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## Running the Simulation | ||
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There are three ways to run the simulation: | ||
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1. **Using Docker with Local Setup** | ||
The `Dockerfile` and environment configuration file `environment.yml` are included in the project. You can build your own Docker image and container with the following steps: | ||
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- Open a terminal in the `RGNN_RL` directory and build the Docker image (ensure the `.` at the end is included): | ||
```bash | ||
docker build -t analoggym_rgcn . | ||
``` | ||
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- After the image is built, create a Docker container with the following command. The `-v` flag links the local path (`/xxx/rgcn_rl`) to the container's internal path (`/app`), allowing real-time file modification. The `-it` flag opens an interactive terminal: | ||
```bash | ||
docker run -v /xxx/rgnn_rl:/app -it analoggym_rgcn /bin/bash | ||
``` | ||
- Inside the container, check if the `analoggym-env` is activated. If not, activate it with: | ||
```bash | ||
conda activate analoggym-env | ||
``` | ||
2. **Using Pre-Built Docker Images from Docker Hub** | ||
A quicker method is to pull the pre-built Docker image from Docker Hub and run the simulation directly. Use the following command to pull the image: | ||
```bash | ||
docker pull chenzhenxin/analoggym_rgcn:latest | ||
``` | ||
After pulling, refer to the steps in Method 1 to create a container and run the simulation. | ||
3. **Running Locally for Precise Simulation Results** | ||
Docker might not resolve ngspice version issues, so for precise results, you can run the simulation locally. First, create a conda virtual environment and install the required packages: | ||
```bash | ||
conda install -c conda-forge ngspice | ||
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 cpuonly -c pytorch | ||
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.13.1+cpu.html | ||
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.13.1+cpu.html | ||
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.13.1+cpu.html | ||
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.13.1+cpu.html | ||
pip install torch-geometric | ||
pip install gymnasium | ||
pip install tabulate | ||
pip install numpy | ||
pip install matplotlib | ||
pip install lPython | ||
``` | ||
For details on resolving ngspice version issues, refer to the Supplementary Information. Once the environment is set up, you can run: | ||
```bash | ||
python main_AMP.py | ||
``` | ||
or | ||
```bash | ||
python main_LDO.py | ||
``` | ||
to start the simulation. | ||
``` | ||
This version is more concise, polished, and suitable for a README file. It maintains a professional tone while clearly explaining the steps. |