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  <div class="hero-eyebrow">Page 4 of 4 · <a href="python_101.html" style="color:#fde68a;">← Hub</a></div>
  <h1>FastAPI &amp; <span class="py-word">engineering</span></h1>
  <p class="hero-desc">Build HTTP APIs with automatic OpenAPI docs, validated payloads, and async I/O. Patterns for loading PyTorch or sklearn models, batch prediction, and how this connects to batch/stream data pipelines in production.</p>
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    <span class="chip chip-gold">FastAPI</span>
    <span class="chip chip-gold">Pydantic v2</span>
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<div class="sec-header" id="fastapi">
  <span class="sec-num">01</span>
  <h2>FastAPI basics</h2>
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      <h3>App, routes, and automatic OpenAPI</h3>
      <p>Run with uvicorn for ASGI</p>
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    <p class="prose">FastAPI is built on Starlette + Pydantic. Type hints on parameters drive validation and generated <strong>/docs</strong>. Use APIRouter to split large services.</p>
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      <pre><span class="kw">from</span> fastapi <span class="kw">import</span> FastAPI

app = FastAPI(title=<span class="str">"Scoring API"</span>, version=<span class="str">"1.0.0"</span>)

@app.get(<span class="str">"/health"</span>)
<span class="kw">def</span> <span class="fn">health</span>() -&gt; dict[<span class="fn">str</span>, <span class="fn">str</span>]:
    <span class="kw">return</span> {<span class="str">"status"</span>: <span class="str">"ok"</span>}</pre>
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    <div class="alert alert-tip"><span class="alert-icon">✓</span><div class="alert-body">Run locally: <code>uvicorn module:app --reload</code>. Behind production, put gunicorn+uvicorn workers or a managed container platform in front.</div></div>
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  <h2>Pydantic models</h2>
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      <h3>Request / response schemas</h3>
      <p>Validation before your handler runs</p>
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      <pre><span class="kw">from</span> pydantic <span class="kw">import</span> BaseModel, Field

<span class="kw">class</span> <span class="fn">PredictIn</span>(BaseModel):
    features: list[float] = Field(..., min_length=<span class="num">1</span>, max_length=<span class="num">10_000</span>)

<span class="kw">class</span> <span class="fn">PredictOut</span>(BaseModel):
    score: float

@app.post(<span class="str">"/predict"</span>, response_model=PredictOut)
<span class="kw">def</span> <span class="fn">predict</span>(body: PredictIn) -&gt; PredictOut:
    <span class="cm"># body.features is already validated</span>
    ...</pre>
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  <span class="sec-num">03</span>
  <h2>Serving ML models</h2>
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<div class="topic" data-search="torch load state_dict inference batch singleton lifespan">
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      <h3>Load once, infer many times</h3>
      <p>Lifespan hooks &amp; thread safety</p>
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    <p class="prose">Load model weights when the process starts (FastAPI <code>lifespan</code> context), not per request. For PyTorch, set <code>model.eval()</code> and wrap inference in <code>torch.no_grad()</code>. Batch inputs when possible to amortize GPU kernel launch.</p>
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      <pre><span class="kw">from</span> contextlib <span class="kw">import</span> asynccontextmanager

@asynccontextmanager
<span class="kw">async</span> <span class="kw">def</span> <span class="fn">lifespan</span>(app: FastAPI):
    <span class="cm"># load model, connect pools</span>
    app.state.model = load_model(<span class="str">"weights.pt"</span>)
    <span class="kw">yield</span>
    <span class="cm"># cleanup</span>

app = FastAPI(lifespan=lifespan)</pre>
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    <div class="alert alert-warn"><span class="alert-icon">⚠</span><div class="alert-body">If multiple workers process share the same GPU, coordinate memory—often one model replica per GPU. For sklearn, joblib-loaded pipelines are typical; watch pickling compatibility across versions.</div></div>
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  <span class="sec-num">04</span>
  <h2>Data engineering touchpoints</h2>
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      <h3>Beyond the API layer</h3>
      <p>Where features and labels come from</p>
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        <div class="info-card-title">Batch</div>
        <p>Scheduled ETL/ELT (Airflow, Dagster, dbt) materializes tables your API or batch scorer reads. <strong>Polars</strong> and <strong>PySpark</strong> handle large extracts before they touch FastAPI.</p>
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      <div class="info-card">
        <div class="info-card-title">Streaming</div>
        <p>Kafka / Redpanda + consumers for near-real-time features; still often land in a store the API queries. Design <strong>idempotent</strong> consumers and monitor lag.</p>
      </div>
    </div>
    <p class="prose">Operationally: structured logging (no secrets/PII in clear text), metrics (latency, error rate), traces across services, and health checks align with platform rules for production ML.</p>
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