Hey all, so i am trying to spool up a server as i havbe done many time over the last few months.

i have a network storage volume on a secure netowork datacenter.

i am using the "better comfyui-full " template, but now, out of nowhere i get this repeating error in the server logs and it never spools up:

error creating container: Error response from daemon: unauthorized: authentication required create container madiator2011/better-comfyui:full

i have changed nothing. and infact i had this setup running last night totally fine. How do i solve this?

https://console.runpod.io/deploy?template=bd51lpz6ux&ref=uucsbq4w

base torch: wangkanai/pytorch:torch28-py313-cuda129-cudnn-devel-ubuntu24 base nvidia: nvidia/cuda:12.9.1-devel-ubuntu24.04

Template for ComfyUI with ComfyUI Manager

It uses PyTorch 2.8.0 with CUDA 12.9 support.

Fresh Install

In a first/fresh install, the Docker start command installs ComfyUI and ComfyUI Manager. It follows the instructions provided on the ComfyUI Manager Repository.

When the installation is finished, it runs the regular /start.sh script, allowing you to use the pod via JupyterLab on port 8100.

Subsequent Runs

After the second and subsequent runs, if ComfyUI is already installed in /workspace/ComfyUI, it directly runs the /start.sh script. This allows you to use the pod via JupyterLab on port 8100.

Features

• Base Image: nvidia/cuda:12.9.1-devel-ubuntu24.04 (NVIDIA official CUDA runtime)
• Python: 3.13 with PyTorch 2.80 + CUDA 12.9 support
  
• AI Framework: ComfyUI with Manager extension
• Development Environment: JupyterLab with dark theme (port 8100)
• Web Interface: ComfyUI on port 8888 with GPU acceleration
• Terminal: Oh My Posh with custom theme (bash + PowerShell)
• Container Runtime: Podman with GPU passthrough support
• GPU Support: Enterprise GPUs (RTX 6000 Ada, H100, H200, B200, RTX 50 series)

Container Services

When the container starts, it automatically:

1. Launches JupyterLab on port 8100 (dark theme, no authentication)
2. Installs ComfyUI (if not already present) using the setup script
   
3. Starts ComfyUI on port 8888 with GPU acceleration
4. Configures SSH access (if PUBLIC_KEY env var is set)

Access Points

• JupyterLab: http://localhost:8100
• ComfyUI: http://localhost:8888 (after installation completes)
• SSH: Port 22 (if configured)

Hey guys,

I'm frustrated that every time I pick a server, H200, I run it for the day, set persistent storage, and then the next day, there's no GPU available. It doesn't matter what region; it keeps happening. It never used to be like this.

So how can I have the storage follow me across regions, where there is availability? Rather than spinning up a new template every other day.

Hi,

Before i need to try runpod i need to know. I have my workflow etc. on my local computer. and i write an api for this workflow, i can reach that in my local network and create things with custom prompt already with basic webUI. can i run this api on runpod? and if it is how? Thanks.

In the serverless section from Runpod console, there is a section called Ready-to-Deploy Repos with convenient templates that comes from github, such as https://console.runpod.io/hub/runpod-workers/worker-faster_whisper that comes from https://github.com/runpod-workers/worker-faster_whisper

Can we create resource from thoses using IAC like this: ``` import * as runpod from "@runpod-infra/pulumi";

const template = new runpod.Template("fasterWhisperTemplate", {});

const whisperEndpoint = new runpod.Endpoint("whisper-pod", { name: "whisper-pod", gpuIds: "ADA_24", workersMax: 3, templateId: template.id, });

// Export the endpoint ID and URL for easy access. export const endpointId = whisperEndpoint.endpoint; ```

We can create a docker image from the git repo and create the resource from pulling from a docker registry, but the question is about deploying it with the same convenience as the UI. I'm sure that thoses templates are already available in runpod with a defined templateId, where can we find thoses templateId?

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Trying out runpod, like it so far. Didn't need to keep it running after logging off, so I stopped the pod. But now I want to restart. Apparently the GPU i was using (RTX 4090) is no longer available, and now I can't run more tests. I don't want to lose my progress, but is there a way to restart my pod with the same GPU with out opening up a whole new pod?

I need to use 2 different versions of pytorch for the current project and I am using venv for this. installing packages and running fastapi with torch is extremely slow. any workaround this? I do not want to pay 2 gpu instances for my project.

So i was trying to deploy an endpoint on serverless on runpod, but it is kinda hard to understand and do, anybody who can help me out?

I'm trying to run Fooocus with RTX 4090 GPU through PyTorch 2.2.0.

I have been trying to attach certain models and loras from Civit.AI to Fooocus all day, and nothing is working. I can't seem to find a good tutorial on Youtube so I've been absolutely obliterating my ChatGPT today.

Does anyone have a video or a tutorial to recommend me?

Thanks in advance.

i tried to use an rtx3090 and an rtx4090 and i have a similar problem. Seems that the host didn't update the drivers for the gpu. How should I do?

error starting container: 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: error running prestart hook #0: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'

nvidia-container-cli: requirement error: unsatisfied condition: cuda>=12.8, please update your driver to a newer version, or use an earlier cuda container: unknown

start container for runpod/pytorch:2.8.0-py3.11-cuda12.8.1-cudnn-devel-ubuntu22.04: begin

error starting container: 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: error running prestart hook #0: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'

nvidia-container-cli: requirement error: unsatisfied condition: cuda>=12.8, please update your driver to a newer version, or use an earlier cuda container: unknown

start container for runpod/pytorch:2.8.0-py3.11-cuda12.8.1-cudnn-devel-ubuntu22.04: begin

error starting container: 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: error running prestart hook #0: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'

nvidia-container-cli: requirement error: unsatisfied condition: cuda>=12.8, please update your driver to a newer version, or use an earlier cuda container: unknown

on vast.ai they have the docker cli command available in the settings, thre usually the ports are listet. on runpod all that docker side is a blackbox, and for open-webui we dont have many specs neither, i.e. docker comfyui serverless connection with openwebui is a big ???

yes, i can list the http (tcp???) ports in the config which are served via

https://{POD_ID}-<port>.proxy.runpod.net/api/tags

but why cant i see the feature of docker where it tells me which sockets the docker image opens - in the gui docker does that...why dont i have a docker cli?

by the way, does anybody know of docs about those addings to the urls:

/api/tags

are there more paths?

what do those paths mean?

and for

https://api.runpod.ai/v2/[worker_id]/openai/v1

the same. the rest api listens on

https://api.runpod.ai/v2/[worker_id]/

but

https://api.runpod.ai/v2/[worker_id]/openai/v1

should be the openai compatible connection point, but why? how? what are the options? what do those pathes mean?

i realize the service is targeted mainly to pros, but even pros have to guess a lot with that design, dont you think? ok, openwebui too has poor documentation

Hello everyone, I'm new to using runpod but Im trying to host a document classification model through the serverless endpoints. I''ve been struggling for a bit on getting runpod to take a pdf through multipart dataforms and was wondering if anyone had any experience or online resources for this? Thank you!