Dockerized FastAPI won’t accept connections: diagnose Uvicorn startup blocked by transformers pipeline

Learn why a Dockerized FastAPI app shows Uvicorn listening yet refuses connections: transformers pipeline loading blocks startup. Fix steps, checks, curl tips.

Dockerized FastAPI app builds and runs, the logs show Uvicorn listening on 0.0.0.0:8000, yet curl or Postman can’t connect. This is a common trap when heavyweight initialization happens during import time and the server never reaches the “ready to accept traffic” state. Below is a practical way to isolate the culprit and get to a reliable fix path without guessing.

Repro setup and the failing example

The application exposes a POST /classify endpoint and loads a transformers zero-shot pipeline at import time. The container is started with uvicorn and port 8000 published. The connection attempt fails with “Couldn’t connect to server”.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from transformers import pipeline as hf_pipeline

class InPayload(BaseModel):
    text: str = Field(..., example="")
    lang: str = Field(
        "en",
        pattern=r"^(en|tr|de|fr|es|it|pt|ru|ar|zh|ja|ko|hi|bn|ur|fa|th|vi|id|ms|nl|sv|no|da|fi|pl|cs|sk|hu|ro|bg|hr|sr|sl|et|lv|lt|el|he|uk|be|ky|uz|km|my|tg|az|hy|ga|cy|is|mk|bs|sq|mn|ne|pa|gl|la)$",
        description="ISO language code",
        example="tr"
    )

class OutPayload(BaseModel):
    label: str
    score: float

api = FastAPI(title="Spam & Abuse Detector")

nlp = hf_pipeline(
    "zero-shot-classification",
    model="joeddav/xlm-roberta-large-xnli"
)

CHOICES = ["spam", "adult_content", "drugs", "non_spam"]

@api.post("/classify", response_model=OutPayload)
def detect(payload: InPayload):
    outcome = nlp(
        sequences=payload.text,
        candidate_labels=CHOICES
    )
    top_idx = outcome["scores"].index(max(outcome["scores"]))
    top_label = outcome["labels"][top_idx]
    top_score = outcome["scores"][top_idx]
    return OutPayload(label=top_label, score=top_score)

What’s actually going on

The server log stops after “Started reloader process …” and never prints the typical “Started server process” and “Application startup complete” lines. The difference matters. When a heavy dependency like a transformers pipeline is created at import time, it can block startup, so Uvicorn doesn’t reach the point where it accepts connections. As a result, any curl or Postman request fails because the socket isn’t open yet.

There’s a straightforward way to verify this: replace the pipeline and return mock data. If connections succeed and the server reaches “Application startup complete”, the container and FastAPI wiring are fine and the issue sits squarely in the model initialization path.

Working isolation: confirm server health without the pipeline

Run the application with the same API surface but without the transformers pipeline. This validates Dockerfile, networking, and FastAPI routing end to end.

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field

class InPayload(BaseModel):
    text: str = Field(..., example="")
    lang: str = Field(
        "en",
        pattern=r"^(en|tr|de|fr|es|it|pt|ru|ar|zh|ja|ko|hi|bn|ur|fa|th|vi|id|ms|nl|sv|no|da|fi|pl|cs|sk|hu|ro|bg|hr|sr|sl|et|lv|lt|el|he|uk|be|ky|uz|km|my|tg|az|hy|ga|cy|is|mk|bs|sq|mn|ne|pa|gl|la)$",
        description="ISO language code",
        example="tr"
    )

class OutPayload(BaseModel):
    label: str
    score: float

api = FastAPI(title="Spam & Abuse Detector")

@api.get("/")
def root():
    return {"Hello": "World"}

TAGS = ["spam", "adult_content", "drugs", "non_spam"]

@api.post("/classify", response_model=OutPayload)
def detect(payload: InPayload):
    fake_label = payload.text
    fake_score = float(len(payload.text))
    return OutPayload(label=fake_label, score=fake_score)

With this version, the server advances to the full startup sequence. You should see lines such as:

INFO: Started server process [...]
INFO: Waiting for application startup.
INFO: Application startup complete.

Once it’s up, send a local request from the same machine that runs Docker:

curl -X POST http://127.0.0.1:8000/classify \
  -H "Content-Type: application/json" \
  -d '{"text":"bla bla","lang":"en"}'

If this returns a JSON response, your container, FastAPI app, and port mapping are functioning. That isolates the problem to the model pipeline initialization.

What to change and how to proceed

At this point there are two actionable takeaways. First, proving that the mock implementation accepts connections means the Dockerfile and app wiring are solid. Second, the zero-shot pipeline created during import is the blocking operation. The practical implication is that the service is not ready to accept requests while the pipeline is loading. You can validate readiness by watching for the “Application startup complete” line before hitting the endpoint. If you try to send traffic earlier, connections will fail.

Another source of confusion is the client. If you use a browser-based client that runs remotely, it cannot reach 127.0.0.1 on your local machine. Use a local curl or a locally installed client that targets http://127.0.0.1:8000.

Why this matters

Large models can delay application readiness and mask themselves as networking issues. Understanding the startup sequence and knowing how to isolate heavy initialization helps avoid chasing the wrong layer. Verifying the app with a lightweight stub removes Docker and routing from the equation and focuses efforts on the real bottleneck.

Conclusion

Keep initialization under control and observe the logs. If the server hasn’t printed “Application startup complete”, it isn’t ready, and connection attempts will fail. When diagnosing, swap in a mock response to confirm the container and FastAPI are fine, then reintroduce the model and wait for it to load before testing. And when using a client, prefer a local curl first to rule out connectivity that can’t reach your localhost.