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Update TORCH_CUDA_VERSION usage (#1284)

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Guillaume Lagrange 2024-02-10 12:01:45 -05:00 committed by GitHub
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commit 8f753b3aae
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5 changed files with 5 additions and 5 deletions
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2
burn-tensor/README.md
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@ -50,7 +50,7 @@ Therefore, creating the tape only requires a simple and efficient graph traversa
## Cuda ## Cuda
To run with CUDA set `TORCH_CUDA_VERSION=cu113`. To run with CUDA set `TORCH_CUDA_VERSION=cu121`.
## Notes ## Notes

2
examples/mnist/README.md
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@ -17,7 +17,7 @@ cargo run --example mnist --release --features ndarray # CPU NdAr
cargo run --example mnist --release --features ndarray-blas-openblas # CPU NdArray Backend - f32 - blas with openblas cargo run --example mnist --release --features ndarray-blas-openblas # CPU NdArray Backend - f32 - blas with openblas
cargo run --example mnist --release --features ndarray-blas-netlib # CPU NdArray Backend - f32 - blas with netlib cargo run --example mnist --release --features ndarray-blas-netlib # CPU NdArray Backend - f32 - blas with netlib
echo "Using tch backend" echo "Using tch backend"
export TORCH_CUDA_VERSION=cu113 # Set the cuda version export TORCH_CUDA_VERSION=cu121 # Set the cuda version
cargo run --example mnist --release --features tch-gpu # GPU Tch Backend - f32 cargo run --example mnist --release --features tch-gpu # GPU Tch Backend - f32
cargo run --example mnist --release --features tch-cpu # CPU Tch Backend - f32 cargo run --example mnist --release --features tch-cpu # CPU Tch Backend - f32
echo "Using wgpu backend" echo "Using wgpu backend"

2
examples/simple-regression/README.md
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@ -23,7 +23,7 @@ cargo run --example regression --release --features ndarray # CPU
cargo run --example regression --release --features ndarray-blas-openblas # CPU NdArray Backend - f32 - blas with openblas cargo run --example regression --release --features ndarray-blas-openblas # CPU NdArray Backend - f32 - blas with openblas
cargo run --example regression --release --features ndarray-blas-netlib # CPU NdArray Backend - f32 - blas with netlib cargo run --example regression --release --features ndarray-blas-netlib # CPU NdArray Backend - f32 - blas with netlib
echo "Using tch backend" echo "Using tch backend"
export TORCH_CUDA_VERSION=cu113 # Set the cuda version export TORCH_CUDA_VERSION=cu121 # Set the cuda version
cargo run --example regression --release --features tch-gpu # GPU Tch Backend - f32 cargo run --example regression --release --features tch-gpu # GPU Tch Backend - f32
cargo run --example regression --release --features tch-cpu # CPU Tch Backend - f32 cargo run --example regression --release --features tch-cpu # CPU Tch Backend - f32
echo "Using wgpu backend" echo "Using wgpu backend"

2
examples/text-classification/README.md
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@ -29,7 +29,7 @@ cd burn
# Use the --release flag to really speed up training. # Use the --release flag to really speed up training.
# Use the f16 feature if your CUDA device supports FP16 (half precision) operations. May not work well on every device. # Use the f16 feature if your CUDA device supports FP16 (half precision) operations. May not work well on every device.
export TORCH_CUDA_VERSION=cu117 # Set the cuda version (CUDA users) export TORCH_CUDA_VERSION=cu121 # Set the cuda version (CUDA users)
# AG News # AG News
cargo run --example ag-news-train --release --features tch-gpu # Train on the ag news dataset cargo run --example ag-news-train --release --features tch-gpu # Train on the ag news dataset

2
examples/text-generation/README.md
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@ -14,7 +14,7 @@ git clone https://github.com/tracel-ai/burn.git
cd burn cd burn
# Use the --release flag to really speed up training. # Use the --release flag to really speed up training.
export TORCH_CUDA_VERSION=cu113 export TORCH_CUDA_VERSION=cu121
cargo run --example text-generation --release cargo run --example text-generation --release
``` ```