Under `MACROPLACEMENT` main directory, run the following command:
```
python -m Plc_client.plc_client_os_test
```
## What do we open-source here?
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@@ -22,11 +21,11 @@ The current Circuit Training framework requires user to download an executable b
## How do I open-source?
All current progress can be reviewed [here](https://github.com/Dinple/circuit_training_os/blob/main/circuit_training/environment/plc_client_os.py). I have generated numerous testcases, varying from a few macros to hundreds of different modules. The purpose of these testcases is to study the behavior of <em>plc_wrapper_main</em> in a scalable way. I have also set up testbench to compare my result to the result from [plc_client.py](https://github.com/Dinple/circuit_training_os/blob/main/circuit_training/environment/plc_client.py).
All current progress can be reviewed [here](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/plc_client-open-source/Plc_client/plc_client_os.py). I have generated numerous testcases, varying from a few macros to hundreds of different modules. The purpose of these testcases is to study the behavior of <em>plc_wrapper_main</em> in a scalable way. I have also set up testbench to compare my result to the result from [plc_client.py](https://github.com/google-research/circuit_training/blob/main/circuit_training/environment/plc_client.py).
## What is the end-goal?
The first step and the current step of this open-source effor is to reproduce similar results to Google's <em>plc_wrapper_main</em> in the testbench. The final step will be plugging [plc_client_os.py](https://github.com/Dinple/circuit_training_os/blob/main/circuit_training/environment/plc_client_os.py) into the Circuit Training Framework and reproduce a quality training.
The first step and the current step of this open-source effor is to reproduce similar results to Google's <em>plc_wrapper_main</em> in the testbench. The final step will be plugging [plc_client_os.py](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/plc_client-open-source/Plc_client/plc_client_os.py) into the Circuit Training Framework and reproduce a quality training.
## Reference
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@@ -58,4 +57,4 @@ The first step and the current step of this open-source effor is to reproduce si
