deepfake-image-detection Model by Hive

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Step 1
Generate API Key

Step 2
Pull and Run the NIM

Bash

$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Get the credentials to download the models from Hive and export them:

Bash

export NIM_REPOSITORY_OVERRIDE="s3://..."
export AWS_REGION="..."
export AWS_ACCESS_KEY_ID="..."
export AWS_SECRET_ACCESS_KEY="..."

Pull and run the NVIDIA NIM with the command below.

Bash

# Create the cache directory on the host machine.
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
chmod 777 $LOCAL_NIM_CACHE

# Run the container with the cache directory as a volume mount.
docker run -it --rm --name=nim-server \
   --runtime=nvidia \
   --gpus='"device=0"' \
   -e NIM_REPOSITORY_OVERRIDE \
   -e AWS_REGION \
   -e AWS_ACCESS_KEY_ID \
   -e AWS_SECRET_ACCESS_KEY \
   -e NIM_HTTP_API_PORT=8003 \
   -p 8003:8003 \
   -p 8002:8002 \
   -v "$LOCAL_NIM_CACHE:/opt/nim/.cache/" \
   nvcr.io/nim/hive/deepfake-image-detection:latest

Step 3
Test the NIM

You can now make a local API call using this curl command:

Bash

invoke_url="http://localhost:8003/v1/infer"
input_image_path="input.jpg"

# download an example image
curl https://assets.ngc.nvidia.com/products/api-catalog/deepfake-image-detection/input/deepfake.jpg > $input_image_path

image_b64=$(base64 $input_image_path)
length=${#image_b64}

echo '{
    "input": ["data:image/png;base64,'${image_b64}'"],
     "return_image": false
}' > payload.json
  
curl $invoke_url \
-H "Content-Type: application/json" \
-d @payload.json

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.

deepfake-image-detection

Step 1Generate API Key

Step 2Pull and Run the NIM

Step 3Test the NIM

Step 1
Generate API Key

Step 2
Pull and Run the NIM

Step 3
Test the NIM