RunPod provides two services: Secure Cloud and Community Cloud. Secure Cloud runs in T4 data centers by our trusted partners. Community Cloud is a peer-to-peer gpu cloud that connects individual compute providers to compute consumers. Our Community Cloud hosts are invite-only and vetted by us. Both solutions offer prices that are far cheaper than large cloud providers such as AWS or GCP can provide.
RunPod leverages technologies such as docker to containerize and isolate guest workloads on a host machine. We take care of handling billing, payment, and automating the host-client interaction.
We'd be happy to help! Join our discord, message us in our chat bubble (bottom right), or shoot us an email at email@example.com!
On-demand instances are for non-interruptible workloads. You pay the on-demand price and cannot be displaced by other customers as long as long as you have funds to keep your pod running.
A spot instance is an interruptible instance that can generally be rented for much cheaper than an on-demand instance Spot instances are great for workloads that are stateless, like mining, or for workloads that you can periodically save to volume disk. Your volume disk is retained even if your spot instance is interrupted.
We currently charge $0.1/GB/month for all storage on running pods and $0.2/GB/month for volume storage on stopped pods. This is because we have limited storage on our compute machines, and we want to make sure that active users have enough space to run their workloads. Storage is charged on a per-minute basis and we never charge users if the host machine is down or unavailable from public internet.
Yes. Your data is run in a multi-tenant environment where other clients do not have the ability to access your pod.
Data privacy is important to us at RunPod. Our terms of service prohibits hosts from trying to inspect your pod data or usage patterns in any way. If you want the highest level of security, go with one of our Secure Cloud machines
You can run any public docker container available on docker hub. If you are not well versed in containers, we would recommend that you stick with the default run types like tensorflow or pytorch. If you know what you are doing, however, you can do a lot more!
Usually this happens for one of several reasons. If you can't figure it out, reach out to us and we'll be happy to help you.
You are trying to run a pod to SSH into, but you did not give the pod an idle job to run like "sleep infinity"
You have given your pod a command that it doesn't know how to run. Check the logs to make sure that you don't have any syntax errors, etc.
It is important for RunPod to provide you with reliable hosts. All of our listed hosts must meet a minimum reliability to get listed. However, if you want the highest level of reliability and security, choose a Secure Cloud host. They are hosted in data centers and have redundant power and internet. RunPod calculates host reliability by maintaining a heartbeat with each host machine in real time. The result of this calculation for several different time ranges is provided on the GPU Cloud homepage.
Pods are billed by the minute.
All your pods are stopped automatically when you don't have enough funds to keep your pods running for at least 10 more mins. When your pods are stopped, your container disk data will be lost, but your volume data will be preserved. You will have 2 days to add more funds. If you fail to do so, your pods will be terminated and pod volumes will be removed.
After you add more funds to your account, you can start your pod if you wish (assuming that there are enough GPUs available on the host machine).
If the host machine loses power, then it will attempt to start your pod again when it comes back online. Your volume data will be preserved and your container will run the same command as it ran the first time you started renting it. Your container disk and anything in memory will be lost!
The host machine will continue to run your pod to the best of its ability even if it is not connected to the internet. If your job requires internet connectivity, then it will not function. You will not be charged if the host loses internet connectivity, even if it continues to run your job. You may, of course, request that your pod exit while the host is offline and it will exit your pod when it regains network connectivity.
We implement a spending limit for newer accounts that will grow over time. This is because we have found that sometimes scammers try to interfere with the natural workings of the platform. We believe that normal usage should not be impacted by this limit. That being said, we are perfectly happy to up your spending limit if you contact us and share your use case with us.
The best way to get data into your pod is to put it somewhere in the cloud and then use our Cloud Sync feature to pull it from your private bucket. We currently offer multiple integrations with cloud providers like AWS, Google Cloud, Azure, Dropbox, and Backblaze. If you just want to get running and have a small dataset (megs, not gigs), then you can use the upload feature inside Jupyter notebook. You can also use scp or sftp if you add and configure sshd in your pod as described in this blog post.
The easiest way to get access to your pod is through the Jupyter notebook if you have it set up. If you need quick terminal access, you can find a step-by-step in this blog post. Note that this terminal access is not a real ssh daemon and therefore only supports basic functionality. Our custom TensorFlow and Pytorch images have easy configuration for real SSH access if you are on our secure cloud. If you need IDE/SCP/SFTP support on your own custom container, then you should add openSSH daemon to your pod and tunnel over an open TCP port.
If you need to run a real SSH daemon in your pod, you can use a runpod container image, or you can find a step-by-step in this blog post to roll your own.
The easiest way to run a Jupyter notebook is to choose the runpod TensorFlow or runpod PyTorch run templates.
Choose the default settings when renting and choose deploy.
Navigate to your "my pods" dashboard when asked on the deploy success page.
Wait a 20 seconds to a few minutes for your pod to get ready.
The Connect button should light up. Click on it for access to the Connect to Jupyter button. Note that it may take an additional 10-30 seconds for the webserver to start serving the notebook. You may get a "502" error in the meantime.
Choose a container that fits your use case. Some simple ones to start with are runpod/tensorflow or runpod/pytorch.
When renting your pod, put bash -c "sleep infinity" into the docker command box if you aren't using one of our runpod images.
Add a volume and a volume path. This will allow you to have persistent storage on the host machine.
Rent the pod.
Navigate to your "my pods" dashboard.
Click on detail expander button on the bottom left of the pod card.
Click on the SSH button on the pod card. You can find it under the name and ID on the left side of the card.
Generate and set up your SSH key in RunPod if you have not already done so.
Copy the SSH command into your local terminal to log in.
Do everything you need to set up your environment
Create a script that you can run to restart your job on failure and that saves your job to the volume path disk every once in a while.
Save your script and anything else your job needs in your volume path
Go to RunPod and navigate to your "my pods" dashboard.
Click on the "edit job" button on the pod you are using.
Edit the docker command to run your script. For example, if your volume mount path is /volume and you have a start.sh script, then your docker command command should look like bash -c "./volume/start.sh"
Your container should restart and begin running your job. If the container goes down, it will automatically start running your script again when it comes back up.
Sure do! Take a look at our legal page.