MMS Usage & Obtaining Preview Model Resources

Using mms requires a Model Farm account login. Please visit Model Farm Account Registration

System Dependency Configuration

Configure the AidLux Package Source

# Download the correct public key
sudo wget -O- https://archive.aidlux.com/ubuntu24/public.key | gpg --dearmor | sudo tee /etc/apt/trusted.gpg.d/private-aidlux.gpg > /dev/null

# Edit the source list file
sudo vim /etc/apt/sources.list.d/private-aidlux.list

# Add the repository provided by AidLux to the source file
deb [arch=arm64 signed-by=/etc/apt/trusted.gpg.d/private-aidlux.gpg] https://archive.aidlux.com/ubuntu24 noble main

# Update the package cache
sudo apt update

After the update is complete, you can use the following command to retrieve the official AidLux SDK dependencies:

sudo apt list | grep aid | grep unknown

# Install software
# Must be installed first (not included with the system)
sudo apt install python3 python3-pip libopencv-dev python3-opencv net-tools
# Must be installed before aidlite
sudo apt install aidlux-aistack-base aidrtcm

# Install aidlite and dependencies
sudo apt install aid-lms aidlms-sdk aidlite-sdk cmake
sudo apt-get install libfmt-dev nlohmann-json3-dev
sudo apt install aidlite-*

# DSP support
sudo apt-get install qcom-fastrpc1
sudo apt-get install qcom-fastrpc-dev

# Install aidgen-sdk
sudo apt install aidgen-qnn240-sdk

# Install mms service
sudo apt install aid-mms

# GPU support
sudo add-apt-repository ppa:ubuntu-qcom-iot/qcom-noble-ppa
sudo apt install qcom-adreno-cl1
sudo ln -s /usr/lib/aarch64-linux-gnu/libOpenCL.so.1 /usr/lib/aarch64-linux-gnu/libOpenCL.so

After installation, check that the aidlite and aidgen directories have been added under /usr/local/share:

Device Authorization

Obtain the Device Serial Number

cat  /sys/devices/soc0/serial_number

Obtain the License File

Provide the serial number to APLUX technical staff to generate a device-specific License file, then place it in the /etc/opt/aidlux/license/AidLuxLics directory.

MMS Usage

MMS is a component provided for APLUX development board users. It allows logging into Model Farm via the command line, querying and downloading model files. The specific workflow is as follows:

1. Login

mms login

# Enter your username:
# Enter your password:

# After entering the correct credentials, you will see:
# Login successfully.

2. Model Search

# List all models
mms list

# Search for models by model name
mms list yolo
Model        Precision  Chipset           Backend
-----        ---------  -------           -------
YOLO-NAS-l   FP16       Qualcomm QCS8550  QNN2.29
YOLO-NAS-l   INT8       Qualcomm QCS6490  QNN2.29
YOLO-NAS-l   INT8       Qualcomm QCS8550  QNN2.29
YOLO-NAS-l   W8A16      Qualcomm QCS6490  QNN2.29
YOLO-NAS-l   W8A16      Qualcomm QCS8550  QNN2.29
YOLO-NAS-m   FP16       Qualcomm QCS8550  QNN2.29
YOLO-NAS-m   INT8       Qualcomm QCS6490  QNN2.29
YOLO-NAS-m   INT8       Qualcomm QCS8550  QNN2.29

3. Model Download

# -m: Model name
# -p: Model precision
# -c: Chip
# -b: QNN version
# Download yolov6l model with INT8 data precision, optimized for QCS8550 chip platform, using QNN2.23 inference framework
mms get -m yolov6l -p int8 -c qcs8550 -b qnn2.23


Downloading model from https://aiot.aidlux.com to directory: /var/opt/modelfarm_models

Downloading [yolov6l_qcs8550_qnn2.23_int8_aidlite.zip] ... done! [40.45MB in 375ms; 81.51MB/s]

Download complete!

Obtaining Preview Model Resources

Models in the Model Farm Preview section do not support web downloads. Developers can download Preview models on APLUX development boards using the mms command. Here is an example using MobileClip2-S3:

1. Log in via mms

mms login

# Enter your username:
# Enter your password:

# After entering the correct credentials, you will see:
# Login successfully.

2. Search for models

mms list mobileclip # Supports keyword search

# You will see output similar to:
Model           Precision  Chipset           Backend
-----           ---------  -------           -------
MobileClip-S2   FP16       Qualcomm QCS8550  QNN2.31
MobileClip2-S3  FP16       Qualcomm QCS8550  QNN2.36