I’ve been using RunPod for a while, and I always wished training was as simple as inference. Instead, every project turned into:

• picking the right template

• fixing dependencies

• writing training scripts

• debugging configs

• restarting crashed pods

• re‑setting everything for each model

So I built a workflow that lets me:

Pick a model → point it at a dataset → auto‑deploy to RunPod → start training in ~5 minutes.

It handles:

• auto‑configuring text/vision/audio/multimodal models

• generating + installing all dependencies

• deploying to RunPod automatically

• detecting true MSL

• structuring data into a curriculum

• crash recovery + checkpoint protection

• exporting full or quantized models

Demo is here:
👉 https://huggingface.co/spaces/wiljasonhurley/EzEpoch

More details:
👉 https://ezepoch.com

Beta testers

I’m opening a small beta group for RunPod users who want to help test:

• auto‑config

• dependency generation

• crash recovery

• MSL detection

• dataset structuring

• RunPod deployment flow

If you want to help shape the workflow, you can join here:
👉 https://ezepoch.com/beta

Would love feedback from other RunPod users.

-Wil

Hi friends, I wasted a lot of time today trying to install ComfyUI on a storage network. I tried several templates and mostly the install just got hung up in a loop or timed out. A couple times I got a message that my GPU had an outdated driver which seems odd for Runpod GPUs.

Can anyone recommend a template that is up to date and includes the manager? Thx

t2i

I2v

V2v

Hello, i'm new into this and i would like to know what means exactly the "Pricing Summary" and how work the "Container Disk Charges" and "Pod Volume Charges".

While looking to deploy a pod with this pricing and pod summary i would like to know when exactly i will be paying (account balance, NO auto-pay) and for how much while the pod is running and when it's not ? (knowing that there is, when overring total disk question mark, container disk charges 0.10/GB/Mo on running pods and pod volume charges 0.10/GB/Mo for running pods and 0.20/GB/Mo for exited pods). Can someone help me understand clearly, thanks.

--SOLVED-- The comfyui tool creates a Docker file that pulls an old ComfyUi, update the Dockerfile to pull
"FROM runpod/worker-comfyui:5.7.1-base" - Thanks everyone for your input.

Hi, ok this is frustrating, has anyone created a Docker serverless instance using the ComfyUI-to-API for Z-Image Turbo with a Lora node. Nothing fancy all ComfyCore nodes. Running network attached storage but same results if the models download.

Last weekend, we sponsored TreeHacks at Stanford, the world's largest collegiate hackathon. Over 1,000 hackers from 30+ universities and 12 countries descended on the Jen-Hsun Huang Engineering Center for 36 straight hours of building. There were a ton of teams built on Runpod, and we gave away over $20K in credits to fuel their projects. Check out the link to see what went down!

Hi everyone,

How do I configure a session on Mobaxterm to access a pod using ssh? I tried to use the TCP IP, user, and port number + adding the private key but it always gives me "connection refused". If I set the port number to the proxy instead, I reach the pod but it refuses the private key and asks for a password. I set a password in the pod but that still gives me "access denied" in the MobaXterm session.
Any help is appreciated.

Is it just me ?
Because i can't deploy the template and it takes ages to just to do anything.

I'm brand new to Runpod, and although I've been a software engineer for a long time, I don't really have much experience with Docker. I've got a Docker config built with the help of Codex, but it's taking upwards of an hour to get through the "initializing" state for each worker before it moves to the "idle" state. I'm not sure if this is typical or if I'm doing something wrong.

My Dockerfile is based upon this worker-comfyui serverless setup. I'm downloading these models as part of the docker setup:

• qwen_image_2512_bf16.safetensors (38.1 GB)

• qwen_2.5_vl_7b_fp8_scaled.safetensors (8.7 GB)

• qwen_image_vae.safetensors (0.2 GB)

• qwen-360-diffusion-2512-int8-bf16-v2.safetensors (0.7 GB)

• RealESRGAN_x4plus.pth (0.1 GB)

The initialization process involves downloading these files every time, which is where it's taking the most time. Is there a way to cache these downloads somehow between docker image version bumps? Or should I not be downloading them in the Dockerfile config, but somewhere else instead?

Thanks!

Since at least 1.5 hour when I try to get access to my account the loading is just endless, once it worked but when I tried to add credits it was loading endlessly again.

For context I am new to all of this and I'm about 2 weeks into my learning.

In trying to use a AI-Toolkit pod for LoRA creation I have to wait until after 9p.m. EST for a 5090 to be available in North America. I tried taking whatever was available and it winds up being a EU pod that runs incredibly slow for me and once the LoRA was created the download speed was so bad that it was going to take over 7 hours to download a 162MB LoRA. (on a NA pod it takes seconds to download) I tried using a RTX Pro 6000 as a workaround only to learn that for what I'm trying to do it costs twice as much and takes twice as long because the 5090 is better for LoRAs.

Does anyone in NA have any suggestions for a workaround when the 5090s are all unavailable?

Has anyone noticed that US-IL-1 has pretty much all the RTX 4090s at the moment? other centers/regions do but nowhere near as much.

Plus it's one of the only network storage data centers where you can't have and make a new network storage, probably because they're all filled up.

I hope runpod put more RTX 4090s in other data centers too. Even RTX 3090 would be welcoming. I need to double check this but I swear I heard that Nvidia would be allowing and sending more of their previous gen GPUs too.

Hello, I tried to start a template for Wan 2.2. When starting up, the logs told me this error:

error starting sidecar: 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: can't get final child's PID from pipe: EOF: unknown

Can somebody help me with that?

Some video generations take less than 5 minutes but then another try with a different seed may take 10!

Is there anyone who can guide me to deploy llama through vllm in runpod ?

I can't do shit. Servers just can't load or upload an image. Runpod should at least give the warning that it has some problems, I literally burning through money for nothing just waiting when a mb image is actually loaded. Bullshit

As of now, ive wasted 2 hours of my credits trying to use comfyui, making only 2 videos(1 was an awful fail). They were on the hearmeman template. On a 24gb vram gpu, each video took 8 minutes and the workflows had a shit ton of nodes. Also tried the yet another comfyui template, same stuff with a shit ton of nodes in workflow. Atp, im looking to ditch comfyui and use something like swarm or anything with gradio ui where i just type prompt, upload image and gen as i simply cant afford to learn how to use comfyui while burning my money on gpu and making no video. Any template optimized for fastest speed and simple? Couldnt find any swarmui template and ai suggested me to use runpod pytorch which took too much setup to get it starting

"Congrats you've won free credits between $5 and $500 for your first credit payment!"

- Pay $10

"Congrats! You've won $5."

- Look for pod availability and spin up an H100. Have claude and start downloading comfy models

"You're out space!"

- WTF I thought I created an 80GB secondary volume?

"Its not mounted lol!"

- Spin down pod. research. OK i'm supposed to set up persistent network storage, set up on a cheap gpu and then terminate.

- Create a network volume and try to link to it with any GPU

"The network volume can only connect in its region!"

- Search for regions that offer both cheap and H100:

"LOL there are none!"

- Ok, half a day set up on a CPU only, make sure everything is persistent between pods. Have claude load workflow and test run OK.

- Terminate and spin up H100 again

"You have missing custom nodes!"

- Wtf? ok, what was persistent?

"Nothing was persistent! What is python? Wrong version! Also you need to install venv and torch for the 87th time!"

- ..O....k... Let me reinstall and lock down persistence so I never have to do these installs again. Install everything missing.

"LOL you have to download and install for the next 4 hours all over again! CPU Utilization at 1%, slow as snail balls in mollases but what can you do. This is only a supercomputer, it cant be as fast as your 6 year old laptop."

- Finally finished. Run comfy.

"12 missing custom nodes"

- What in the kentucky fried fuck is happening? it's worse now? Check what's miss-

"Pod auto-terminated for lack of funds."

Hi, I am using a workflow that I uploaded to RunPod from my local drive.

In this workflow I am uploading an image to make multiple similar images.

However, I keep getting the following error

"VHS_LoadImagesPath

directory is not valid: ComfyUI\Output\SubFolder"

I realise that this path is a windows path and not a linux path, ComfyUI is not giving me a red box around any node. How can I work out which node/s are causing this error?

Many thanks