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- Use-case A: Inference AI di GPU (Python + PyTorch/Tastor) dengan TensorRT deployment. - Use-case B: Pemrosesan video real-time menggunakan NVENC/NVDEC (C++/FFmpeg binding). - Use-case C: Pipeline data AI edge dengan Jetson (Python/C++).

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Worker Basic

This is a minimal serverless worker example. You can use the provided code to build a Docker image and deploy it as a serverless endpoint. When a request is sent to the endpoint, a worker spins up and executes the rp_handler.py script. You can replace the sleep function with any machine learning task, such as image generation, text generation, or speech-to-text conversion.

To test this code locally:

# 1. Create a Python virtual environment
python3 -m venv venv

# 2. Activate the virtual environment
# On macOS/Linux:

source venv/bin/activate

# On Windows:
venv\Scripts\activate

# 3. Install the RunPod SDK
pip install runpod

# 4. Run your script locally, the script will automatically read test_input.json as input, passing it to the handler function as an event
python3 rp_handler.py

Build and Push Docker Image to a Container Registry (e.g., Docker Hub)

# Build docker image
docker build -t your-dockerhub-username/your-image-name:v1.0.0 --platform linux/amd64 .

# Push docker image to docker hub
docker push your-dockerhub-username/your-image-name:v1.0.0

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- Use-case A: Inference AI di GPU (Python + PyTorch/Tastor) dengan TensorRT deployment. - Use-case B: Pemrosesan video real-time menggunakan NVENC/NVDEC (C++/FFmpeg binding). - Use-case C: Pipeline data AI edge dengan Jetson (Python/C++).

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  • Python 73.3%
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