infernet-1.0.0 update
This commit is contained in:
arshan-ritual
@ -7,12 +7,15 @@ ENV PYTHONDONTWRITEBYTECODE 1
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ENV PIP_NO_CACHE_DIR 1
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ENV RUNTIME docker
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ENV PYTHONPATH src
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ARG index_url
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ENV UV_EXTRA_INDEX_URL ${index_url}
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RUN apt-get update
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RUN apt-get install -y git curl
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# install uv
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ADD --chmod=755 https://astral.sh/uv/install.sh /install.sh
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ADD https://astral.sh/uv/install.sh /install.sh
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RUN chmod 755 /install.sh
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RUN /install.sh && rm /install.sh
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COPY src/requirements.txt .
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@ -5,7 +5,7 @@ TAG := $(DOCKER_ORG)/example-$(EXAMPLE_NAME)-infernet:latest
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.phony: build run build-multiplatform
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build:
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@docker build -t $(TAG) .
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@docker build -t $(TAG) --build-arg index_url=$(index_url) .
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run:
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docker run -p 3000:3000 --env-file tgi-llm.env $(TAG)
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@ -1,52 +1,46 @@
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{
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"log_path": "infernet_node.log",
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"server": {
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"port": 4000
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},
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"chain": {
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"enabled": true,
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"trail_head_blocks": 0,
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"rpc_url": "http://host.docker.internal:8545",
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"coordinator_address": "0x5FbDB2315678afecb367f032d93F642f64180aa3",
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"wallet": {
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"max_gas_limit": 4000000,
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"private_key": "0x59c6995e998f97a5a0044966f0945389dc9e86dae88c7a8412f4603b6b78690d"
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}
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},
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"startup_wait": 1.0,
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"docker": {
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"username": "your-username",
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"password": ""
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},
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"redis": {
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"host": "redis",
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"port": 6379
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},
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"forward_stats": true,
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"containers": [
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{
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"id": "tgi-llm",
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"image": "ritualnetwork/example-tgi-llm-infernet:latest",
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"external": true,
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"port": "3000",
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"allowed_delegate_addresses": [],
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"allowed_addresses": [],
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"allowed_ips": [],
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"command": "--bind=0.0.0.0:3000 --workers=2",
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"env": {
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"TGI_SERVICE_URL": "http://{your_service_ip}:{your_service_port}"
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}
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"log_path": "infernet_node.log",
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"server": {
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"port": 4000
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},
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{
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"id": "anvil-node",
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"image": "ritualnetwork/infernet-anvil:0.0.0",
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"external": true,
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"port": "8545",
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"allowed_delegate_addresses": [],
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"allowed_addresses": [],
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"allowed_ips": [],
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"command": "",
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"env": {}
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}
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]
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"chain": {
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"enabled": true,
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"trail_head_blocks": 0,
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"rpc_url": "http://host.docker.internal:8545",
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"registry_address": "0x663F3ad617193148711d28f5334eE4Ed07016602",
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"wallet": {
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"max_gas_limit": 4000000,
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"private_key": "0x59c6995e998f97a5a0044966f0945389dc9e86dae88c7a8412f4603b6b78690d"
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}
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},
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"startup_wait": 1.0,
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"docker": {
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"username": "your-username",
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"password": ""
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},
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"redis": {
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"host": "redis",
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"port": 6379
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},
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"forward_stats": true,
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"snapshot_sync": {
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"sleep": 3,
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"batch_size": 100
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},
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"containers": [
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{
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"id": "tgi-llm",
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"image": "ritualnetwork/example-tgi-llm-infernet:latest",
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"external": true,
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"port": "3000",
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"allowed_delegate_addresses": [],
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"allowed_addresses": [],
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"allowed_ips": [],
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"command": "--bind=0.0.0.0:3000 --workers=2",
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"env": {
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"TGI_SERVICE_URL": "http://{your_service_ip}:{your_service_port}"
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},
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"accepted_payments": {}
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}
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]
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}
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@ -2,10 +2,11 @@ import logging
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import os
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from typing import Any, cast
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from eth_abi import decode, encode # type: ignore
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from infernet_ml.utils.service_models import InfernetInput, InfernetInputSource
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from eth_abi.abi import decode, encode
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from infernet_ml.utils.service_models import InfernetInput, JobLocation
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from infernet_ml.workflows.inference.tgi_client_inference_workflow import (
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TGIClientInferenceWorkflow,
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TgiInferenceRequest,
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)
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from quart import Quart, request
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@ -16,7 +17,7 @@ def create_app() -> Quart:
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app = Quart(__name__)
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workflow = TGIClientInferenceWorkflow(
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server_url=cast(str, os.environ.get("TGI_SERVICE_URL"))
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server_url=os.environ["TGI_SERVICE_URL"],
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)
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workflow.setup()
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@ -38,42 +39,51 @@ def create_app() -> Quart:
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"""
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infernet_input: InfernetInput = InfernetInput(**req_data)
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if infernet_input.source == InfernetInputSource.OFFCHAIN:
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prompt = cast(dict[str, Any], infernet_input.data).get("prompt")
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else:
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# On-chain requests are sent as a generalized hex-string which we will
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# decode to the appropriate format.
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(prompt,) = decode(
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["string"], bytes.fromhex(cast(str, infernet_input.data))
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)
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match infernet_input:
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case InfernetInput(source=JobLocation.OFFCHAIN):
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prompt = cast(dict[str, Any], infernet_input.data).get("prompt")
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case InfernetInput(source=JobLocation.ONCHAIN):
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# On-chain requests are sent as a generalized hex-string which we will
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# decode to the appropriate format.
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(prompt,) = decode(
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["string"], bytes.fromhex(cast(str, infernet_input.data))
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)
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case _:
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raise ValueError("Invalid source")
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result: dict[str, Any] = workflow.inference({"text": prompt})
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result: dict[str, Any] = workflow.inference(
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TgiInferenceRequest(text=cast(str, prompt))
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)
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if infernet_input.source == InfernetInputSource.OFFCHAIN:
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"""
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In case of an off-chain request, the result is returned as a dict. The
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infernet node expects a dict format.
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"""
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return {"data": result}
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else:
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"""
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In case of an on-chain request, the result is returned in the format:
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{
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"raw_input": str,
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"processed_input": str,
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"raw_output": str,
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"processed_output": str,
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"proof": str,
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}
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refer to: https://docs.ritual.net/infernet/node/containers for more info.
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"""
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return {
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"raw_input": "",
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"processed_input": "",
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"raw_output": encode(["string"], [result]).hex(),
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"processed_output": "",
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"proof": "",
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}
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match infernet_input:
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case InfernetInput(destination=JobLocation.OFFCHAIN):
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"""
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In case of an off-chain request, the result is returned as a dict. The
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infernet node expects a dict format.
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"""
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return {"data": result}
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case InfernetInput(destination=JobLocation.ONCHAIN):
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"""
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In case of an on-chain request, the result is returned in the format:
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{
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"raw_input": str,
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"processed_input": str,
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"raw_output": str,
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"processed_output": str,
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"proof": str,
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}
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refer to: https://docs.ritual.net/infernet/node/containers for more
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info.
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"""
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return {
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"raw_input": "",
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"processed_input": "",
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"raw_output": encode(["string"], [result]).hex(),
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"processed_output": "",
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"proof": "",
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}
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case _:
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raise ValueError("Invalid destination")
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return app
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@ -1,6 +1,5 @@
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quart==0.19.4
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infernet_ml==0.1.0
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PyArweave @ git+https://github.com/ritual-net/pyarweave.git
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infernet-ml==1.0.0
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infernet-ml[tgi_inference]==1.0.0
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web3==6.15.0
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retry2==0.9.5
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text-generation==0.6.1
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