ort

name	ort
description	ONNX Runtime in Rust via the `ort` crate (2.x): loading sessions, configuring CPU/CoreML/CUDA execution providers, tensor I/O with ndarray, async-safe spawn_blocking wrapping, global thread-pool init, and debugging provider/opset issues
user-invocable	false
disable-model-invocation	true
version	1.0.0
category	toolchain
author	Claude MPM Team
license	MIT
progressive_disclosure	{"entry_point":{"summary":"Load ONNX models in Rust with the ort 2.x crate: commit the global thread pool once, build a Session with providers in priority order (CUDA > CoreML > CPU), run inference inside spawn_blocking, and mark integration tests #[ignore]","when_to_use":"When adding or using ONNX models in Rust — embedders, VAD/STT/TTS, classifiers; when targeting CoreML on Apple Silicon or CUDA on Linux/Windows; when debugging silent execution-provider fallback or opset compatibility","quick_start":"1. Add ort dep + features 2. Commit global thread pool once at startup 3. Build Session with providers in priority order 4. Wrap run() in spawn_blocking 5. Mark ONNX tests #[ignore]"},"token_estimate":{"entry":160,"full":4200}}
context_limit	800
tags	["rust","ort","onnx","ml","inference","embeddings","coreml","cuda","ndarray"]
requires_tools	[]

ONNX Runtime in Rust (ort 2.x)

Overview

ort is the Rust binding to ONNX Runtime. Use it when you need to run ONNX-format models locally — text embedders, speech models, classifiers — with hardware-accelerated execution providers (CoreML on Apple Silicon, CUDA on Linux/Windows). The ort 2.x API changed significantly from 1.x; all patterns here target ort = "=2.0.0-rc.12" (the version pinned by fastembed-rs).

Reference implementation: crates/trusty-common/src/embedder/fast_embedder.rs

Quick Start

1. Cargo.toml setup

Library crate (default-features = false avoids linking the bundled ORT runtime unnecessarily; consumers opt into ONNX via a feature):

# Why: pin to the exact rc version fastembed-rs requires to avoid ABI mismatch.
# What: base features give std + the 24-API-level surface; coreml is macOS-only.
[dependencies.ort]
version = "=2.0.0-rc.12"
default-features = false
features = ["std", "api-24"]
optional = true

# macOS: add CoreML EP (builds to .mlmodelc, runs on ANE/GPU).
[target.'cfg(target_os = "macos")'.dependencies.ort]
version = "=2.0.0-rc.12"
default-features = false
features = ["coreml", "std", "api-24"]
optional = true

# ndarray interop (zero-copy tensor views).
ndarray = { version = "0.16", optional = true }

Binary / daemon crate (load-dynamic avoids statically linking libonnxruntime into the binary, reducing binary size when the shared lib is already present):

[dependencies.ort]
version = "=2.0.0-rc.12"
default-features = false
features = ["load-dynamic", "std", "api-24"]

# For CUDA GPU inference on Linux:
# features = ["load-dynamic", "cuda", "std", "api-24"]

ndarray tensor interop (add alongside ort):

# Why: ort's TensorRef::from_array_view needs ndarray's ArrayView.
# What: zero-copy conversion between ndarray arrays and ort Tensors.
ndarray = { version = "0.16", features = ["std"] }
# or in Cargo.toml features = ["std", "ndarray"] on the ort dep directly

Core Concepts

2. Global ORT thread pool — commit ONCE before any Session

ORT's per-session thread pool cannot be overridden from outside the session builder. The only lever that reaches an opaque session (e.g., one built internally by fastembed-rs) is the global environment thread pool. Commit it exactly once, before any Session is created.

Why this matters: The deferred-embed deadlock (trusty-tools issue #1668 / originally #1542) was traced to fastembed's internal session hardcoding with_intra_threads(available_parallelism()). On a multi-core host, 8+ ORT threads spawn; two busy-wait at ~70% CPU while the rest block forever inside condition_variable::wait, yielding zero embeddings and an empty HNSW index. Committing a global pool with intra_threads = 1 and spin_control = false causes ORT to call DisablePerSessionThreads, overriding fastembed's value.

use std::sync::OnceLock;

/// Why: record the committed threading opts so we log them once and can
/// surface them via /health or diagnostics.
/// What: stores the resolved OrtThreadingOptions committed to the global pool.
/// Test: resolver unit tests in mod.rs; actual commit is #[ignore]-tested.
static ORT_RUNTIME: OnceLock<OrtThreadingOptions> = OnceLock::new();

/// Why: commit the ORT global thread pool BEFORE any Session is created.
///
/// What: reads TRUSTY_ORT_INTRA_THREADS / TRUSTY_ORT_INTER_THREADS /
/// TRUSTY_ORT_ALLOW_SPINNING (defaults: 1 / 1 / false), builds
/// GlobalThreadPoolOptions, calls ort::init().with_global_thread_pool().commit().
/// The commit call returns `true` on success, `false` if an environment already
/// exists (meaning another Session was created first — the deadlock fix did NOT
/// take effect).
///
/// Test: #[ignore] ONNX integration tests verify round-trip inference after init.
fn init_ort_runtime() {
    ORT_RUNTIME.get_or_init(|| {
        let intra = std::env::var("TRUSTY_ORT_INTRA_THREADS")
            .ok()
            .and_then(|v| v.parse::<i16>().ok())
            .unwrap_or(1);
        let inter = std::env::var("TRUSTY_ORT_INTER_THREADS")
            .ok()
            .and_then(|v| v.parse::<i16>().ok())
            .unwrap_or(1);
        let spinning = std::env::var("TRUSTY_ORT_ALLOW_SPINNING")
            .map(|v| v == "1" || v.eq_ignore_ascii_case("true"))
            .unwrap_or(false);

        let pool = ort::environment::GlobalThreadPoolOptions::default()
            .with_intra_threads(intra)
            .and_then(|p| p.with_inter_threads(inter))
            .and_then(|p| p.with_spin_control(spinning));

        match pool {
            Ok(pool) => {
                let committed = ort::init().with_global_thread_pool(pool).commit();
                if committed {
                    tracing::info!(
                        intra_threads = intra,
                        inter_threads = inter,
                        allow_spinning = spinning,
                        "ORT global thread pool committed (DisablePerSessionThreads active)"
                    );
                } else {
                    tracing::warn!(
                        "ORT environment already committed before init_ort_runtime() — \
                         deadlock fix did NOT take effect; ensure no Session is created \
                         before calling init_ort_runtime()"
                    );
                }
            }
            Err(e) => {
                tracing::error!(
                    error = %e,
                    "Failed to build ORT global thread pool options; falling back to defaults"
                );
            }
        }

        OrtThreadingOptions { intra_threads: intra, inter_threads: inter, allow_spinning: spinning }
    });
}

3. Build a Session with providers in priority order

Providers are tried in registration order; the first available one wins. Always register more-capable (faster) providers first with CPU as the unconditional fallback.

use ort::{GraphOptimizationLevel, Session};

/// Why: build an ORT session with accelerated EP if available, CPU fallback.
/// What: CUDA (if feature enabled) > CoreML (macOS) > CPU (always).
///   Disables the BFC arena on CPU to prevent 19–53 GB virtual address pre-alloc.
/// Test: mock path; actual model load is #[ignore]-tested.
fn build_session(model_bytes: &[u8]) -> anyhow::Result<Session> {
    // CPU provider — always last, arena disabled to avoid massive VA reservation.
    // Why: ort's default CPU BFCArena pre-allocates 19–53 GB of virtual address
    // space on first inference; disable it to keep RSS predictable.
    let cpu = ort::execution_providers::CPU::default()
        .with_arena_allocator(false)
        .build();

    let mut providers: Vec<ort::execution_providers::ExecutionProviderDispatch> = Vec::new();

    // CUDA (Linux/Windows GPU inference).
    #[cfg(feature = "embedder-cuda")]
    {
        use ort::ep::ArenaExtendStrategy;
        providers.push(
            ort::ep::CUDA::default()
                .with_arena_extend_strategy(ArenaExtendStrategy::SameAsRequested)
                .with_memory_limit(12 * 1024 * 1024 * 1024) // 12 GiB cap
                .build(),
        );
    }

    // CoreML (Apple Silicon — ANE/GPU).
    #[cfg(all(target_os = "macos", feature = "coreml"))]
    {
        use ort::ep::CoreML;
        providers.push(
            CoreML::default()
                .with_static_input_shapes(true)
                .with_specialization_strategy(
                    ort::ep::coreml::CoreMLSpecializationStrategy::FastPrediction,
                )
                .build(),
        );
    }

    providers.push(cpu);

    // commit_from_memory_directly: zero-copy if bytes outlive the session.
    // Use commit_from_file for a path, commit_from_memory for a Vec<u8>.
    let session = Session::builder()?
        .with_optimization_level(GraphOptimizationLevel::All)?
        .with_execution_providers(providers)?
        .commit_from_memory_directly(model_bytes)?;

    Ok(session)
}

Tip: commit_from_file, commit_from_memory, and commit_from_memory_directly are the terminal builder calls. There is no standalone .build().

4. Tensor I/O with ndarray

use ndarray::{Array2, ArrayView2};
use ort::{inputs, tensor::{Tensor, TensorRef}};

/// Why: wrap ndarray arrays into ORT tensors without copying data where possible.
/// What: TensorRef::from_array_view is zero-copy; Tensor::from_array owns data.
/// Test: #[ignore] integration test with real model.
fn run_inference(
    session: &mut ort::Session,
    input: ArrayView2<'_, f32>,
) -> anyhow::Result<Array2<f32>> {
    // Zero-copy view — input array must outlive the TensorRef.
    let tensor = TensorRef::from_array_view(input)?;

    // Named input; name must match the ONNX graph's input node name.
    let outputs = session.run(inputs!["input_ids" => &tensor])?;

    // Extract as owned ndarray.
    let embedding: Array2<f32> = outputs[0]
        .try_extract_array::<f32>()?
        .into_dimensionality::<ndarray::Ix2>()?
        .to_owned();

    Ok(embedding)
}

// Dynamic batch size: disable memory pattern to allow variable batch shapes.
fn build_dynamic_session(model_bytes: &[u8]) -> anyhow::Result<ort::Session> {
    Ok(Session::builder()?
        .with_memory_pattern(false)?     // allow dynamic batch dimension
        .with_optimization_level(GraphOptimizationLevel::All)?
        .commit_from_memory_directly(model_bytes)?)
}

5. Async wrapping — never run inference on a tokio thread

Session is Send + Sync but run() takes &mut self. Wrap behind a parking_lot::Mutex and call from spawn_blocking.

use parking_lot::Mutex;
use std::sync::Arc;
use tokio::task;

/// Why: ORT inference is CPU-bound and blocks; running it on a tokio async
///   thread starves the executor and can deadlock the blocking thread pool.
/// What: Arc<Mutex<Session>> allows sharing across tasks; spawn_blocking
///   moves the lock-and-run onto a dedicated blocking thread.
/// Test: unit tests use a MockEmbedder; #[ignore] tests hit a real model.
pub struct OrtInferencer {
    session: Arc<Mutex<ort::Session>>,
}

impl OrtInferencer {
    /// Initialise EAGERLY at startup — do NOT defer behind Mutex<Option<Session>>.
    ///
    /// Why: the #1668 deferred-init deadlock: if multiple tokio tasks race to
    ///   initialise the session concurrently (all blocked on the same
    ///   Mutex<Option<Session>>), each calls spawn_blocking, exhausting the
    ///   blocking thread pool. Eager init at startup means the Session exists
    ///   before any request arrives.
    pub async fn new(model_bytes: Vec<u8>) -> anyhow::Result<Self> {
        let session = task::spawn_blocking(move || -> anyhow::Result<ort::Session> {
            // Commit global thread pool FIRST (idempotent via OnceLock).
            init_ort_runtime();
            build_session(&model_bytes)
        })
        .await
        .context("spawn_blocking error during Session init")??;

        Ok(Self {
            session: Arc::new(Mutex::new(session)),
        })
    }

    pub async fn embed(&self, input: Vec<f32>, shape: [usize; 2]) -> anyhow::Result<Vec<f32>> {
        let session = Arc::clone(&self.session);
        task::spawn_blocking(move || -> anyhow::Result<Vec<f32>> {
            use ndarray::Array2;
            let arr = Array2::from_shape_vec(shape, input)?;
            let mut guard = session.lock();
            let result = run_inference(&mut guard, arr.view())?;
            Ok(result.into_raw_vec())
        })
        .await
        .context("spawn_blocking error during inference")?
    }
}

Anti-pattern: Mutex<Option<Session>> with lazy init — concurrent callers all call spawn_blocking to race at init, exhaust the pool, and deadlock. Initialise Arc<Session> eagerly at startup instead.

Testing

Mark ONNX integration tests #[ignore] so CI stays fast while still allowing local validation with a real model:

/// Why: downloading a real ONNX model (~22 MB) in CI adds 30-60s and
///   requires network access; mark #[ignore] to exclude from default runs.
/// What: verifies the full init → inference → dimension check pipeline.
/// Test: run with `cargo test -p my-crate --features embedder -- --include-ignored`
#[tokio::test]
#[ignore = "requires ONNX model download (~22 MB); run with --include-ignored"]
async fn embedder_returns_correct_dim() {
    let model_bytes = std::fs::read("tests/fixtures/model.onnx").unwrap();
    let inferencer = OrtInferencer::new(model_bytes).await.unwrap();
    let output = inferencer.embed(vec![0.0_f32; 128], [1, 128]).await.unwrap();
    assert_eq!(output.len(), 384, "embedding dim should be 384");
}

/// Why: non-ONNX unit tests must always run (no model download needed).
/// What: exercises the mock path without ORT.
/// Test: always runs in CI.
#[tokio::test]
async fn mock_embedder_returns_correct_dim() {
    // Use a MockEmbedder that returns fixed-size zero vectors.
    let mock = MockEmbedder::new(384);
    let result = mock.embed_batch(&["hello world".to_string()]).await.unwrap();
    assert_eq!(result[0].len(), 384);
}

Run commands:

# Fast: no model download; runs in CI.
cargo test -p my-crate --features embedder

# Full: downloads ONNX model and runs integration tests.
cargo test -p my-crate --features embedder -- --include-ignored

# Single named test.
cargo test -p my-crate --features embedder -- embedder_returns_correct_dim --include-ignored --nocapture

Gotchas

Silent execution-provider fallback

ORT silently falls back to CPU when a registered EP is unavailable. Always log the resolved EP or call ep.is_available() before building the Session.

// Detect fallback: log SILENT FALLBACK with the expected vs actual provider.
if expected_provider != ExecutionProvider::Cpu {
    tracing::error!(
        "SILENT FALLBACK: {} EP failed — running on CPU. \
         Inference will be slower than expected.",
        expected_provider
    );
}

CoreML silent FP16 cast

CoreML may silently cast FP32 weights to FP16, reducing precision. Export your model in MLProgram format (mlpackage) rather than NeuralNetwork (mlmodel) to avoid the cast.

CPU arena pre-allocation

ORT's default CPU BFCArena reserves 19–53 GB of virtual address space on first inference (issue #89). Always pass .with_arena_allocator(false) on the CPU provider.

ort::init() must precede all Sessions

Call init_ort_runtime() (which calls ort::init().commit()) before any Session::builder()...commit_from_*() call. Once any session exists, the global environment is locked and the thread-pool commit returns false.

run() needs &mut self — must use Mutex or pool

ORT's Session::run() takes &mut self. Wrap in Mutex<Session> for concurrent access, or use a pool of sessions. Never expose &Session directly from an Arc.

CoreML cold-start compile to .mlmodelc

On first run, CoreML compiles the model to a .mlmodelc bundle. Set a persistent model_cache_dir on the CoreML EP so recompilation is skipped on restarts. Without a cache dir, every process restart pays the compile cost.

Export opset

ONNX opset ≤ 16 is the safest target for broad ORT compatibility. ORT may refuse or silently degrade models exported at opset 17+ with older runtime versions.

Windows DLL conflicts with load-dynamic

When using the load-dynamic feature on Windows, ensure only one copy of onnxruntime.dll is on PATH. Conflicting copies from different packages cause runtime crashes at session creation.

Forgetting the "std" feature

Without features = ["std"], commit_from_file and related APIs are not available. default-features = false is correct for library crates but always add "std" back explicitly.

Decision Trees

Which session commit call?

Situation	API
Model bytes already in memory, bytes outlive session	commit_from_memory_directly(&bytes)
Model bytes in a Vec<u8>, want ORT to own a copy	commit_from_memory(bytes.as_slice())
Model is a file on disk	commit_from_file("/path/to/model.onnx")

Which execution provider?

Hardware	Recommended EP	Notes
Apple Silicon (M1–M4)	CoreML	Add features = ["coreml"]; cold-compile on first run
NVIDIA GPU (Linux)	CUDA	Add features = ["cuda"]; cap memory with with_memory_limit
CPU-only / CI	CPU	Always last; disable arena with with_arena_allocator(false)

Anti-Patterns

• Calling ort::init() after creating any Session — the commit returns false and the thread pool is not set.
• Using unwrap() inside spawn_blocking closures — panics in a blocking thread are not caught by the async runtime; use ? and propagate via anyhow::Result.
• Deferring Session init behind Mutex<Option<Session>> — concurrent callers exhaust the blocking thread pool (the #1668 deadlock).
• Registering CUDA or CoreML but not logging the fallback when they are unavailable — silent performance regressions.
• Dynamic batch inference without .with_memory_pattern(false) — ORT pre-allocates for the first batch shape and panics on a different shape.

Resources

• Reference implementation: crates/trusty-common/src/embedder/fast_embedder.rs
• #[ignore] ONNX test pattern: crates/trusty-embedderd/tests/bit_identical.rs
• ort crate docs: https://docs.rs/ort
• ort guide: https://ort.pyke.io
• ONNX Runtime graph optimizations: https://onnxruntime.ai/docs/performance/model-optimizations/graph-optimizations.html
• ONNX opset compatibility matrix: https://onnxruntime.ai/docs/reference/compatibility.html