The Moment a Multi-Node Deployment Hits a Wall: When Container Entrypoints Collide

Introduction

In the complex dance of deploying large language models across distributed GPU infrastructure, there are moments when a carefully constructed plan unravels in a single line of error output. Message <msg id=6675> captures exactly such a moment. In this message, the assistant executes a simple command — waiting 60 seconds and then tailing a log file — and is confronted with a stark failure: exec: "./run-cluster-node.sh": stat ./run-cluster-node.sh: no such file or directory. This seemingly mundane error message represents a fundamental architectural incompatibility between two pieces of infrastructure that the assistant was attempting to bridge. The message is a pivot point in a larger narrative of deploying the Qwen3.5-122B-A10B-FP8 model across two NVIDIA DGX Spark nodes, and understanding it requires unpacking the reasoning, assumptions, and context that led to this failure.

The Broader Mission: Multi-Node Inference on DGX Spark

To understand why this message matters, we must first understand the mission. The assistant is deploying Qwen3.5-122B-A10B-FP8, a 122-billion-parameter mixture-of-experts model with FP8 quantization, across two DGX Spark systems. Each DGX Spark is an NVIDIA Grace Blackwell ARM-based workstation with a single GB10 GPU (SM121 architecture, 120GB unified memory). The model itself is approximately 119GB in size — too large to fit on a single Spark's GPU memory alongside the operating system and runtime overhead. The solution is tensor parallelism across both nodes, splitting the model across the two GPUs using NVIDIA's InfiniBand RoCE interconnect.

This is not a trivial deployment. The assistant has already navigated a series of challenges: discovering that SGLang's multi-node NCCL initialization hangs indefinitely, pivoting to vLLM, finding a community-built Docker image (hellohal2064/vllm-qwen3.5-gb10) that supports Qwen3.5 on GB10, and attempting to integrate this image with the existing launch-cluster.sh infrastructure that handles Ray-based multi-node orchestration.

What the Message Actually Contains

The message itself is deceptively simple. The assistant runs:

sleep 60 && ssh aurora@10.1.230.180 'tail -20 /home/aurora/vllm-qwen35-cluster.log' 2>&1

This is a status check — a 60-second pause followed by reading the last 20 lines of a cluster launch log. The log output reveals the outcome of the previous message's attempt:

Action: exec
Checking SSH connectivity to worker nodes...
  SSH to 192.168.200.13: OK
Starting Head Node on 192.168.200.12...
7c6745632964c3571174bcf1999ac5a34746fe9b24589b88d119cdd698eaefea
docker: Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: exec: "./run-cluster-node.sh": stat ./run-cluster-node.sh: no such file or directory: unknown

The SSH connectivity check passes — the worker node at 192.168.200.13 is reachable. The head node container is started (the long hex string is a container ID). But then the Docker daemon reports a fatal error: the container runtime tried to execute ./run-cluster-node.sh and that file simply does not exist inside the container.

The Chain of Reasoning That Led Here

To understand why this error occurred, we need to trace the assistant's reasoning through the preceding messages. The assistant had been trying to use the hellohal2064/vllm-qwen3.5-gb10 Docker image — which has the correct vLLM version (0.17.1rc1) with Qwen3.5 model support — with the launch-cluster.sh script from the spark-vllm-docker infrastructure, which handles Ray cluster setup, NCCL configuration, and multi-node Docker orchestration.

The fundamental tension is that these two components were designed independently. The hellohal2064 image has its own entrypoint script that sets up default arguments and launches vLLM directly. The launch-cluster.sh script expects containers to run its own run-cluster-node.sh script, which initializes the Ray cluster and coordinates multi-node execution.

In message <msg id=6674>, the assistant attempted to resolve this conflict by overriding the image's entrypoint:

VLLM_SPARK_EXTRA_DOCKER_ARGS="--entrypoint= -v /home/aurora/models/Qwen3.5-122B-A10B-FP8:/models/Qwen3.5-122B-A10B-FP8:ro"

The --entrypoint= flag sets the entrypoint to empty, which should allow the command specified in the Docker run (the exec action of launch-cluster.sh) to run directly without the image's default entrypoint interfering. The assistant's reasoning was: if we bypass the image's entrypoint, the launch-cluster.sh script can run its own commands inside the container.

The Critical Assumption — and Why It Failed

The assistant's assumption was reasonable but incomplete. The launch-cluster.sh script doesn't just run arbitrary commands inside the container — it specifically runs ./run-cluster-node.sh, a script that is part of the spark-vllm-docker infrastructure and is expected to be present inside the Docker image. The vllm-node base image (used by the original launch-cluster.sh setup) includes this script. The hellohal2064/vllm-qwen3.5-gb10 image, being a completely different build, does not.

This is a classic integration failure in containerized distributed systems. The launch-cluster.sh script and the vllm-node image form a tightly coupled pair — the script generates Docker run commands that reference files and scripts that only exist in the vllm-node image. Swapping in a different image breaks this coupling, regardless of whether the entrypoint is overridden.

The error message tells the story precisely: exec: "./run-cluster-node.sh": stat ./run-cluster-node.sh: no such file or directory. The Docker container was created successfully (the container ID 7c674563... was generated), but when the container runtime tried to start the process, it couldn't find the specified executable. The container never actually ran.

Input Knowledge Required

To fully understand this message, one needs several layers of context:

  1. The infrastructure architecture: Knowledge that launch-cluster.sh is a shell script that orchestrates multi-node Docker container launches, using Ray for distributed coordination, and that it relies on run-cluster-node.sh as the startup script inside containers.
  2. Docker container mechanics: Understanding how Docker entrypoints work, how --entrypoint="" overrides the default entrypoint, and how the command passed to docker run is interpreted differently depending on whether an entrypoint is set.
  3. The vLLM ecosystem: Knowledge that different vLLM builds have different model support, that the hellohal2064/vllm-qwen3.5-gb10 image is a community build specifically for Qwen3.5 on GB10 hardware, and that vLLM 0.17 includes Qwen3.5 model classes that vLLM 0.14 (in the vllm-node image) lacks.
  4. The hardware context: Understanding that DGX Spark systems have 120GB unified memory, that the Qwen3.5-122B-A10B-FP8 model is ~119GB, and that tensor parallelism across two nodes is necessary to fit the model.
  5. The previous attempts: The assistant had already tried building a hybrid Docker image (msg 6673-6674), running single-node tests (msg 6660-6666), and directly using the image with launch-cluster.sh without entrypoint override (msg 6670-6672). Each attempt revealed different incompatibilities.

Output Knowledge Created

This message produces several important pieces of knowledge:

  1. Negative result confirmed: The hellohal2064/vllm-qwen3.5-gb10 image is incompatible with the launch-cluster.sh infrastructure when used directly, even with entrypoint override. The missing run-cluster-node.sh script is the blocking issue.
  2. Root cause identified: The error pinpoints the exact missing file, making the next steps clear. The assistant now knows that a hybrid approach — combining the vllm-node base image (which has run-cluster-node.sh) with the Qwen3.5 model files from the hellohal2064 image — is the correct path forward.
  3. Architectural insight: The message reveals the tight coupling between launch-cluster.sh and the vllm-node image. This is valuable knowledge for anyone working with this infrastructure — the cluster launch script is not image-agnostic.

The Thinking Process Visible in the Message

While the message itself is a straightforward bash command execution, the thinking process is revealed through what the assistant chose to check and how it interpreted the result. The 60-second sleep before checking the log shows the assistant's expectation that the cluster launch would take some time — it was anticipating a successful startup, not an immediate failure.

The fact that the assistant checks the log file rather than checking container status directly (docker ps or docker inspect) suggests an understanding that the launch-cluster.sh script writes its output to this log file, and that the log would contain the most complete picture of what happened.

The truncated output ending with "Ru..." at the bottom of the log is also telling — the assistant's tail -20 command captured only the last 20 lines, and the error output was longer. The "Ru..." is likely the beginning of "Runtime..." or a truncated line from a subsequent error message. This truncation itself is a minor technical detail — the assistant used tail -20 which may not have captured the full error chain.

What Came Next

The following messages show the assistant's response to this failure. In <msg id=6676>, the assistant immediately diagnoses the issue: "The launch-cluster.sh tries to run ./run-cluster-node.sh (which is its own script for Ray setup), but with --entrypoint="" the image can't find it." The assistant then pivots to a new strategy: building a proper patched Docker image that combines the vllm-node base (with Ray and multi-node support) with the Qwen3.5 model files from the hellohal2064 image.

This pivot involves extracting the exact model registry entries from the hellohal2064 image (msg 6677), checking the registry format in the old vllm-node image (msg 6678), and constructing a Dockerfile that copies the Qwen3.5 model files and patches the model registry (msg 6679). The assistant even encounters a shell escaping issue with zsh (msg 6679) and resolves it by writing the patch script to a file instead (msg 6680).

Broader Significance

This message is a microcosm of a common challenge in ML infrastructure engineering: integrating independently developed components that were never designed to work together. The hellohal2064/vllm-qwen3.5-gb10 image was built by a community member specifically to run Qwen3.5 on DGX Spark hardware. The launch-cluster.sh infrastructure was built by another team (or the same team at a different time) to handle multi-node Ray orchestration. Neither was designed with the other in mind.

The assistant's journey through this integration — trying direct use, entrypoint override, hybrid image builds, and finally a proper patched image — mirrors the real-world process of deploying cutting-edge AI models on heterogeneous infrastructure. The error in <msg id=6675> is not a mistake in the traditional sense; it's a necessary discovery step that reveals the true architecture of the system and guides the assistant toward the correct solution.

Conclusion

Message <msg id=6675> captures a moment of failure that is also a moment of learning. The assistant's attempt to bridge two independently designed infrastructure components hits a wall — a missing shell script in a Docker image. But this failure is productive: it reveals the tight coupling between the cluster launch script and its expected base image, and it guides the assistant toward the correct approach of building a properly integrated Docker image. In the broader narrative of deploying Qwen3.5-122B-A10B-FP8 across two DGX Spark nodes, this message marks the point where the assistant abandons the shortcut of using a pre-built image directly and commits to the more involved but ultimately correct path of building a custom integrated image. The error message itself — stat ./run-cluster-node.sh: no such file or directory — is a textbook example of how containerized distributed systems fail when component boundaries are mismatched, and how those failures, properly interpreted, illuminate the path forward.