History

Guillaume Lagrange cdd1fa1672 Refactor tensor data (#1916 ) * Move distribution to module * Add new TensorData with serialization support * Implement display and from for TensorData * Add missing Cargo.lock * Add missing bytemuck feature * Add zeros, ones, full and random TensorData methods * Refactor Data -> TensorData usage * Fix tests Since TensorData is not generic over the element type anymore no type inference can be done by the compiler. We must explicitly cast the expected results to the expected backend type. * Remove commented line * Fix import * Add record-backward-compat * Remove dim const generic from TensorData * Support NestedValue de/serialization with TensorData * Fix burn-jit tests * Remove eprinln * Refactor onnx import to use TensorData * Fix tch from_data * Fix nested value serialization for u8 * Fix missing import * Fix reduce min onnx test * Fix deprecated attribute * Remove shape getter * Remove strict assert in tests * Add tensor data as_bytes * Add tensor check for rank mismatch * Fix typo (dimensions plural) * Fix error message * Update book examples with from_data and fix Display impl for TensorData * Add deprecation note		2024-06-26 20:22:19 -04:00
..
examples	docs(book-&-examples): modify book and examples with new `prelude` module (#1372 )	2024-02-28 13:25:25 -05:00
src	Refactor tensor data (#1916 )	2024-06-26 20:22:19 -04:00
Cargo.toml	[refactor] Move burn crates to their own crates directory (#1336 )	2024-02-20 13:57:55 -05:00
README.md	Update TORCH_CUDA_VERSION usage (#1284 )	2024-02-10 12:01:45 -05:00

README.md

MNIST

The example is showing you how to:

Define your own custom module (MLP).
Create the data pipeline from a raw dataset to a batched multi-threaded fast DataLoader.
Configure a learner to display and log metrics as well as to keep training checkpoints.

The example can be run like so:

git clone https://github.com/tracel-ai/burn.git
cd burn
# Use the --release flag to really speed up training.
echo "Using ndarray backend"
cargo run --example mnist --release --features ndarray                # CPU NdArray Backend - f32 - single thread
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
echo "Using tch backend"
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-cpu                # CPU Tch Backend - f32
echo "Using wgpu backend"
cargo run --example mnist --release --features wgpu