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@@ -243,17 +243,17 @@ You can check the performance of model with `models/my_model/noise1_scale2.0x_be
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( Docker image is available at https://hub.docker.com/r/nagadomi/waifu2x )
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( Docker image is available at https://hub.docker.com/r/nagadomi/waifu2x )
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-Requires `nvidia-docker`.
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+Requires [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).
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```
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```
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docker build -t waifu2x .
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docker build -t waifu2x .
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-nvidia-docker run -p 8812:8812 waifu2x th web.lua
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-nvidia-docker run -v `pwd`/images:/images waifu2x th waifu2x.lua -force_cudnn 1 -m scale -scale 2 -i /images/miku_small.png -o /images/output.png
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+docker run --gpus all -p 8812:8812 waifu2x th web.lua
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+docker run --gpus all -v `pwd`/images:/images waifu2x th waifu2x.lua -force_cudnn 1 -m scale -scale 2 -i /images/miku_small.png -o /images/output.png
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```
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```
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Note that running waifu2x in without [JIT caching](https://devblogs.nvidia.com/parallelforall/cuda-pro-tip-understand-fat-binaries-jit-caching/) is very slow, which is what would happen if you use docker.
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Note that running waifu2x in without [JIT caching](https://devblogs.nvidia.com/parallelforall/cuda-pro-tip-understand-fat-binaries-jit-caching/) is very slow, which is what would happen if you use docker.
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For a workaround, you can mount a host volume to the `CUDA_CACHE_PATH`, for instance,
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For a workaround, you can mount a host volume to the `CUDA_CACHE_PATH`, for instance,
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```
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```
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-nvidia-docker run -v $PWD/ComputeCache:/root/.nv/ComputeCache waifu2x th waifu2x.lua --help
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+docker run --gpus all -v $PWD/ComputeCache:/root/.nv/ComputeCache waifu2x th waifu2x.lua --help
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```
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```
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nagadomi