This tutorial uses new
supervisely-cli util. Old version of this tutorial can be found here.
Follow those simple steps to install new instance of Supervisely to your server.
Before you start installation of Supervisely to your host, please make sure your server meets hardware & software requirements below.
Minimum hardware requirements are the following:
- CPU: 4 vCPUs / cores
- Memory: 8 GB RAM
- Hard drive: 100 GB storage or more
Choose hard drive size according to your needs. There is also an option to attach any S3 compatible storage to distribute data in your cloud.
Change storage path if necessary
If don't have enough free space on a system drive, but you have mounted another, you can change place where the data is stored by changing
GPU instance deployment¶
If you plan to use smart tool (AI powered semantic segmentation) or model zoo (human-in-the-loop) you will need a server with GPU. Requirements are the following:
- GPU Memory: 8 GB or more (GeForce GTX 1080, Tesla K80, Tesla P100)
You can install Supervisely and run GPU computations on a different machines using agents (see cluster).
AWS & cloud providers¶
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, Ubuntu 14.04, Debian Jessie. All other pre-requirements can be installed via
When all requirements above have been installed, you can deploy Supervisely.
Step 1. Get your unique key from us¶
Before installation we will send you
license.key, unique installation key and command to install
supervise-cli. Please run it on machine where you want to install Supervisely and make sure
sudo supervisely gives you help information.
Step 2. Install pre-requirements¶
sudo supervisely install-all in your terminal. We will detect necessary dependencies and install them. The following software may be installed:
- Docker CE
- Docker Compose
- CUDA 9.0 & Nvidia Driver (for Training Module)
- NVIDIA Container Runtime (for Training Module)
If don't have NVIDIA drivers and CUDA, you will be asked if need to install it. Choose
y if you have GPU on your server. Your computer will be rebooted during installation of NVIDIA driver.
Step 3. Download configuration¶
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. Be default, we download it into a
Step 4. Edit configuration¶
/opt/supervisely/.env. There are some configuration variables 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 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
STORAGE_ACCESS_KEY- Access key for S3 integration (optional)
STORAGE_SECRET_KEY- Secret key for S3 integration (optional)
Step 5. Login to Docker registry¶
sudo supervisely login to login to our private docker registry. We provide necessary credentials in
.env file to you don't have to input anything.
If you see "syntax error", you forgot to set one of the variables in
.env. Please go to previous step to edit required variables.
You should see "Login Succeeded" in your terminal.
Step 6. Run Supervisely¶
Now it's time to start Supervisely instance. Run
sudo supervisely up -d. We will pull docker images from registry and run every service defined in
docker-compose.yml in detached mode.
This can take a while, so you please relax and grab yourself come coffee .
Step 7. Enter license key¶
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 file
license.key here and press "Update license".
You should see a "Success" message - your license has been updated.
Step 8. Start using Supervisely!¶
Now you can refresh current page. You will see login box:
Enter default credentials: login and password
admin. Now your are in. Welcome!
We strongly advice you to complete post-installation steps after initial setup.
I open "http://localhost" but see an error¶
Database is still initializing. Wait a minute and try again.
I try to login but see "You have no permissions for this action. Please, contact administrator."¶
One of our internal services haven't got the license key. Refresh the page and try again.
My "admin / Main Node" agent is stuck in "Waiting" status¶
It seems agent has tired to wait for Supervisely to start. Restart agent manually:
sudo supervisely restart agent.
When i deploy an agent i see a "runtime" error¶
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 new node modal window and run deploy command again.
Import fails with
Please make sure that you have drag-and-dropped a folder and not files itself.
There are no or just a few models in Model Zoo¶
Models are still downloading. It can take a while, just wait some time and refresh Model Zoo page.