi==0.104.1
-uvicorn[standard]==0.24.0
+fastapi
+uvicorn[standard]
 
 # Configuration management
-pydantic==2.5.0
-pydantic-settings==2.0.3
-python-dotenv==0.21.0
+pydantic>=2.0.0,<3.0.0
+pydantic-settings
+python-dotenv
 
 # File upload handling
-python-multipart==0.0.6
+python-multipart
 
 # ML/AI dependencies (newly added)
 transformers>=4.35.0
-torch>=2.4.0  # Newer PyTorch with NumPy 2.x support
 pillow>=10.0.0
 
 # Dataset generation and testing

requirements.torch.cpu.in

@@ -0,0 +1,3 @@
+--index-url https://download.pytorch.org/whl/cpu
+torch
+torchvision

requirements.torch.cpu.txt

@@ -0,0 +1,3 @@
+--index-url https://download.pytorch.org/whl/cpu
+torch==2.9.1+cpu
+torchvision==0.24.1+cpu

requirements.txt

@@ -1,5 +1,5 @@
 # This file was autogenerated by uv via the following command:
-#    uv pip compile requirements.in
+#    uv pip compile --python-version 3.12 -o requirements.txt requirements.in
 absl-py==2.3.1
     # via dyff-client
 annotated-types==0.7.0
@@ -17,7 +17,7 @@ argon2-cffi-bindings==25.1.0
     # via argon2-cffi
 arrow==1.4.0
     # via isoduration
-asttokens==3.0.0
+asttokens==3.0.1
     # via stack-data
 async-lru==2.0.5
     # via jupyterlab
@@ -46,8 +46,10 @@ cffi==2.0.0
     # via argon2-cffi-bindings
 charset-normalizer==3.4.4
     # via requests
-click==8.3.0
-    # via uvicorn
+click==8.3.1
+    # via
+    #   -r requirements.in
+    #   uvicorn
 comm==0.2.3
     # via ipykernel
 debugpy==1.8.17
@@ -58,17 +60,15 @@ defusedxml==0.7.1
     # via nbconvert
 dnspython==2.8.0
     # via email-validator
-# dyff==0.36.1
-    # via -r requirements.in
 dyff-audit==0.16.1
-    # via dyff
+    # via -r requirements.in
 dyff-client==0.23.5
     # via
-    #   dyff
+    #   -r requirements.in
     #   dyff-audit
 dyff-schema==0.39.1
     # via
-    #   dyff
+    #   -r requirements.in
     #   dyff-audit
     #   dyff-client
 email-validator==2.3.0
@@ -110,7 +110,7 @@ huggingface-hub==0.36.0
     # via
     #   tokenizers
     #   transformers
-hypothesis==6.147.0
+hypothesis==6.148.1
     # via
     #   dyff-schema
     #   hypothesis-jsonschema
@@ -394,13 +394,13 @@ rfc3986-validator==0.1.1
     #   jupyter-events
 rfc3987-syntax==1.1.0
     # via jsonschema
-rpds-py==0.28.0
+rpds-py==0.29.0
     # via
     #   jsonschema
     #   referencing
 ruamel-yaml==0.18.16
     # via dyff-audit
-ruamel-yaml-clib==0.2.14
+ruamel-yaml-clib==0.2.15
     # via ruamel-yaml
 safetensors==0.6.2
     # via transformers

response.json

@@ -0,0 +1 @@
+{"logprobs":[-0.859380304813385,-1.2701971530914307,-2.1918208599090576,-1.69235098361969],"localizationMask":{"mediaType":"image/png","data":"iVBORw0KGgoAAAANSUhEUgAAA8AAAAKDAQAAAAD9Fl5AAAAAu0lEQVR4nO3NsREAMAgDMWD/nZMVKEwn1T5/FQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMCl3g5f+HC24TRhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWFhYWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAj70gwKsTlmdBwAAAABJRU5ErkJggg=="}}

upload_model.py

@@ -0,0 +1,93 @@
+# SPDX-FileCopyrightText: 2025 UL Research Institutes
+# SPDX-License-Identifier: Apache-2.0
+
+import sys
+import time
+from pathlib import Path
+
+import click
+
+from dyff.client import Client
+from dyff.schema.platform import *
+from dyff.schema.requests import *
+
+from app.api.models import PredictionResponse
+
+# ----------------------------------------------------------------------------
+
+WORKDIR = Path(__file__).resolve().parent
+
+
+@click.command()
+@click.option(
+    "--account",
+    type=str,
+    required=True,
+    help="Your account ID",
+)
+@click.option(
+    "--name",
+    type=str,
+    required=True,
+    help="The name of your detector model. For display and querying purposes only.",
+)
+@click.option(
+    "--image",
+    type=str,
+    required=True,
+    help="The Docker image to upload. Must exist in your local Docker deamon.",
+)
+@click.option(
+    "--endpoint",
+    type=str,
+    default="predict",
+    help="The endpoint to call on your model to make a prediction.",
+)
+def main(account: str, name: str, image: str, endpoint: str) -> None:
+    dyffapi = Client()
+
+    # You can set these to a known ID to skip that step
+    artifact_id = None
+    service_id = None
+
+    # Upload the image
+    if artifact_id is None:
+        # Create an Artifact resource
+        artifact = dyffapi.artifacts.create(ArtifactCreateRequest(account=account))
+        click.echo(f"artifact_id = \"{artifact.id}\"")
+        time.sleep(5)
+        # Push the image from the local Docker daemon
+        dyffapi.artifacts.push(artifact, source=f"docker-daemon:{image}")
+        time.sleep(5)
+        # Indicate that we're done pushing
+        dyffapi.artifacts.finalize(artifact.id)
+    else:
+        artifact = dyffapi.artifacts.get(artifact_id)
+        assert artifact is not None
+
+    # Create a runnable InferenceService
+    if service_id is None:
+        # Don't change this
+        service_request = InferenceServiceCreateRequest(
+            account=account,
+            name=name,
+            model=None,
+            runner=InferenceServiceRunner(
+                kind=InferenceServiceRunnerKind.CONTAINER,
+                imageRef=EntityIdentifier.of(artifact),
+                resources=ModelResources(),
+            ),
+            interface=InferenceInterface(
+                endpoint=endpoint,
+                outputSchema=DataSchema.make_output_schema(PredictionResponse),
+            ),
+        )
+        service = dyffapi.inferenceservices.create(service_request)
+        click.echo(f"service_id = \"{service.id}\"")
+    else:
+        service = dyffapi.inferenceservices.get(service_id)
+        assert service is not None
+
+
+if __name__ == "__main__":
+    main()