Follow these simple steps to install a new instance of Supervisely to your server.
Before you start installation of Supervisely to your host, please make sure your server meets the hardware & software requirements below.
The minimum hardware requirements are as following:
CPU: 4 vCPUs / cores
Memory: 8 GB RAM
Hard drive: 100 GB storage or more
Choose the hard drive size according to your needs. There is also an option to attach any S3 compatible storage to distribute the data in your cloud.
If you plan to use smart tool (AI powered semantic segmentation) or neural networks (human-in-the-loop) you will need a server with GPU. The requirements are as following:
GPU Memory: 8 GB or more (GeForce GTX 1080, Tesla K80, Tesla P100)
You can install Supervisely and run GPU computations on different machines using agents (see cluster).
We recommend to use the following EC2-instances for deployment in AWS:
1 × m5.2xlarge — the platform itself
1 × p2.xlarge — (for Training Module)
To run Supervisely you will need Linux OS with kernel 3.10 or newer. Debian-based distributions are preferable: Ubuntu 18.04, 16.04, 14.04, Debian Jessie. All other pre-requirements can be installed via
When all requirements above have been installed, you can deploy Supervisely.
Before the installation we will send you a
license.key, a unique installation key and command to install
supervise-cli. Please run it on the machine where you want to install Supervisely and make sure
sudo supervisely gives you help information.
sudo supervisely install-all in your terminal. We will detect the necessary dependencies and install them. The following software may be installed:
If don't have NVIDIA drivers and CUDA, you will be asked if you need to install it. Choose
y if you have GPU on your server. Your computer will be rebooted during the installation of the NVIDIA driver.
We have sent you installation key that looks like "hdhUssJskOskAA". Run
sudo supervisely auth <installation key> to authenticate yourself. Now, run
sudo supervisely update to download the latest version of Supervisely. We will ask you to provide a directory to store configuration in and some required variables. Be default, we use
/opt/supervisely folder. You can always check where is your configuration directory using
supervisely where command.
Though we will ask you to provide required configuration variables on the previous step, it's a good idea to make a final overview.
/opt/supervisely/.env. There are some configuration variables in it that you might want to change. Look for
<please, insert the value here> - you need to provide values here explicitly because we don't know those values in advance.
Here are some variables that you can find here:
SERVER_ADDRESS - Public or local network address of the server where you deploy Supervisely. That value will be used by agents to connect to the cluster. Example: 192.168.1.42 or supervisely.intranet. If you have a very restrictive network environment with firewall, you can use
ip addr list docker0 to get your docker0 IP address - it is some kind of
localhost for docker.
STORAGE_ACCESS_KEY - Access key for S3 integration (optional)
STORAGE_SECRET_KEY - Secret key for S3 integration (optional)
sudo supervisely login to login to our private docker registry. We provide the necessary credentials in
.env file so you don't have to input anything.
You should see "Login Succeeded" in your terminal.
Now it's time to start the Supervisely instance. Run
sudo supervisely upgrade --skip-backup. We will pull docker images from registry and run every service defined in
docker-compose.yml in detached mode.
This can take a while.
When the script has finished, you can open Supervisely in your web browser. Go to
http://localhost page. You will see the following:
Enter your license key from the file
license.key here and press "Update license".
You should see a "Success" message - your license has been updated.
Now you can refresh the current page. You will see the login box:
Enter the default credentials: login and password
admin. Now your are in. Welcome!
Database is still initializing. Wait a minute and try again.
One of our internal services haven't got the license key. Refresh the page and try again.
It seems the agent has tried to wait for Supervisely to start. Restart agent manually:
sudo supervisely restart agent.
Nvidia container runtime has not been installed. Try to run command
docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi to see if it works.
If you don't want to run train & inference tasks, please remove checkbox "Use nvidia runtime" under advances settings in the new node modal window and run the deploy command again.
The models are still downloading. It can take a while, just wait some time and refresh the Models list.