foodsnow solution

autotests/helm/values.yaml

@@ -25,8 +25,8 @@ global:
     activeDeadlineSeconds: 3600 # 1h
 
   env:
-    PARTICIPANT_NAME: <REPLACE_WITH_USERNAME>
-    api_host: http://inca-smc-mlops-challenge-solution.default.svc.cluster.local/<REPLACE_WITH_ENDPOINT>
+    PARTICIPANT_NAME: foodsnow
+    api_host: http://inca-smc-mlops-challenge-solution.default.svc.cluster.local/process
 
     # K6, do not edit!
     K6_PROMETHEUS_RW_SERVER_URL: http://kube-prometheus-stack-prometheus.monitoring.svc.cluster.local:9090/api/v1/write

solution/Dockerfile

@@ -0,0 +1,12 @@
+FROM pytorch/pytorch:latest
+
+RUN mkdir /code
+
+COPY ./requirements.txt /code/requirements.txt
+COPY ./app.py /code/app.py
+COPY ./models.py /code/models.py
+
+WORKDIR /code
+RUN pip install -r /code/requirements.txt
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

solution/app.py

@@ -0,0 +1,14 @@
+from fastapi import FastAPI
+from starlette.requests import Request
+
+import models
+
+
+pipelines = models.get_pipelines()
+app = FastAPI()
+
+
+@app.post("/process")
+async def predict(request: Request):
+    text = (await request.body()).decode()
+    return await pipelines.process(text)

solution/docker-compose.yml

@@ -0,0 +1,20 @@
+version: "3.9"
+services:
+  web:
+    build: .
+    ports:
+      - "8000:8000"
+    deploy:
+      resources:
+        reservations:
+          devices:
+          - driver: nvidia
+            device_ids: ['0']
+            capabilities: [gpu]
+
+networks:
+  default:
+    driver: bridge
+    ipam:
+      config:
+        - subnet: 172.16.57.0/24

solution/helm/envs/foodsnow.yaml

@@ -0,0 +1,35 @@
+global:
+  # add any variables you need in format `key: value`
+  # variables will be available in the container as environment variables
+  env:
+    EXAMPLE: "example"
+
+  # not sure if it's needed, overall the server should use 1 gpu by default
+  resources:
+      limits:
+        nvidia.com/gpu: 1
+
+  # change 8000 to your application target port
+  pod:
+    ports:
+      - name: http
+        containerPort: 8000
+        protocol: TCP
+  service:
+    targetPort: 8000
+
+  # add any configmap data you need
+  # configmaps will be mounted to /workspace/<configmap-name>
+  config:
+    mount:
+      path: /workspace
+    # Map of configmap entries. Entries might be of types: string, map
+    data:
+      conf1.yaml:
+        key1:
+          key11: value11
+          key12: value12
+        key2: value2
+      conf2.yaml:
+        key1: value1
+        key2: value2

solution/models.py

@@ -0,0 +1,70 @@
+import asyncio
+
+from transformers import pipeline
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+
+
+MODELS = {
+    "cardiffnlp": "cardiffnlp/twitter-xlm-roberta-base-sentiment",
+    "ivanlau": "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base",
+    "svalabs": "svalabs/twitter-xlm-roberta-crypto-spam",
+    "EIStakovskii": "EIStakovskii/xlm_roberta_base_multilingual_toxicity_classifier_plus",
+    "jy46604790": "jy46604790/Fake-News-Bert-Detect",
+}
+
+class Pipelines:
+
+    pipelines = {}
+
+    def register_model(self, model: str, model_path: str, tokenizer_path: str):
+        try:
+            model_instance = AutoModelForSequenceClassification.from_pretrained(model_path)
+            tokenizer_instance = AutoTokenizer.from_pretrained(tokenizer_path)
+            pipe = pipeline(
+                "text-classification",
+                model=model_instance,
+                tokenizer=tokenizer_instance,
+                device=0
+            )
+        except Exception as e:
+            print("Couldn't register a model", model)
+            raise(e)
+
+        self.pipelines[model] = pipe
+
+
+    async def query_model(self, model: str, text: str) -> str:
+        if model not in self.pipelines:
+            return None
+
+        return model, self.pipelines[model](text)[0]
+
+
+    async def process(self, text: str):
+        futures = []
+        for model in MODELS:
+            futures.append(
+                asyncio.ensure_future(self.query_model(model, text))
+            )
+        results = await asyncio.gather(*futures)
+
+        output = {
+            model: result for model, result in results
+        }
+        if "cardiffnlp" in output:
+            output["cardiffnlp"]["label"] = output["cardiffnlp"]["label"].upper()
+
+        return output
+
+
+def get_pipelines() -> Pipelines:
+    pipelines = Pipelines()
+    for model, path in MODELS.items():
+        pipelines.register_model(
+            model=model,
+            model_path=path,
+            tokenizer_path=path,
+        )
+    return pipelines
+

solution/requirements.txt

@@ -0,0 +1,47 @@
+anyio==3.6.2
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+cmake==3.26.3
+fastapi==0.95.1
+filelock==3.12.0
+fsspec==2023.5.0
+h11==0.14.0
+huggingface-hub==0.14.1
+idna==3.4
+Jinja2==3.1.2
+lit==16.0.3
+MarkupSafe==2.1.2
+mpmath==1.3.0
+networkx==3.1
+numpy==1.24.3
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-cupti-cu11==11.7.101
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+nvidia-cufft-cu11==10.9.0.58
+nvidia-curand-cu11==10.2.10.91
+nvidia-cusolver-cu11==11.4.0.1
+nvidia-cusparse-cu11==11.7.4.91
+nvidia-nccl-cu11==2.14.3
+nvidia-nvtx-cu11==11.7.91
+packaging==23.1
+protobuf==3.20.3
+pydantic==1.10.7
+PyYAML==6.0
+regex==2023.5.5
+requests==2.30.0
+scipy==1.10.1
+sentencepiece==0.1.99
+sniffio==1.3.0
+starlette==0.26.1
+sympy==1.11.1
+tokenizers==0.13.3
+torch==2.0.1
+tqdm==4.65.0
+transformers==4.28.1
+triton==2.0.0
+typing_extensions==4.5.0
+urllib3==2.0.2
+uvicorn==0.22.0