mirror of https://github.com/tracel-ai/burn.git
32 lines
1.7 KiB
Markdown
32 lines
1.7 KiB
Markdown
# Regression
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The example shows you how to:
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- Define a custom dataset for regression problems. We implement the [Diabetes Toy Dataset](https://huggingface.co/datasets/Jayabalambika/toy-diabetes)
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from HuggingFace hub. The dataset is also available as part of toy regression datasets in sklearn[datasets](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html).
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- Create a data pipeline from a raw dataset to a batched fast DataLoader with min-max feature scaling.
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- Define a Simple NN model for regression using Burn Modules.
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> **Note**
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> This example makes use of the HuggingFace [`datasets`](https://huggingface.co/docs/datasets/index)
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> library to download the datasets. Make sure you have [Python](https://www.python.org/downloads/)
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> installed on your computer.
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The example can be run like so:
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```bash
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git clone https://github.com/tracel-ai/burn.git
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cd burn
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# Use the --release flag to really speed up training.
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echo "Using ndarray backend"
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cargo run --example regression --release --features ndarray # CPU NdArray Backend - f32 - single thread
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cargo run --example regression --release --features ndarray-blas-openblas # CPU NdArray Backend - f32 - blas with openblas
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cargo run --example regression --release --features ndarray-blas-netlib # CPU NdArray Backend - f32 - blas with netlib
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echo "Using tch backend"
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export TORCH_CUDA_VERSION=cu121 # Set the cuda version
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cargo run --example regression --release --features tch-gpu # GPU Tch Backend - f32
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cargo run --example regression --release --features tch-cpu # CPU Tch Backend - f32
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echo "Using wgpu backend"
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cargo run --example regression --release --features wgpu
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```
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