Lambda Docs

Getting started

Can my data be recovered once I've terminated my instance?

We cannot recover your data once you've terminated your instance! Before terminating an instance, make sure to back up all data that you want to keep.
If you want to save data even after you terminate your instance, create a persistent storage file system.
The persistent storage file system must be attached to your instance before you start your instance. The file system cannot be attached to your instance after you start your instance.
When you create a file system, a directory with the name of your file system is created in your home directory. For example, if the name of your file system is PERSISTENT-FILE-SYSTEM, the directory is created at /home/ubuntu/PERSISTENT-FILE-SYSTEM. Data not stored in this directory is erased once you terminate your instance and cannot be recovered.

Can I pause my instance instead of terminating it?

It currently isn't possible to pause (suspend) your instance rather than terminating it. But, this feature is in the works.
Until this feature is implemented, you can use persistent storage file systems to imitate some of the benefits of being able to pause your instance.

Do you support Kubernetes (K8s)?

Kubernetes, also known as K8s, isn't supported on On-Demand Cloud.
However, Lambda offers managed Kubernetes for Reserved Cloud.
See our Managed Kubernetes Product Outline to learn more.
Managed Kubernetes Product Outline.pdf
Managed Kubernetes Product Outline

Why can't my program find the NVIDIA cuDNN library?

Unfortunately, the NVIDIA cuDNN license limits how cuDNN can be used on our instances.
On our instances, cuDNN can only be used by the PyTorch® framework and TensorFlow library installed as part of Lambda Stack.
Other software, including PyTorch and TensorFlow installed outside of Lambda Stack, won’t be able to find and use the cuDNN library installed on our instances.
Software outside of Lambda Stack usually looks for the cuDNN library files in /usr/lib/x86_64-linux-gnu. However, on our instances, the cuDNN library files are in /usr/lib/python3/dist-packages/tensorflow.
Creating symbolic links, or "symlinks," for the cuDNN library files might allow your program to find the cuDNN library on our instances.
Run the following command to create symlinks for the cuDNN library files:
for cudnn_so in /usr/lib/python3/dist-packages/tensorflow/libcudnn*; do
sudo ln -s "$cudnn_so" /usr/lib/x86_64-linux-gnu/

How do I open Jupyter Notebook on my instance?

To open Jupyter Notebook on your instance:
  1. 1.
    In the GPU instances dashboard, find the row for your instance.
  2. 2.
    Click Launch in the Cloud IDE column.
Watch Lambda's GPU Cloud Tutorial with Jupyter Notebook video on YouTube to learn more about using Jupyter Notebook on Lambda GPU Cloud instances.

Is it possible to use more than one SSH key?

It’s possible to allow more than one SSH key to access your instance. To do so, you need to add public keys to ~/.ssh/authorized_keys. You can do this with the echo command.
This FAQ assumes that you’ve already generated another SSH key pair, that is, a private key and a public key.
Public keys look like this:
ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIK5HIO+OQSyFjz0clkvg+48YAihYMo5J7AGKiq+9Alg8 user@hostname
SSH into your instance as you normally do and run:
echo 'PUBLIC-KEY' >> ~/.ssh/authorized_keys
Replace PUBLIC-KEY with the public key you want to add to your instance. Make sure to keep the single quotes (' ').
You should now be able to log into your instance using the SSH key you just added.
You can make sure the public key has been added by running:
cat ~/.ssh/authorized_keys
The last line of output should be the public key you just added.

What SSH key formats are supported?

You can add SSH keys in the following formats using the dashboard or the Cloud API:
  • OpenSSH (the format ssh-keygen uses by default when generating keys)
  • RFC4716 (the format PuTTYgen uses when you save a public key)
  • PKCS8
  • PEM
  • OpenSSH keys look like:
    ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIK5HIO+OQSyFjz0clkvg+48YAihYMo5J7AGKiq+9Alg8 foo@bar
  • RFC4716 keys begin with:
    ---- BEGIN SSH2 PUBLIC KEY ----
  • PKCS8 keys begin with:
    -----BEGIN PUBLIC KEY-----
  • PEM keys begin with, for example:
    -----BEGIN RSA PUBLIC KEY-----

How long does it take for instances to launch?

Single-GPU instances usually take 3-5 minutes to launch.
Multi-GPU instances usually take 10-15 minutes to launch.
Jupyter Notebook and Demos can take a few minutes after an instance launches to become accessible.
Billing starts the moment an instance begins booting.

How do I learn my instance's private IP address and other info?

You can learn your instance's private IP address with the ip command.
You can learn what ports are open on your instance with the nmap command.

Learn your instance's private IP address

To learn your instance's private IP address, SSH into your instance and run:
ip -4 -br addr show | grep '10.'
The above command will output, for example:
enp5s0 UP
In the above example, the instance's private IP address is
If you want your instance's private IP address and only that address, run the following command instead:
ip -4 -br addr show | grep -Eo '10\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)'
The above command will output, for example:

Learn what ports on your instance are publicly accessible

You can use Nmap to learn what ports on your instance are publicly accessible, that is, reachable over the Internet.
The instructions, below, assume you're running Ubuntu on your computer.
First, install Nmap on your computer (not on your instance) by running:
sudo apt install -y nmap
Next, run:
Replace INSTANCE-IP-ADDRESS with your instance's IP address, which you can get from the Cloud dashboard.
The command will output, for example:
Starting Nmap 7.80 ( ) at 2023-01-11 13:22 PST
Nmap scan report for
Host is up (0.041s latency).
Not shown: 999 filtered ports
22/tcp open ssh
Nmap done: 1 IP address (1 host up) scanned in 6.42 seconds
In the above example, TCP port 22 (SSH) is publicly accessible.
If nmap doesn’t show TCP/22 (SSH) or any other ports open, your:
  • Instance might be terminated. Check the GPU Instances dashboard to find out.
  • Firewall rules might be blocking incoming connections to your instance.
nmap -Pn INSTANCE-IP-ADDRESS only scans the 1,000 most common TCP ports.

How do I close my account?

To close your Lambda GPU Cloud account:
  1. 1.
    Back up all of your data on your instances as well as in your persistent storage file systems.
  1. 2.
    Terminate all of your instances from the Cloud dashboard or using the Cloud API.
  2. 3.
    Delete all of your persistent storage file systems.
  3. 4.
    In the Cloud dashboard, under Settings, click Close my account. Carefully read the warning in the dialog box that appears. To proceed with closing your account, type in close account, then click Close account.