a61f068c64
* Add an initial wasi-nn implementation for Wasmtime This change adds a crate, `wasmtime-wasi-nn`, that uses `wiggle` to expose the current state of the wasi-nn API and `openvino` to implement the exposed functions. It includes an end-to-end test demonstrating how to do classification using wasi-nn: - `crates/wasi-nn/tests/classification-example` contains Rust code that is compiled to the `wasm32-wasi` target and run with a Wasmtime embedding that exposes the wasi-nn calls - the example uses Rust bindings for wasi-nn contained in `crates/wasi-nn/tests/wasi-nn-rust-bindings`; this crate contains code generated by `witx-bindgen` and eventually should be its own standalone crate * Test wasi-nn as a CI step This change adds: - a GitHub action for installing OpenVINO - a script, `ci/run-wasi-nn-example.sh`, to run the classification example
35 lines
1.9 KiB
Bash
Executable File
35 lines
1.9 KiB
Bash
Executable File
#!/bin/bash
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# The following script demonstrates how to execute a machine learning inference using the wasi-nn module optionally
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# compiled into Wasmtime. Calling it will download the necessary model and tensor files stored separately in $FIXTURE
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# into $TMP_DIR (optionally pass a directory with existing files as the first argument to re-try the script). Then,
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# it will compile the example code in crates/wasi-nn/tests/example into a Wasm file that is subsequently
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# executed with the Wasmtime CLI.
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set -e
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WASMTIME_DIR=$(dirname "$0" | xargs dirname)
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FIXTURE=https://gist.github.com/abrown/c7847bf3701f9efbb2070da1878542c1/raw/07a9f163994b0ff8f0d7c5a5c9645ec3d8b24024
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# Inform the environment of OpenVINO library locations. Then we use OPENVINO_INSTALL_DIR below to avoid building all of
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# OpenVINO from source (quite slow).
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source /opt/intel/openvino/bin/setupvars.sh
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# Build Wasmtime with wasi-nn enabled; we attempt this first to avoid extra work if the build fails.
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OPENVINO_INSTALL_DIR=/opt/intel/openvino cargo build -p wasmtime-cli --features wasi-nn
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# Download all necessary test fixtures to the temporary directory.
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TMP_DIR=${1:-$(mktemp -d -t ci-XXXXXXXXXX)}
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wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/frozen_inference_graph.bin
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wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/frozen_inference_graph.xml
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wget --no-clobber --directory-prefix=$TMP_DIR $FIXTURE/tensor-1x3x300x300-f32.bgr
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# Now build an example that uses the wasi-nn API.
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pushd $WASMTIME_DIR/crates/wasi-nn/examples/classification-example
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cargo build --release --target=wasm32-wasi
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cp target/wasm32-wasi/release/wasi-nn-example.wasm $TMP_DIR
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popd
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# Run the example in Wasmtime (note that the example uses `fixture` as the expected location of the model/tensor files).
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OPENVINO_INSTALL_DIR=/opt/intel/openvino cargo run --features wasi-nn -- run --mapdir fixture::$TMP_DIR $TMP_DIR/wasi-nn-example.wasm
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# Clean up.
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rm -rf $TMP_DIR |