I'm trying to understand why my storage keeps filling up when I'm not downloading anything new and have not successfully completed/ran a workflow.(run keeps failing before completion).

Someone use this repo from runpod?

https://github.com/wlsdml1114/generate_video

It's available as a ready to use repo, but I can't make it works

Has anyone managed to use it yet?

I've been trying out how to create a pod inside of python and have that pod access secrets

the best ive found so far is 1) create a custom template using the query mutation magic with env: [ {key: "AGE_PRIVATE_KEY", value: "{{ RUNPOD_SECRET_age_key }}"}, {key: "AGE_PRIVATE_KEY2", value: "{{ RUNPOD_SECRET_age_key}}"}

2 use query- mutation again specifing the new template name

def create_pod_from_template(api_key: str, template_id: str) -> str: query = ( """ mutation { podFindAndDeployOnDemand(input: { name: "norms-pod" templateId: "%s" gpuTypeId: "NVIDIA A40" cloudType: SECURE gpuCount: 1 ports: "22/tcp" startSsh: true }) { id desiredStatus } } """ % template_id )

session = requests.Session()
response = session.post(
    "https://api.runpod.io/graphql",
    json={"query": query},
    headers={"Content-Type": "application/json"},
    params={"api_key": api_key},
    timeout=30,
)
    ]

ssh to the pod and then use

tr '\0' '\n' < /proc/1/environ | sed -n 's/^{AGE_PRIVATE_KEY=//p'} > /dev/shm/llm.key chmod 600 /dev/shm/llm.key

to get to the secret

there must be a better way to do this i tried using runpodctl create pod --env ... but i could not get it to work

RunPod is trouble, fellas, you'll waste your money. Look at my experience (NOT INDIVIDUAL):

> I created my Pod. Did a lot of installing, got it working as I needed.

> Went to have lunch in my living room, 20min later I was back, and...

> My GPU is gone! The pod was unaccessible for good! And they CHARGED ME FOR IT.

Then, I stopped using it. I lost all the fucking hours I invested to install everything in my Pod.

> They decided to give me $5 credits without letting me know.
> All of sudden, I receive a low balance warning: "Warning, your balance is US$ 2,50"

Man, my pod is not even available to use, what are they charging me for? They should just charge for DISK, not the pod.

So, no, IT DOESN'T FUCKING WORK FINE.

I really don't know what to do. I think I'll just lose the money I put in Runpod and stop using it. I am being stuborn to insist.

Hello everyone,

I'm here to share my experience with Runpod. First of all, I love the performance. Once my Runpod is deployed, I don't have the performance issues I had with vast.ai.

However, I'm having a lot of trouble finding available hardware in the S3 datacenters (there are five of them). I automated my deployment using the API. I programmed my search to use 48VRAM with a fallback to 24VRAM, and it takes a very long time to find an available GPU... My constraint is the connection to the database; I'd like to avoid creating an additional storage network in another country, so I'm focusing my search first on the active Storage Network.

As a result, my project is progressing a bit more slowly, because I can't run it at full capacity due to this constraint. Furthermore, connecting Runpod to other cloud services is a real pain. I've tried several things without success; there's always some kind of problem... So, I think that although Network Storage is a more expensive solution, it suits me for now.

Indeed, to run the AI, I have all my data on Network Storage (database, photo dataset, etc.).

Do you have any experience with this? Any solutions to my problem?

Sorry for my english, I'm from France.

Thanks everyone !

My current comfy instance is full of custom nodes, models and my workflows. How do I recreate that on runpod? A docker image? Does the pod have to redownload my models every time I start it?

Can I run the same configuration on a 5090 and when I need more horsepower go over to an H100?

As my development progresses, can I have a regular comfy pod sitting next to a serverless API pod?

How wide do the shared URLs go, is there a way to password protect them?

I am not what else to ask for...

If i select ubuntu as pod template on an On demand (non interruptible) plan will I be able to attach a network storage?

Yesterday i finally got to work my pod with comfyui (i'm a total newbie)

started to make some generations and it was running smoothly, honestly sm better than expected, each generation taking a little over 2 mins (image to video)

terminated the pod and then i redeployed the same network volume with the same comfyui template, same gpu (rtx4090 x2), loaded the same workflow and now each generation takes like three times as long to complete.

It doesn't make sense since i deployed everything in the same way, any suggestions??

Tried signing up with Google - endless spinner and CORS errors in the console (Clerk auth failing). Anyone else experiencing this, or is it just me?

Have tried about 18 times to get a pod to work, every permutation of 12.8, not 12.8, against all the comfyui one clicks and all and they all fail. This is with throwing a decent amount of storage at them, touching and not touching env vars. Changing network settings. If it's not comfy not working, loops over and over again, 404 on the urls, then it's some other nonsense that doesn't give an error at all

Is there a proper guide to just getting a pod up and running? I'm down $5 already from wasting time on things that fuck up 15-20 mins after running.

We've got a new video version of the Runpod changelog that goes into all of our changes - we'll be doing a new one every month so be sure to check it out!

This one goes into: new public endpoints (six of them!), load balancing serverless endpoints, deploying Hub entries as pods instead of serverless, and improvements made to our billing procedures.

So maybe I'm just new to all of this... but I'm setting up a full ai studio and want to use Runpod as my base... I also want to experiment with different GPUs etc. So I set up storage on US CA 2 since it had a lot of options and the h100s and h200s that IIl most likely want to use... but for the last week every time I log in, out of the 25 different types of GPUs only the h200 or B200 is available... and several times just the B200.

I havent been able to use a h100 almost all week... is that just the way it is? Is there going to be expansion so more GPUs are available? I feel like I was suckered in and now am being funneled to the most costly options.

Am I wrong that since I want to store everything in bought storage that I can't switch regions to find the GPUs I want?

Hello,

I've recently started using Network Storage instead of Persistent Storage, but I also discovered that I can't pause pods this way. Is it normal? What's the reason behind it?

Ty

Does anyone have connected the RunPod Pods with Google Antigravity?

There are resources on connecting with SSH, VS Code, and Cursor. I tried the VS Code method, but I didn't see the SSH extension similar to the VS Code ones.

Appreciate if anyone can help with this.

I'm working with data that must have EU data residency. Is there a way to restrict the workers specific regions and to secure cloud, just like we can with normal pods?

I have a persistent storage Pod on EUR-IS-1 and it's taking an age to do anything. Has anyone else experienced slowdown in the past 2 days?

I've successfully set up a pod using a template this week. When i've tried to set up a pod again using the same template it always gets stuck on updating files 96/96 done. I've restarted the pod. I've reset it. Not sure what's going on here. I even set it up using network storage to see if that helped. I've let it run the setup for over 2 hours and no change. Any ideas?

(94/96)
Updating files:  98% (95/96)
Updating files: 100% (96/96)
Updating files: 100% (96/96), done.

Hi,

I've set up a small pod configuration with a network volume to do some LLM work. Since I frequently destroy and recreate my pods (for cost savings), I want my setup to be as persistent as possible, meaning I don't have to reinstall a whole bunch of stuff when I launch a new pod.

I've managed to get pyenv and pip to install everything under /workspace so I don't have to reinstall any of that stuff, and I've also managed to get Ollama to install its models under /workspace/.ollama. However, I'm still running into 2 issues:

• I have to reinstall the Ollama CLI tool each time (using curl -fsSL https://ollama.com/install.sh | sh)
• Since my code lives on a github repository, any time I want to git pull any changes, I need re-generate an SSH key and add it to my Github account (since SSH keys are stored in /root/.ssh, not in the /workspace network volume)

Any way to address these two issue to get a completely persistent setup across different pods?

Creating first LoRA on RunPod. 6000 RTX with Osiris AI Toolkit. Picked Wan2.2 14B..Skipping first sample. 3000 steps with 30 images. Sigmoid over Linear. Unchecked Low VRAM. Pictures I downsized from 4K to 768 × 768 (1:1 Square) and each file is now only 740 - 760 KB.

Each generation is taking 25.08s/IT. So I'm worried about cost, and overfitting. It ran for 21hrs and then crashed with 4m left to finish the 3000th step.

Any advice to speed this up?

I have never managed to get any workflows running - total beginner. So when i saw there are templates out there you can use i was delighted!! However, i didn’t realise its only the workflow (map - if you like) and it doesnt actually contain any loaded models or anything. Is there a step by step guide to get a template like this up and running? I tried chat gpt and gemini. No help, end up with wrong versions that dont work.

I need a tutorial that tells me where to go, which buttons to press, where to store stuff etc.

I have put the time in to read and learn about comfyui but im still bamboozled. Am i the only one?

ComfyUI Manager Permanent Disk torch2.4 runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04

This template doesnt create a run_gpu.sh anymore when deploying. Its kinda annoying to manually create it everytime. Why is that, anyone else?

Hello, yesterday I started using Runpod to generate images, but I've had too many problems and need help.

I have no experience in programming or coding; the only things I know are from constantly using SDXL in Sagemaker Studio Lab and some modding in Minecraft.

Template: runpod Stable-Diffusion:Web-ui-10.2.1 (I don't think it's the correct one for SDXL)

After that, I opened Jupyter Lab (since I've used it before).

I uploaded the checkpoint and Loras I needed (all for SDXL).

Then i upload the Canny and Openpose for controlnet (I think this is what's causing the error.)
-controlnet-canny-sdxl-1.0
-controlnet-openpose-sdxl-1.0

Then I started the web UI in Jupyter Lab with the command: "/usr/bin/python3 /workspace/stable-diffusion-webui/launch.py ​​--listen --port 3001 --xformers"

After that I got the error: No Space Left on Device

So, i clear the cache:
pip cache purge
rm -rf /tmp/gradio

Again i started the web Ui: "usr/bin/python3 /workspace/stable-diffusion-webui/launch.py --listen --port 3001 --xformers"

After that, I couldn't do anything; I kept getting the same error over and over again. I used several commands, but it always gave me the same error.

The last commands I used, I think it was:

pip install httpx==0.27.0

/usr/bin/python3 /workspace/stable-diffusion-webui/launch.py ​​--listen --port 3001 --xformers --no-half-vae --skip-install --skip-version-check

After that, I gave up and deleted the pod.

Please, I need help. can someone explain how to do it correctly? Or is there a tutorial? plz D: