| ## Installation |
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| This section provides a tutorial on building a working environment for `LibContinual` from scratch. |
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| ## Get the `LibContinual` library |
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| Use the following command to get `LibContinual`: |
|
|
| ```shell |
| cd ~ |
| git clone https://github.com/RL-VIG/LibContinual.git |
| ``` |
|
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| ## Configure the `LibContinual` environment |
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| The environment can be configured in any of the following ways: |
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| 1. conda(recommend) |
| ```shell |
| cd <path-to-LibContinual> # cd in `LibContinual` directory |
| conda env create -f requirements.yaml |
| ``` |
| |
| 2. pip |
| ```shell |
| cd <path-to-LibContinual> # cd in `LibContinual` directory |
| pip install -r requirements.txt |
| ``` |
| 3. or whatever works for you as long as the following package version conditions are meet: |
| ``` |
| diffdist==0.1 |
| numpy==1.21.5 |
| pandas==1.1.5 |
| Pillow==9.2.0 |
| PyYAML==6.0.1 |
| scikit_learn==1.0.2 |
| torch==1.12.1 |
| torchvision==0.13.1 |
| tqdm==4.64.1 |
| python==3.8.0 |
| timm=0.6.7 |
| ``` |
| |
| ## Test the installation |
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| 1. set the `config` as follows in `run_trainer.py`: |
| ```python |
| config = Config("./config/lucir.yaml").get_config_dict() |
| ``` |
| 2. modify `data_root` in `config/lucir.yaml` to the path of the dataset to be used. |
| 3. run code |
| ```shell |
| python run_trainer.py |
| ``` |
| 4. If the first output is correct, it means that `LibContinual` has been successfully installed. |
| |
| ## Next |
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