feat: publishing infernet-container-starter v0.2.0

projects/gpt4/container/.gitignore

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+sample-gpt3.env
+config.json

projects/gpt4/container/Dockerfile

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+FROM python:3.11-slim as builder
+
+WORKDIR /app
+
+ENV PYTHONUNBUFFERED 1
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PIP_NO_CACHE_DIR 1
+ENV RUNTIME docker
+ENV PYTHONPATH src
+
+RUN apt-get update
+RUN apt-get install -y git curl
+
+# install uv
+ADD --chmod=755 https://astral.sh/uv/install.sh /install.sh
+RUN /install.sh && rm /install.sh
+
+COPY src/requirements.txt .
+
+RUN /root/.cargo/bin/uv pip install --system --no-cache -r requirements.txt
+
+COPY src src
+
+ENTRYPOINT ["hypercorn", "app:create_app()"]
+CMD ["-b", "0.0.0.0:3000"]

projects/gpt4/container/Makefile

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+DOCKER_ORG := ritualnetwork
+EXAMPLE_NAME := gpt4
+TAG := $(DOCKER_ORG)/example-$(EXAMPLE_NAME)-infernet:latest
+
+.phony: build run build-multiplatform try-prompt
+
+build:
+	mkdir -p root-config
+	@docker build -t $(TAG) .
+
+run: build
+	@docker run --env-file $(EXAMPLE_NAME).env -p 3000:3000 $(TAG)
+
+# You may need to set up a docker builder, to do so run:
+# docker buildx create --name mybuilder --bootstrap --use
+# refer to https://docs.docker.com/build/building/multi-platform/#building-multi-platform-images for more info
+build-multiplatform:
+	docker buildx build --platform linux/amd64,linux/arm64 -t $(TAG) --push .

projects/gpt4/container/README.md

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+# GPT 4
+In this example, we run a minimalist container that makes use of our closed-source model
+workflow: `CSSInferenceWorkflow`. Refer to [src/app.py](src/app.py) for the
+implementation of the quart application.
+
+## Requirements
+To use the model you'll need to have an OpenAI api key. Get one at
+[OpenAI](https://openai.com/)'s website.
+
+## Run the Container
+
+```bash
+make run
+```
+
+## Test the Container
+```bash
+curl -X POST localhost:3000/service_output -H "Content-Type: application/json" \
+  -d '{"source": 1, "data": {"text": "can shrimps actually fry rice?"}}'
+```

projects/gpt4/container/config.sample.json

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+{
+  "log_path": "infernet_node.log",
+  "server": {
+    "port": 4000
+  },
+  "chain": {
+    "enabled": true,
+    "trail_head_blocks": 0,
+    "rpc_url": "http://host.docker.internal:8545",
+    "coordinator_address": "0x5FbDB2315678afecb367f032d93F642f64180aa3",
+    "wallet": {
+      "max_gas_limit": 4000000,
+      "private_key": "0x59c6995e998f97a5a0044966f0945389dc9e86dae88c7a8412f4603b6b78690d"
+    }
+  },
+  "startup_wait": 1.0,
+  "docker": {
+    "username": "your-username",
+    "password": ""
+  },
+  "redis": {
+    "host": "redis",
+    "port": 6379
+  },
+  "forward_stats": true,
+  "containers": [
+    {
+      "id": "gpt4",
+      "image": "ritualnetwork/example-gpt4-infernet:latest",
+      "external": true,
+      "port": "3000",
+      "allowed_delegate_addresses": [],
+      "allowed_addresses": [],
+      "allowed_ips": [],
+      "command": "--bind=0.0.0.0:3000 --workers=2",
+      "env": {
+        "OPENAI_API_KEY": "barabeem baraboom"
+      }
+    },
+    {
+      "id": "anvil-node",
+      "image": "ritualnetwork/infernet-anvil:0.0.0",
+      "external": true,
+      "port": "8545",
+      "allowed_delegate_addresses": [],
+      "allowed_addresses": [],
+      "allowed_ips": [],
+      "command": "",
+      "env": {}
+    }
+  ]
+}

projects/gpt4/container/gpt4.env.sample

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+OPENAI_API_KEY=

projects/gpt4/container/src/app.py

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+import logging
+from typing import Any, cast
+
+from eth_abi import decode, encode  # type: ignore
+from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
+from infernet_ml.workflows.inference.css_inference_workflow import CSSInferenceWorkflow
+from quart import Quart, request
+
+log = logging.getLogger(__name__)
+
+
+def create_app() -> Quart:
+    app = Quart(__name__)
+
+    workflow = CSSInferenceWorkflow(provider="OPENAI", endpoint="completions")
+
+    workflow.setup()
+
+    @app.route("/")
+    def index() -> str:
+        """
+        Utility endpoint to check if the service is running.
+        """
+        return "GPT4 Example Program"
+
+    @app.route("/service_output", methods=["POST"])
+    async def inference() -> dict[str, Any]:
+        req_data = await request.get_json()
+        """
+        InfernetInput has the format:
+            source: (0 on-chain, 1 off-chain)
+            data: dict[str, Any]
+        """
+        infernet_input: InfernetInput = InfernetInput(**req_data)
+
+        if infernet_input.source == InfernetInputSource.OFFCHAIN:
+            prompt = cast(dict[str, Any], infernet_input.data).get("prompt")
+        else:
+            # On-chain requests are sent as a generalized hex-string which we will
+            # decode to the appropriate format.
+            (prompt,) = decode(
+                ["string"], bytes.fromhex(cast(str, infernet_input.data))
+            )
+
+        result: dict[str, Any] = workflow.inference(
+            {
+                "model": "gpt-4-0613",
+                "params": {
+                    "endpoint": "completions",
+                    "messages": [
+                        {"role": "system", "content": "You are a helpful assistant."},
+                        {"role": "user", "content": prompt},
+                    ],
+                },
+            }
+        )
+
+        if infernet_input.source == InfernetInputSource.OFFCHAIN:
+            """
+            In case of an off-chain request, the result is returned as is.
+            """
+            return {"message": result}
+        else:
+            """
+            In case of an on-chain request, the result is returned in the format:
+            {
+                "raw_input": str,
+                "processed_input": str,
+                "raw_output": str,
+                "processed_output": str,
+                "proof": str,
+            }
+            refer to: https://docs.ritual.net/infernet/node/containers for more info.
+            """
+            return {
+                "raw_input": "",
+                "processed_input": "",
+                "raw_output": encode(["string"], [result]).hex(),
+                "processed_output": "",
+                "proof": "",
+            }
+
+    return app
+
+
+if __name__ == "__main__":
+    """
+    Utility to run the app locally. For development purposes only.
+    """
+    create_app().run(port=3000)

projects/gpt4/container/src/requirements.txt

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+quart==0.19.4
+infernet_ml==0.1.0
+PyArweave @ git+https://github.com/ritual-net/pyarweave.git
+web3==6.15.0
+retry2==0.9.5