Advanced Settings
Applying Environment Variables(EV) to Run Pipeline
EV can be applied in two ways:
1. As a Docker Compose environment parameter input
2. In the env files
The input parameter will override the one in the env files if both are used.
Run with Custom Environment Variables
Environment variables with make commands
Example
make PIPELINE_SCRIPT=yolov5s_effnetb0.sh RESULTS_DIR="../render_results" run-render-mode
Environment variable with docker compose up
Example
PIPELINE_SCRIPT=yolov5s_effnetb0.sh RESULTS_DIR="../render_results" docker compose -f src/docker-compose.yml --env-file src/res/yolov5-cpu.env up -d
Note
The environment variables set like this are known as command line environment overrides and are applied to this run only.
They will override the default values in env files and docker-compose.yml.
Editing the Environment Files
Environment variable files can be used to persist environment variables between deployments. You can find these files in src/res/ folder with our default environment variables for Automated Self Checkout.
| Environment File | Description |
|---|---|
src/res/all-cpu.env |
Runs pipeline on CPU for decoding, pre-processing, and inferencing |
src/res/all-gpu.env |
Runs pipeline on GPU for decoding, pre-processing, and inferencing |
src/res/all-dgpu.env |
Runs pipeline on discrete GPU for decoding, pre-processing, and inferencing |
src/res/all-npu.env |
Runs pipeline on NPU for inferencing only |
src/res/yolov5-cpu-class-gpu.env |
Uses CPU for detection and GPU for classification |
src/res/yolov5-gpu-class-cpu.env |
Uses GPU for detection and CPU for classification |
After modifying or creating a new .env file you can load the .env file through the make command or docker compose up
Make
make PIPELINE_SCRIPT=yolov5s_effnetb0.sh DEVICE_ENV=res/all-gpu.env run-render-mode
Docker compose
docker compose -f src/docker-compose.yml --env-file src/res/yolov5-cpu-class-gpu.env up -d
Environment Variables (EVs)
The table below lists the environment variables (EVs) that can be used as inputs for the container running the inferencing pipeline.
This list of EVs is for running through the make file or docker compose up
| Variable | Description | Values |
|---|---|---|
DEVICE_ENV |
Path to device specific environment file that will be loaded into the pipeline container | res/all-gpu.env |
DOCKER_COMPOSE |
The docker-compose.yml file to run | src/docker-compose.yml |
RETAIL_USE_CASE_ROOT |
The root directory for Automated Self Checkout in relation to the docker-compose.yml | .. |
RESULTS_DIR |
Directory to output results | ../results |
This list of parameters that can be set when running docker compose up
| Variable | Description | Values |
|---|---|---|
-v |
Volume binding for containers in the Docker Compose | -v results/:/tmp/results |
-e |
Override environment variables inside of the Docker Container | -e LOG_LEVEL debug |
This list of EVs is common for all profiles.
| Variable | Description | Values |
|---|---|---|
BARCODE_RECLASSIFY_INTERVAL |
time interval in seconds for barcode classification | Ex: 5 |
BATCH_SIZE |
number of frames batched together for a single inference to be used in gvadetect batch-size element | 0-N |
CLASSIFICATION_OPTIONS |
extra classification pipeline instruction parameters | "", "reclassify-interval=1 batch-size=1 nireq=4 gpu-throughput-streams=4" |
DETECTION_OPTIONS |
extra object detection pipeline instruction parameters | "", "ie-config=NUM_STREAMS=2 nireq=2" |
GST_DEBUG |
for running pipeline in gst debugging mode | 0, 1 |
LOG_LEVEL |
log level to be set when running gst pipeline | ERROR, INFO, WARNING, and more |
OCR_RECLASSIFY_INTERVAL |
time interval in seconds for OCR classification | Ex: 5 |
RENDER_MODE |
for displaying pipeline and overlay CV metadata | 1, 0 |
PIPELINE_COUNT |
Number of Automated Self Checkout Docker container instances to launch | Ex: 1 |
PIPELINE_SCRIPT |
Pipeline script to run. | yolov5s.sh, yolov5s_effnetb0.sh, yolov5s_full.sh |