Site commands

Site commands enable you to interact directly with the deployed Deepomatic sites from your terminal. Here is the list of actions that you can do:
create / delete
manifest
install / uninstall
show
current (start, stop, logs, status)
list / save / load
update / upgrade / rollback
intervention
model (infer, draw, blur)
Create / Delete
To create a site, you need to use the following command and to specify a name and the application version that you want to deploy on your site. You can then change this application version.
Create a site
deepo site create -n my-super-site -v app_version_id
To delete a site, you need to use the following command and to specify the Site Id that you want to delete.
Delete a site
deepo site delete -s site_id
Manifest
To obtain the manifest necessary for the installation of your site, you must use the following command, specifying the Site Id and the type of manifest that you want to fit with your deployment.
Get manifest for a site
deepo site manifest -i site_id -t gke
The target manifest are:
gke: Google Kubernetes Engine
docker-compose
Install / Uninstall
Run locally the following command. The site will be installed using the latest docker-compose / kubernetes manifest downloaded from the API.
Install a site
deepo site install
Uninstalling a site removes all versions and files from a site.
Uninstall a site
deepo site uninstall
Show
The show command displays the currently deployed site's id, and the id of the currently installed application.
Display current site id and installed application 
deepo site show
Current
The current command lets you use the following submenu for the currently deployed site:
Start current site
deepo site current start
Stop current site
deepo site current stop
Get logs from current site
deepo site current logs
Get status of current site
deepo site current status
List / Save / Load
The list command returns the ids of the sites that have been installed.
List installed sites
deepo site list
The save command creates an archive that contains the given site, which can been installed on another server (e.g. offline)
Save a site
deepo site save -s site_id -a achive_path
The load command loads a site from an archive generated by a save command.
Load a site
deepo site load -a achive_path
Upgrade / Rollback
The upgrade command install a new version of the site (using the latest docker-compose/kubernetes manifest from the api)
Upgrade a site
deepo site upgrade -s site_id -a app_version_id
The rollback command reloads the nth previous version (upgrade followed by rollback 1 does nothing)
Rollback a site
deepo site rollback -s site_id -n rollback_index
Intervention
The intervention command lets you interact with the customer API. There must then be a customer-api service in your application to work. You can then use the create / delete / infer / status commands.
Create an intervention
deepo site intervention create
Delete an intervention
deepo site intervention delete
Add an image to an intervention
deepo site intervention infer
Get intervention status
depeo site intervention status
Model
When you deploy an application version on a site, it is possible to run inferences on the model version included in the application version. 
Get your Model Version ID and your Routing Key
To retrieve the MODEL_VERSION_ID of the model version that you want to use within your application and the corresponding Routing Key, you need to go on the Apps tab in the Deployment section and click on the app version that is deployed on your site. 
The MODEL_VERSION_ID  is specified with the -r or  --recognition_id argument.
The ROUTING_KEY corresponds to the rabbitMQ queue identifier.
You will need to provide one additional parameters to run model actions: the AMPQ_URL. It corresponds to the rabbitMQ address. You will find this address in your server deployment files. It should look like amqp://user:pwd@ip:port/vhost  
This parameter will be defined during the GPU server setup. Besides, you don't need to specify any credentials.
Sample commands
Running an inference with on-premises Server
deepo site model infer -i img.jpg -o pred.json -r 123 -k queue -u amqp://user:pwd@ip:port/vho
Model actions
There are four different model actions:
infer: Compute predictions only.
draw: Display the prediction result, whether tags or the bounding boxes.
blur: Blur the bounding boxes.
noop: Benchmark input and output processing capabilities.
They follow the same recipe:
Retrieve one or several inputs.
Compute predictions using the trained neural network
Output the result in different formats: image, video, JSON, stream, etc.
Generic command
deepo site model infer -i myinput -o myoutput1 myoutput2 ...
Input
Input types
The Deepomatic CLI supports different types of input:
Image: Supported formats include bmp, jpeg, jpg , jpe,png, tif and tiff.
Video: Supported formats include avi, mp4, webm and mjpg.
Studio JSON: Deepomatic Studio JSON format, used to specify several images or videos.
Sample input JSON format
{
  "images": [
  	{
  		"location": "/path/to/img.jpg"
  	},
  	{
  		"location": "/path/to/video.mp4"
  	},
  ]
}
Directory: Analyse all images and videos found in the directory.
Digit: Retrieve the stream from the corresponding device. For instance, 0 for the installed webcam.
Network stream: Supported network streams include rtsp, http and https.
Specify input
Inputs are specified using the -i for input option. Below an example with each type of inputs.
Sample input commands
deepo site model infer -i /path/to/my_img.bmp ...        # Image
deepo site model infer -i /path/to/my_vid.mp4 ...        # Video
deepo site model infer -i /path/to/my_studio.json ...    # Studio JSON
deepo site model infer -i /path/to/my_dir ...            # Directory
deepo site model infer -i 0 ...                          # Device number
deepo site model infer -i rtsp://ip:port/channel ...     # RTSP stream
Output
Output types
The Deepomatic CLI supports different types of output:
Image: Supported formats include bmp, jpeg, jpg , jpe,png, tif and tiff.
Video: Supported formats include avi and mp4.
Run JSON: Deepomatic Run JSON format for raw predictions.
Studio JSON: Deepomatic Studio JSON format for Studio-like prediction scores. This is specified using the -s or --studio_format option.
Integer wildcard JSON: A standard Run/Studio JSON, except that the name contains the frame number. For instance -o frame %03d.json will output frame001.json, frame002.json, ...
String wildcard JSON: Same as the integer wildcard except this time the frame name is used. For instance -o pred_%s.jpg will output pred_img1_123.json, pred_img2_123.json, ...
Standard output: On rare cases you might want to output the model results directly to the process standard output using the stdout option. For instance this allows you to stream directly to vlc.
Display output: Opens a window and displays the result. Quit with q.
Specify output
Output are specified using the -o for output option. Below an example with each type of inputs.
Please note that in order to avoid duplicate computations, it is possible to specify several outputs at the same time, for instance to blur an image and store the predictions.
Sample output commands
deepo site model draw -i img.jpg -o img_drawn.jpg ...         # Image
deepo site model draw -i vid.mp4 -o img_drawn_%04d.jpg ...    # Wildcard images
deepo site model draw -i vid.mp4 -o vid_drawn.mp4 ...         # Video
deepo site model draw -i img.jpg -o pred.json ...             # Run JSON
deepo site model draw -i img.jpg -o pred.json -s  ...         # Studio JSON
deepo site model draw -i vid.mp4 -o pred_%s.json ...          # String wildcard JSON
deepo site model draw -i vid.mp4 -o pred_%04d.json ...        # Integer wildcard JSON
deepo site model draw -i vid.mp4 -o stdout ...                # Standard output
deepo site model draw -i vid.mp4 -o window ...                # Display output
deepo site model draw -i vid.mp4 -o vid_drawn.mp4 pred_%04d.json  ...  # Multiple outputs
Options
Commands have additional options that you can use with a flag. There is a short flag -f and a long flag --flag. Note that one use a simple - while the other uses two --. Also some options need an additional argument. Find below the option table. When indicated, all means that all four commands infer, draw , blur and noop are concerned.
Short
Long
Commands
Description
i
input
all
Input consummed.
​
input_fps
all
Input FPS used for video extraction.
​
skip_frame
all
Number of frames to skip inbetween two frames.
R
recursive
all
Recursive directory search.
o
output
all
Outputs produced.
​
output_fps
all
Output FPS used for video reconstruction.
s
studio_format
infer draw blur
Convert from Run to Studio format.
F
fullscren
draw blur noop
Fullscreen if window output.
​
from_file
draw blur
Use prediction from precomputed JSON.
r
recognition_id
infer draw blur
Neural network recognition version ID.
t
threshold
infer draw blur
Threshold for predictions.
u
ampq_url
infer draw blur
AMQP url for on-premises deployments.
k
routing_key
infer draw blur
Recognition routing key for on-premises deployments.
S
draw_score
draw
Overlay prediction score.
​
no_draw_scores
draw
Do not overlay prediction score.
L
draw_labels
draw
Overlay the prediction label.
​
no_draw_labels
draw
Do not overlay the prediction label.
M
blur_method
blur
Blur method,pixel, gaussian or black.
B
blur_strengh
blur
Blur strength.
​
verbose
all
Increase output verbosity.
 

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FAQ

Last updated 1 month ago

Short	Long	Commands	Description
`i`	`input`	`all`	Input consummed.
	`input_fps`	`all`	Input FPS used for video extraction.
	`skip_frame`	`all`	Number of frames to skip inbetween two frames.
`R`	`recursive`	`all`	Recursive directory search.
`o`	`output`	`all`	Outputs produced.
	`output_fps`	`all`	Output FPS used for video reconstruction.
`s`	`studio_format`	`infer` `draw` `blur`	Convert from Run to Studio format.
`F`	`fullscren`	`draw` `blur` `noop`	Fullscreen if window output.
	`from_file`	`draw` `blur`	Use prediction from precomputed JSON.
`r`	`recognition_id`	`infer` `draw` `blur`	Neural network recognition version ID.
`t`	`threshold`	`infer` `draw` `blur`	Threshold for predictions.
`u`	`ampq_url`	`infer` `draw` `blur`	AMQP url for on-premises deployments.
`k`	`routing_key`	`infer` `draw` `blur`	Recognition routing key for on-premises deployments.
`S`	`draw_score`	`draw`	Overlay prediction score.
	`no_draw_scores`	`draw`	Do not overlay prediction score.
`L`	`draw_labels`	`draw`	Overlay the prediction label.
	`no_draw_labels`	`draw`	Do not overlay the prediction label.
`M`	`blur_method`	`blur`	Blur method,`pixel`, `gaussian` or `black.`
`B`	`blur_strengh`	`blur`	Blur strength.
	`verbose`	`all`	Increase output verbosity.