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4. If you have a [CUDA-supported GPU](https://developer.nvidia.com/cuda-gpus), you can speed up the training and inference:
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3.1 Install **NVIDIA Container Toolkit** as described [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-with-apt). NVIDIA Container Toolkit is supported as written [here](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#nvidia-compute-software-support-on-wsl-2).
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3.2 Add _nvidia_ as runtime in Docker Engine daemon config (see _Docker Desktop_ -> _Change settings_ -> _Docker Engine_). Add these lines to your config:
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```json
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"runtimes": {
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"nvidia": {
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"path": "nvidia-container-runtime",
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"runtimeArgs": []
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```
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For example, the full Docker Engine daemon config json code should look like that:
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```json
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{
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"builder": {
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"gc": {
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"defaultKeepStorage": "20GB",
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"enabled": true
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}
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},
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"experimental": false,
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"runtimes": {
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"nvidia": {
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"path": "nvidia-container-runtime",
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"runtimeArgs": []
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}
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}
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}
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```
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and restart Docker Desktop.
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4.1 Install **NVIDIA Container Toolkit** as described [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-with-apt). NVIDIA Container Toolkit is supported as written [here](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#nvidia-compute-software-support-on-wsl-2).
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4.2 Add _nvidia_ as runtime in Docker Engine daemon config (see _Docker Desktop_ -> _Change settings_ -> _Docker Engine_). Add these lines to your config:
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```json
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"runtimes": {
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"nvidia": {
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"path": "nvidia-container-runtime",
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"runtimeArgs": []
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```
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For example, the full Docker Engine daemon config json code should look like that:
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```json
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{
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"builder": {
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"gc": {
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"defaultKeepStorage": "20GB",
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"enabled": true
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}
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},
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"experimental": false,
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"runtimes": {
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"nvidia": {
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"path": "nvidia-container-runtime",
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"runtimeArgs": []
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}
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}
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}
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```
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and restart Docker Desktop.
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5. Type `code .` in the terminal to open the project in VS Code. Alternatively, you can launch VS Code and select the project to open from the UI.
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