diff --git a/backends/tract/tests/session.rs b/backends/tract/tests/session.rs
index e6f696f0..5320482a 100644
--- a/backends/tract/tests/session.rs
+++ b/backends/tract/tests/session.rs
@@ -14,7 +14,7 @@ fn mnist_5() -> ort::Result<()> {
 
 	ort::set_api(ort_tract::api());
 
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level3)?
 		.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")
 		.expect("Could not download model from file");
diff --git a/docs/pages/index.mdx b/docs/pages/index.mdx
index 898d1c73..37f7677e 100644
--- a/docs/pages/index.mdx
+++ b/docs/pages/index.mdx
@@ -53,7 +53,7 @@ Once you've got a model, load it via `ort` by creating a [`Session`](/fundamenta
 ```rust
 use ort::session::{builder::GraphOptimizationLevel, Session};
 
-let model = Session::builder()?
+let mut model = Session::builder()?
     .with_optimization_level(GraphOptimizationLevel::Level3)?
     .with_intra_threads(4)?
     .commit_from_file("yolov8m.onnx")?;
diff --git a/docs/pages/perf/io-binding.mdx b/docs/pages/perf/io-binding.mdx
index 0de5df27..2489fad0 100644
--- a/docs/pages/perf/io-binding.mdx
+++ b/docs/pages/perf/io-binding.mdx
@@ -62,9 +62,9 @@ Outputs can be bound to any device -- they can even stay on the EP device if you
 If you do bind an output to the session's device, it is not guaranteed to be synchronized after `run`, just like `bind_input`. You can force outputs to synchronize immediately using `IoBinding::synchronize_outputs`.
 
 ## Running
-To run a session using an I/O binding, you simply call `run()` on `IoBinding` instead of on the session:
+To run a session using an I/O binding, you simply call the session's `run_binding()` function with the created `IoBinding`:
 ```rs
-let outputs = io_binding.run()?;
+let outputs = session.run_binding(&binding)?;
 ```
 
 `outputs` provides the same interface as the outputs returned by `Session::run`, it just returns the outputs that you bound earlier.
@@ -77,10 +77,10 @@ let action: Tensor<f32> = outputs.remove("action").unwrap().downcast()?;
 Here is a more complete example of the I/O binding API in a scenario where I/O performance can be improved significantly. This example features a typical text-to-image diffusion pipeline, using a text encoder like CLIP to create the condition tensor and a UNet for diffusion.
 
 ```rs
-let text_encoder = Session::builder()?
+let mut text_encoder = Session::builder()?
 	.with_execution_providers([CUDAExecutionProvider::default().build()])?
 	.commit_from_file("text_encoder.onnx")?;
-let unet = Session::builder()?
+let mut unet = Session::builder()?
 	.with_execution_providers([CUDAExecutionProvider::default().build()])?
 	.commit_from_file("unet.onnx")?;
 
@@ -91,7 +91,7 @@ let text_condition = {
         vec![49, 272, 503, 286, 1396, 353, 9653, 284, 1234, 287, 616, 2438, 11, 7926, 13, 3423, 338, 3362, 25, 12520, 238, 242]
     ))?)?;
     binding.bind_output_to_device("output0", &text_encoder.allocator().memory_info())?;
-    binding.run()?.remove("output0").unwrap()
+    text_encoder.run_binding(&binding)?.remove("output0").unwrap()
 };
 
 let input_allocator = Allocator::new(
@@ -111,7 +111,7 @@ io_binding.bind_output("noise_pred", Tensor::<f32>::new(&output_allocator, [1, 4
 
 for _ in 0..20 {
 	io_binding.bind_input("latents", &latents)?;
-	let noise_pred = io_binding.run()?.remove("noise_pred").unwrap();
+	let noise_pred = unet.run_binding(&io_binding)?.remove("noise_pred").unwrap();
 
 	let mut latents = latents.extract_tensor_mut();
 	latents += &noise_pred.try_extract_tensor::<f32>()?;
diff --git a/examples/async-gpt2-api/examples/async-gpt2-api.rs b/examples/async-gpt2-api/examples/async-gpt2-api.rs
index 8a783c0e..cdb2a0ca 100644
--- a/examples/async-gpt2-api/examples/async-gpt2-api.rs
+++ b/examples/async-gpt2-api/examples/async-gpt2-api.rs
@@ -17,7 +17,7 @@ use ort::{
 };
 use rand::Rng;
 use tokenizers::Tokenizer;
-use tokio::net::TcpListener;
+use tokio::{net::TcpListener, sync::Mutex};
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};
 
 #[tokio::main]
@@ -44,7 +44,7 @@ async fn main() -> anyhow::Result<()> {
 	let tokenizer = Tokenizer::from_file(Path::new(env!("CARGO_MANIFEST_DIR")).join("data").join("tokenizer.json")).unwrap();
 
 	let app_state = AppState {
-		session: Arc::new(session),
+		session: Arc::new(Mutex::new(session)),
 		tokenizer: Arc::new(tokenizer)
 	};
 
@@ -59,15 +59,21 @@ async fn main() -> anyhow::Result<()> {
 
 #[derive(Clone)]
 struct AppState {
-	session: Arc<Session>,
+	session: Arc<Mutex<Session>>,
 	tokenizer: Arc<Tokenizer>
 }
 
-fn generate_stream(tokenizer: Arc<Tokenizer>, session: Arc<Session>, mut tokens: Vec<i64>, gen_tokens: usize) -> impl Stream<Item = ort::Result<Event>> + Send {
+fn generate_stream(
+	tokenizer: Arc<Tokenizer>,
+	session: Arc<Mutex<Session>>,
+	mut tokens: Vec<i64>,
+	gen_tokens: usize
+) -> impl Stream<Item = ort::Result<Event>> + Send {
 	async_stream_lite::try_async_stream(|yielder| async move {
 		for _ in 0..gen_tokens {
+			let input = TensorRef::from_array_view((vec![1, 1, tokens.len() as i64], tokens.as_slice()))?;
 			let probabilities = {
-				let input = TensorRef::from_array_view((vec![1, 1, tokens.len() as i64], tokens.as_slice()))?;
+				let mut session = session.lock().await;
 				let outputs = session.run_async(ort::inputs![input])?.await?;
 				let (dim, probabilities) = outputs["output1"].try_extract_raw_tensor()?;
 
@@ -93,7 +99,7 @@ fn generate_stream(tokenizer: Arc<Tokenizer>, session: Arc<Session>, mut tokens:
 	})
 }
 
-impl FromRef<AppState> for Arc<Session> {
+impl FromRef<AppState> for Arc<Mutex<Session>> {
 	fn from_ref(input: &AppState) -> Self {
 		Arc::clone(&input.session)
 	}
@@ -104,6 +110,6 @@ impl FromRef<AppState> for Arc<Tokenizer> {
 	}
 }
 
-async fn generate(State(session): State<Arc<Session>>, State(tokenizer): State<Arc<Tokenizer>>) -> Sse<impl Stream<Item = ort::Result<Event>>> {
+async fn generate(State(session): State<Arc<Mutex<Session>>>, State(tokenizer): State<Arc<Tokenizer>>) -> Sse<impl Stream<Item = ort::Result<Event>>> {
 	Sse::new(generate_stream(tokenizer, session, vec![0], 50)).keep_alive(KeepAlive::new())
 }
diff --git a/examples/cudarc/src/main.rs b/examples/cudarc/src/main.rs
index 14c333c4..afc76964 100644
--- a/examples/cudarc/src/main.rs
+++ b/examples/cudarc/src/main.rs
@@ -19,7 +19,7 @@ fn main() -> anyhow::Result<()> {
 		.with_execution_providers([CUDAExecutionProvider::default().build().error_on_failure()])
 		.commit()?;
 
-	let model =
+	let mut session =
 		Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/modnet_photographic_portrait_matting.onnx")?;
 
 	let original_img = image::open(Path::new(env!("CARGO_MANIFEST_DIR")).join("data").join("photo.jpg")).unwrap();
@@ -45,7 +45,7 @@ fn main() -> anyhow::Result<()> {
 		)
 		.unwrap()
 	};
-	let outputs = model.run(ort::inputs![tensor])?;
+	let outputs = session.run(ort::inputs![tensor])?;
 
 	let output = outputs["output"].try_extract_tensor::<f32>()?;
 
diff --git a/examples/custom-ops/examples/custom-ops.rs b/examples/custom-ops/examples/custom-ops.rs
index f92c0646..3a0539a4 100644
--- a/examples/custom-ops/examples/custom-ops.rs
+++ b/examples/custom-ops/examples/custom-ops.rs
@@ -74,7 +74,7 @@ impl Operator for CustomOpTwo {
 }
 
 fn main() -> ort::Result<()> {
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_operators(OperatorDomain::new("test.customop")?.add(CustomOpOne)?.add(CustomOpTwo)?)?
 		.commit_from_file("tests/data/custom_op_test.onnx")?;
 
diff --git a/examples/gpt2/examples/gpt2.rs b/examples/gpt2/examples/gpt2.rs
index bf679b2d..7366c4ca 100644
--- a/examples/gpt2/examples/gpt2.rs
+++ b/examples/gpt2/examples/gpt2.rs
@@ -35,7 +35,7 @@ fn main() -> ort::Result<()> {
 	let mut rng = rand::thread_rng();
 
 	// Load our model
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level1)?
 		.with_intra_threads(1)?
 		.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/gpt2.onnx")?;
diff --git a/examples/modnet/examples/modnet.rs b/examples/modnet/examples/modnet.rs
index 5ce39a93..264811b8 100644
--- a/examples/modnet/examples/modnet.rs
+++ b/examples/modnet/examples/modnet.rs
@@ -15,7 +15,7 @@ fn main() -> ort::Result<()> {
 		.with_execution_providers([CUDAExecutionProvider::default().build()])
 		.commit()?;
 
-	let model =
+	let mut session =
 		Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/modnet_photographic_portrait_matting.onnx")?;
 
 	let original_img = image::open(Path::new(env!("CARGO_MANIFEST_DIR")).join("data").join("photo.jpg")).unwrap();
@@ -31,7 +31,7 @@ fn main() -> ort::Result<()> {
 		input[[0, 2, y, x]] = (b as f32 - 127.5) / 127.5;
 	}
 
-	let outputs = model.run(inputs!["input" => TensorRef::from_array_view(input.view())?])?;
+	let outputs = session.run(inputs!["input" => TensorRef::from_array_view(input.view())?])?;
 
 	let output = outputs["output"].try_extract_tensor::<f32>()?;
 
diff --git a/examples/phi-3-vision/src/main.rs b/examples/phi-3-vision/src/main.rs
index ffd8cf31..d48eff9d 100644
--- a/examples/phi-3-vision/src/main.rs
+++ b/examples/phi-3-vision/src/main.rs
@@ -24,7 +24,7 @@ fn get_current_time() -> Instant {
 	Instant::now()
 }
 
-fn get_image_embedding(vision_model: &Session, img: &Option<DynamicImage>) -> Result<Array3<f32>> {
+fn get_image_embedding(vision_model: &mut Session, img: &Option<DynamicImage>) -> Result<Array3<f32>> {
 	let visual_features = if let Some(img) = img {
 		let image_processor = image_process::Phi3VImageProcessor::new();
 		let result = image_processor.preprocess(img)?;
@@ -46,7 +46,7 @@ fn get_image_embedding(vision_model: &Session, img: &Option<DynamicImage>) -> Re
 	Ok(visual_features)
 }
 
-fn get_text_embedding(text_embedding_model: &Session, input_ids: &Array2<i64>) -> Result<Array3<f32>> {
+fn get_text_embedding(text_embedding_model: &mut Session, input_ids: &Array2<i64>) -> Result<Array3<f32>> {
 	let outputs = text_embedding_model.run(ort::inputs![
 		"input_ids" => TensorRef::from_array_view(input_ids)?,
 	])?;
@@ -102,9 +102,9 @@ fn format_chat_template(img: &Option<DynamicImage>, txt: &str) -> String {
 
 pub async fn generate_text(
 	tokenizer: &Tokenizer,
-	vision_model: &Session,
-	text_embedding_model: &Session,
-	generation_model: &Session,
+	vision_model: &mut Session,
+	text_embedding_model: &mut Session,
+	generation_model: &mut Session,
 	image: &Option<DynamicImage>,
 	text: &str
 ) -> Result<()> {
@@ -213,19 +213,19 @@ async fn main() -> Result<()> {
 
 	let data_dir = Path::new(env!("CARGO_MANIFEST_DIR")).join("data");
 	let tokenizer = Tokenizer::from_file(data_dir.join("tokenizer.json")).map_err(|e| anyhow::anyhow!("Error loading tokenizer: {:?}", e))?;
-	let vision_model = Session::builder()?.commit_from_file(data_dir.join(VISION_MODEL_NAME))?;
-	let text_embedding_model = Session::builder()?.commit_from_file(data_dir.join(TEXT_EMBEDDING_MODEL_NAME))?;
-	let generation_model = Session::builder()?.commit_from_file(data_dir.join(GENERATION_MODEL_NAME))?;
+	let mut vision_model = Session::builder()?.commit_from_file(data_dir.join(VISION_MODEL_NAME))?;
+	let mut text_embedding_model = Session::builder()?.commit_from_file(data_dir.join(TEXT_EMBEDDING_MODEL_NAME))?;
+	let mut generation_model = Session::builder()?.commit_from_file(data_dir.join(GENERATION_MODEL_NAME))?;
 
 	// Generate text from text
 	let image: Option<DynamicImage> = None;
 	let text = "Who are you?".to_string();
-	generate_text(&tokenizer, &vision_model, &text_embedding_model, &generation_model, &image, &text).await?;
+	generate_text(&tokenizer, &mut vision_model, &mut text_embedding_model, &mut generation_model, &image, &text).await?;
 
 	// Generate text from image and text
 	let image: Option<DynamicImage> = Some(image::open(data_dir.join("example.jpg"))?);
 	let text = "What is shown in this image?".to_string();
-	generate_text(&tokenizer, &vision_model, &text_embedding_model, &generation_model, &image, &text).await?;
+	generate_text(&tokenizer, &mut vision_model, &mut text_embedding_model, &mut generation_model, &image, &text).await?;
 
 	Ok(())
 }
diff --git a/examples/sentence-transformers/examples/semantic-similarity.rs b/examples/sentence-transformers/examples/semantic-similarity.rs
index 6aed6e63..4f228f1f 100644
--- a/examples/sentence-transformers/examples/semantic-similarity.rs
+++ b/examples/sentence-transformers/examples/semantic-similarity.rs
@@ -25,7 +25,7 @@ fn main() -> ort::Result<()> {
 		.commit()?;
 
 	// Load our model
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level1)?
 		.with_intra_threads(1)?
 		.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/all-MiniLM-L6-v2.onnx")?;
diff --git a/examples/training/examples/train-clm-simple.rs b/examples/training/examples/train-clm-simple.rs
index 187cf827..b1bc3551 100644
--- a/examples/training/examples/train-clm-simple.rs
+++ b/examples/training/examples/train-clm-simple.rs
@@ -110,7 +110,7 @@ fn main() -> ort::Result<()> {
 
 	trainer.export("trained-clm.onnx", ["probs"])?;
 
-	let session = Session::builder()?.commit_from_file("trained-clm.onnx")?;
+	let mut session = Session::builder()?.commit_from_file("trained-clm.onnx")?;
 
 	let mut stdout = std::io::stdout();
 
diff --git a/examples/training/examples/train-clm.rs b/examples/training/examples/train-clm.rs
index 2c29c69d..3f94da03 100644
--- a/examples/training/examples/train-clm.rs
+++ b/examples/training/examples/train-clm.rs
@@ -102,7 +102,7 @@ fn main() -> ort::Result<()> {
 
 	trainer.export("trained-clm.onnx", ["probs"])?;
 
-	let session = Session::builder()?.commit_from_file("trained-clm.onnx")?;
+	let mut session = Session::builder()?.commit_from_file("trained-clm.onnx")?;
 
 	let mut stdout = std::io::stdout();
 
diff --git a/examples/yolov8/Cargo.toml b/examples/yolov8/Cargo.toml
index bfa1def2..5ca11aba 100644
--- a/examples/yolov8/Cargo.toml
+++ b/examples/yolov8/Cargo.toml
@@ -5,7 +5,7 @@ version = "0.0.0"
 edition = "2021"
 
 [dependencies]
-ort = { path = "../../" }
+ort = { path = "../../", features = [ "fetch-models" ] }
 ndarray = "0.16"
 tracing-subscriber = { version = "0.3", default-features = false, features = [ "env-filter", "fmt" ] }
 image = "0.25"
diff --git a/examples/yolov8/examples/yolov8.rs b/examples/yolov8/examples/yolov8.rs
index 7c24ddf5..f7625982 100644
--- a/examples/yolov8/examples/yolov8.rs
+++ b/examples/yolov8/examples/yolov8.rs
@@ -64,7 +64,7 @@ fn main() -> ort::Result<()> {
 		input[[0, 2, y, x]] = (b as f32) / 255.;
 	}
 
-	let model = Session::builder()?.commit_from_url(YOLOV8M_URL)?;
+	let mut model = Session::builder()?.commit_from_url(YOLOV8M_URL)?;
 
 	// Run YOLOv8 inference
 	let outputs: SessionOutputs = model.run(inputs!["images" => TensorRef::from_array_view(&input)?])?;
diff --git a/src/adapter.rs b/src/adapter.rs
index e5ff368c..db6fc421 100644
--- a/src/adapter.rs
+++ b/src/adapter.rs
@@ -36,7 +36,7 @@ impl Drop for AdapterInner {
 /// ```
 /// # use ort::{adapter::Adapter, session::{run_options::RunOptions, Session}, value::Tensor};
 /// # fn main() -> ort::Result<()> {
-/// let model = Session::builder()?.commit_from_file("tests/data/lora_model.onnx")?;
+/// let mut model = Session::builder()?.commit_from_file("tests/data/lora_model.onnx")?;
 /// let lora = Adapter::from_file("tests/data/adapter.orl", None)?;
 ///
 /// let mut run_options = RunOptions::new()?;
@@ -69,7 +69,7 @@ impl Adapter {
 	/// # 	value::Tensor
 	/// # };
 	/// # fn main() -> ort::Result<()> {
-	/// let model = Session::builder()?
+	/// let mut model = Session::builder()?
 	/// 	.with_execution_providers([CUDAExecutionProvider::default().build()])?
 	/// 	.commit_from_file("tests/data/lora_model.onnx")?;
 	///
@@ -116,7 +116,7 @@ impl Adapter {
 	/// # 	value::Tensor
 	/// # };
 	/// # fn main() -> ort::Result<()> {
-	/// let model = Session::builder()?
+	/// let mut model = Session::builder()?
 	/// 	.with_execution_providers([CUDAExecutionProvider::default().build()])?
 	/// 	.commit_from_file("tests/data/lora_model.onnx")?;
 	///
@@ -166,7 +166,7 @@ mod tests {
 	#[test]
 	fn test_lora() -> crate::Result<()> {
 		let model = std::fs::read("tests/data/lora_model.onnx").expect("");
-		let session = Session::builder()?.commit_from_memory(&model)?;
+		let mut session = Session::builder()?.commit_from_memory(&model)?;
 		let lora = std::fs::read("tests/data/adapter.orl").expect("");
 		let lora = Adapter::from_memory(&lora, None)?;
 
@@ -190,7 +190,7 @@ mod tests {
 	#[test]
 	fn test_lora_from_memory() -> crate::Result<()> {
 		let model = std::fs::read("tests/data/lora_model.onnx").expect("");
-		let session = Session::builder()?.commit_from_memory(&model)?;
+		let mut session = Session::builder()?.commit_from_memory(&model)?;
 
 		let lora_bytes = std::fs::read("tests/data/adapter.orl").expect("");
 		let lora = Adapter::from_memory(&lora_bytes, None)?;
diff --git a/src/io_binding.rs b/src/io_binding.rs
index 2fe2ca78..59989a0d 100644
--- a/src/io_binding.rs
+++ b/src/io_binding.rs
@@ -3,13 +3,11 @@
 use alloc::{
 	ffi::CString,
 	string::{String, ToString},
-	sync::Arc,
-	vec::Vec
+	sync::Arc
 };
 use core::{
 	fmt::Debug,
-	ptr::{self, NonNull},
-	slice
+	ptr::{self, NonNull}
 };
 
 use crate::{
@@ -17,7 +15,7 @@ use crate::{
 	error::Result,
 	memory::MemoryInfo,
 	ortsys,
-	session::{NoSelectedOutputs, RunOptions, Session, SharedSessionInner, output::SessionOutputs},
+	session::{Session, SharedSessionInner},
 	util::MiniMap,
 	value::{DynValue, Value, ValueInner, ValueTypeMarker}
 };
@@ -50,10 +48,10 @@ use crate::{
 /// # 	value::Tensor
 /// # };
 /// # fn main() -> ort::Result<()> {
-/// let text_encoder = Session::builder()?
+/// let mut text_encoder = Session::builder()?
 /// 	.with_execution_providers([CUDAExecutionProvider::default().build()])?
 /// 	.commit_from_file("text_encoder.onnx")?;
-/// let unet = Session::builder()?
+/// let mut unet = Session::builder()?
 /// 	.with_execution_providers([CUDAExecutionProvider::default().build()])?
 /// 	.commit_from_file("unet.onnx")?;
 ///
@@ -82,7 +80,7 @@ use crate::{
 ///
 /// for _ in 0..20 {
 /// 	io_binding.bind_input("latents", &latents)?;
-/// 	let noise_pred = io_binding.run()?.remove("noise_pred").unwrap();
+/// 	let noise_pred = unet.run_binding(&io_binding)?.remove("noise_pred").unwrap();
 ///
 /// 	let mut latents = latents.extract_tensor_mut();
 /// 	latents += &noise_pred.try_extract_tensor::<f32>()?;
@@ -99,8 +97,8 @@ use crate::{
 pub struct IoBinding {
 	ptr: NonNull<ort_sys::OrtIoBinding>,
 	held_inputs: MiniMap<String, Arc<ValueInner>>,
-	output_values: MiniMap<String, Option<DynValue>>,
-	session: Arc<SharedSessionInner>
+	pub(crate) output_values: MiniMap<String, Option<DynValue>>,
+	_session: Arc<SharedSessionInner>
 }
 
 impl IoBinding {
@@ -109,9 +107,9 @@ impl IoBinding {
 		ortsys![unsafe CreateIoBinding(session.ptr().cast_mut(), &mut ptr)?; nonNull(ptr)];
 		Ok(Self {
 			ptr: unsafe { NonNull::new_unchecked(ptr) },
-			session: session.inner(),
 			held_inputs: MiniMap::new(),
-			output_values: MiniMap::new()
+			output_values: MiniMap::new(),
+			_session: session.inner()
 		})
 	}
 
@@ -197,55 +195,6 @@ impl IoBinding {
 		self.synchronize_outputs()?;
 		Ok(())
 	}
-
-	/// Performs inference on the session using the bound inputs specified by [`IoBinding::bind_input`].
-	pub fn run(&mut self) -> Result<SessionOutputs<'_, '_>> {
-		self.run_inner(None)
-	}
-
-	/// Performs inference on the session using the bound inputs specified by [`IoBinding::bind_input`].
-	pub fn run_with_options(&mut self, run_options: &RunOptions<NoSelectedOutputs>) -> Result<SessionOutputs<'_, '_>> {
-		self.run_inner(Some(run_options))
-	}
-
-	fn run_inner(&mut self, run_options: Option<&RunOptions<NoSelectedOutputs>>) -> Result<SessionOutputs<'_, '_>> {
-		let run_options_ptr = if let Some(run_options) = run_options { run_options.ptr() } else { ptr::null() };
-		ortsys![unsafe RunWithBinding(self.session.ptr().cast_mut(), run_options_ptr, self.ptr())?];
-
-		// let owned_ptrs: HashMap<*mut ort_sys::OrtValue, &Value> = self.output_values.values().map(|c| (c.ptr().cast_mut(),
-		// c)).collect();
-		let mut count = self.output_values.len();
-		if count > 0 {
-			let mut output_values_ptr: *mut *mut ort_sys::OrtValue = ptr::null_mut();
-			ortsys![unsafe GetBoundOutputValues(self.ptr(), self.session.allocator.ptr().cast_mut(), &mut output_values_ptr, &mut count)?; nonNull(output_values_ptr)];
-
-			let output_values = unsafe { slice::from_raw_parts(output_values_ptr, count).to_vec() }
-				.into_iter()
-				.zip(self.output_values.iter())
-				.map(|(ptr, (_, value))| unsafe {
-					if let Some(value) = value {
-						DynValue::clone_of(value)
-					} else {
-						DynValue::from_ptr(
-							NonNull::new(ptr).expect("OrtValue ptrs returned by GetBoundOutputValues should not be null"),
-							Some(Arc::clone(&self.session))
-						)
-					}
-				})
-				.collect::<Vec<_>>();
-
-			// output values will be freed when the `Value`s in `SessionOutputs` drop
-
-			Ok(SessionOutputs::new_backed(
-				self.output_values.iter().map(|(k, _)| k.as_str()).collect(),
-				output_values,
-				&self.session.allocator,
-				output_values_ptr.cast()
-			))
-		} else {
-			Ok(SessionOutputs::new_empty())
-		}
-	}
 }
 
 unsafe impl Send for IoBinding {}
@@ -308,7 +257,7 @@ mod tests {
 	#[test]
 	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_mnist_input_bound() -> Result<()> {
-		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
+		let mut session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 
 		let array = get_image();
 
@@ -316,7 +265,7 @@ mod tests {
 		binding.bind_input(&session.inputs[0].name, &Tensor::from_array(array)?)?;
 		binding.bind_output_to_device(&session.outputs[0].name, &MemoryInfo::new(AllocationDevice::CPU, 0, AllocatorType::Device, MemoryType::CPUOutput)?)?;
 
-		let outputs = binding.run()?;
+		let outputs = session.run_binding(&binding)?;
 		let probabilities = extract_probabilities(&outputs[0])?;
 		assert_eq!(probabilities[0].0, 5);
 
@@ -326,7 +275,7 @@ mod tests {
 	#[test]
 	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_mnist_input_output_bound() -> Result<()> {
-		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
+		let mut session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 
 		let array = get_image();
 
@@ -336,7 +285,7 @@ mod tests {
 		let output = Array2::from_shape_simple_fn((1, 10), || 0.0_f32);
 		binding.bind_output(&session.outputs[0].name, Tensor::from_array(output)?)?;
 
-		let outputs = binding.run()?;
+		let outputs = session.run_binding(&binding)?;
 		let probabilities = extract_probabilities(&outputs[0])?;
 		assert_eq!(probabilities[0].0, 5);
 
@@ -346,7 +295,7 @@ mod tests {
 	#[test]
 	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_send_iobinding() -> Result<()> {
-		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
+		let mut session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 
 		let array = get_image();
 
@@ -356,7 +305,7 @@ mod tests {
 
 		let probabilities = std::thread::spawn(move || {
 			binding.bind_input(&session.inputs[0].name, &Tensor::from_array(array)?)?;
-			let outputs = binding.run()?;
+			let outputs = session.run_binding(&binding)?;
 			let probabilities = extract_probabilities(&outputs[0])?;
 			Ok::<Vec<(usize, f32)>, crate::Error>(probabilities)
 		})
@@ -371,7 +320,7 @@ mod tests {
 	#[test]
 	#[cfg(all(feature = "ndarray", feature = "fetch-models"))]
 	fn test_mnist_clear_bounds() -> Result<()> {
-		let session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
+		let mut session = Session::builder()?.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")?;
 
 		let array = get_image();
 
@@ -382,7 +331,7 @@ mod tests {
 		binding.bind_output(&session.outputs[0].name, Tensor::from_array(output)?)?;
 
 		{
-			let outputs = binding.run()?;
+			let outputs = session.run_binding(&binding)?;
 			let probabilities = extract_probabilities(&outputs[0])?;
 			assert_eq!(probabilities[0].0, 5);
 		}
@@ -391,13 +340,13 @@ mod tests {
 		binding.bind_output_to_device(&session.outputs[0].name, &MemoryInfo::new(AllocationDevice::CPU, 0, AllocatorType::Device, MemoryType::CPUOutput)?)?;
 
 		{
-			let outputs = binding.run()?;
+			let outputs = session.run_binding(&binding)?;
 			let probabilities = extract_probabilities(&outputs[0])?;
 			assert_eq!(probabilities[0].0, 5);
 		}
 
 		binding.clear_inputs();
-		assert!(binding.run().is_err());
+		assert!(session.run_binding(&binding).is_err());
 
 		Ok(())
 	}
diff --git a/src/operator/tests.rs b/src/operator/tests.rs
index e9b8d242..3ec78a75 100644
--- a/src/operator/tests.rs
+++ b/src/operator/tests.rs
@@ -78,7 +78,7 @@ impl Operator for CustomOpTwo {
 #[test]
 fn test_custom_ops() -> crate::Result<()> {
 	let model = std::fs::read("tests/data/custom_op_test.onnx").expect("");
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_operators(OperatorDomain::new("test.customop")?.add(CustomOpOne)?.add(CustomOpTwo)?)?
 		.commit_from_memory(&model)?;
 
diff --git a/src/session/mod.rs b/src/session/mod.rs
index 1fe2676a..c3da343a 100644
--- a/src/session/mod.rs
+++ b/src/session/mod.rs
@@ -3,7 +3,7 @@
 //! ```
 //! # use ort::{session::Session, value::TensorRef};
 //! # fn main() -> ort::Result<()> {
-//! let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+//! let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 //! let input = ndarray::Array4::<f32>::zeros((1, 64, 64, 3));
 //! let outputs = session.run(ort::inputs![TensorRef::from_array_view(&input)?])?;
 //! # 	Ok(())
@@ -16,8 +16,9 @@ use core::{
 	ffi::{CStr, c_char},
 	iter,
 	marker::PhantomData,
-	ops::Deref,
-	ptr::{self, NonNull}
+	ops::{Deref, DerefMut},
+	ptr::{self, NonNull},
+	slice
 };
 
 use crate::{
@@ -27,7 +28,7 @@ use crate::{
 	memory::Allocator,
 	metadata::ModelMetadata,
 	ortsys,
-	value::{Value, ValueType}
+	value::{DynValue, Value, ValueType}
 };
 
 #[cfg(feature = "std")]
@@ -83,7 +84,7 @@ impl Drop for SharedSessionInner {
 /// ```
 /// # use ort::{session::Session, value::TensorRef};
 /// # fn main() -> ort::Result<()> {
-/// let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+/// let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 /// let input = ndarray::Array4::<f32>::zeros((1, 64, 64, 3));
 /// let outputs = session.run(ort::inputs![TensorRef::from_array_view(&input)?])?;
 /// # 	Ok(())
@@ -113,6 +114,11 @@ impl Deref for InMemorySession<'_> {
 		&self.session
 	}
 }
+impl DerefMut for InMemorySession<'_> {
+	fn deref_mut(&mut self) -> &mut Self::Target {
+		&mut self.session
+	}
+}
 
 /// Information about a [`Session`] input.
 #[derive(Debug)]
@@ -188,13 +194,13 @@ impl Session {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{run_options::RunOptions, Session}, tensor::TensorElementType, value::{Value, ValueType, TensorRef}};
 	/// # fn main() -> ort::Result<()> {
-	/// let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+	/// let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 	/// let input = ndarray::Array4::<f32>::zeros((1, 64, 64, 3));
 	/// let outputs = session.run(ort::inputs![TensorRef::from_array_view(&input)?])?;
 	/// # 	Ok(())
 	/// # }
 	/// ```
-	pub fn run<'s, 'i, 'v: 'i, const N: usize>(&'s self, input_values: impl Into<SessionInputs<'i, 'v, N>>) -> Result<SessionOutputs<'s, 's>> {
+	pub fn run<'s, 'i, 'v: 'i, const N: usize>(&'s mut self, input_values: impl Into<SessionInputs<'i, 'v, N>>) -> Result<SessionOutputs<'s, 's>> {
 		match input_values.into() {
 			SessionInputs::ValueSlice(input_values) => {
 				self.run_inner::<NoSelectedOutputs>(&self.inputs.iter().map(|input| input.name.as_str()).collect::<Vec<_>>(), input_values.iter(), None)
@@ -218,7 +224,7 @@ impl Session {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{Session, run_options::RunOptions}, value::{Value, ValueType, TensorRef}, tensor::TensorElementType};
 	/// # fn main() -> ort::Result<()> {
-	/// # 	let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+	/// # 	let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 	/// # 	let input = Value::from_array(ndarray::Array4::<f32>::zeros((1, 64, 64, 3)))?;
 	/// let run_options = Arc::new(RunOptions::new()?);
 	///
@@ -237,7 +243,7 @@ impl Session {
 	/// # }
 	/// ```
 	pub fn run_with_options<'r, 's: 'r, 'i, 'v: 'i, O: SelectedOutputMarker, const N: usize>(
-		&'s self,
+		&'s mut self,
 		input_values: impl Into<SessionInputs<'i, 'v, N>>,
 		run_options: &'r RunOptions<O>
 	) -> Result<SessionOutputs<'r, 's>> {
@@ -333,6 +339,58 @@ impl Session {
 		Ok(SessionOutputs::new(output_names, outputs))
 	}
 
+	pub fn run_binding<'b, 's: 'b>(&'s mut self, binding: &'b IoBinding) -> Result<SessionOutputs<'b, 's>> {
+		self.run_binding_inner(binding, None)
+	}
+
+	pub fn run_binding_with_options<'r, 'b, 's: 'b>(
+		&'s mut self,
+		binding: &'b IoBinding,
+		run_options: &'r RunOptions<NoSelectedOutputs>
+	) -> Result<SessionOutputs<'b, 's>> {
+		self.run_binding_inner(binding, Some(run_options))
+	}
+
+	fn run_binding_inner<'r, 'b, 's: 'b>(
+		&'s self,
+		binding: &'b IoBinding,
+		run_options: Option<&'r RunOptions<NoSelectedOutputs>>
+	) -> Result<SessionOutputs<'b, 's>> {
+		let run_options_ptr = if let Some(run_options) = run_options { run_options.ptr() } else { ptr::null() };
+		ortsys![unsafe RunWithBinding(self.inner.ptr().cast_mut(), run_options_ptr, binding.ptr())?];
+
+		// let owned_ptrs: HashMap<*mut ort_sys::OrtValue, &Value> = self.output_values.values().map(|c| (c.ptr().cast_mut(),
+		// c)).collect();
+		let mut count = binding.output_values.len();
+		if count > 0 {
+			let mut output_values_ptr: *mut *mut ort_sys::OrtValue = ptr::null_mut();
+			ortsys![unsafe GetBoundOutputValues(binding.ptr(), self.inner.allocator.ptr().cast_mut(), &mut output_values_ptr, &mut count)?; nonNull(output_values_ptr)];
+
+			let output_values = unsafe { slice::from_raw_parts(output_values_ptr, count).to_vec() }
+				.into_iter()
+				.zip(binding.output_values.iter())
+				.map(|(ptr, (_, value))| unsafe {
+					if let Some(value) = value {
+						DynValue::clone_of(value)
+					} else {
+						DynValue::from_ptr(NonNull::new(ptr).expect("OrtValue ptrs returned by GetBoundOutputValues should not be null"), Some(self.inner()))
+					}
+				})
+				.collect::<Vec<_>>();
+
+			// output values will be freed when the `Value`s in `SessionOutputs` drop
+
+			Ok(SessionOutputs::new_backed(
+				binding.output_values.iter().map(|(k, _)| k.as_str()).collect(),
+				output_values,
+				self.allocator(),
+				output_values_ptr.cast()
+			))
+		} else {
+			Ok(SessionOutputs::new_empty())
+		}
+	}
+
 	/// Asynchronously run input data through the ONNX graph, performing inference.
 	///
 	/// Inference will be performed on a thread in the session's thread pool. **Thus, the session must have been
@@ -346,16 +404,16 @@ impl Session {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{Session, run_options::RunOptions}, value::{Value, ValueType, TensorRef}, tensor::TensorElementType};
 	/// # fn main() -> ort::Result<()> { tokio_test::block_on(async {
-	/// let session = Session::builder()?.with_intra_threads(2)?.commit_from_file("tests/data/upsample.onnx")?;
+	/// let mut session = Session::builder()?.with_intra_threads(2)?.commit_from_file("tests/data/upsample.onnx")?;
 	/// let input = ndarray::Array4::<f32>::zeros((1, 64, 64, 3));
 	/// let outputs = session.run_async(ort::inputs![TensorRef::from_array_view(&input)?])?.await?;
 	/// # 	Ok(())
 	/// # }) }
 	/// ```
 	#[cfg(feature = "std")]
-	#[cfg_attr(docsrs, doc(cfg(feature = "std")))] // TODO: parking_lot
+	#[cfg_attr(docsrs, doc(cfg(feature = "std")))]
 	pub fn run_async<'s, 'i, 'v: 'i + 's, const N: usize>(
-		&'s self,
+		&'s mut self,
 		input_values: impl Into<SessionInputs<'i, 'v, N>>
 	) -> Result<InferenceFut<'s, 's, 'v, NoSelectedOutputs>> {
 		match input_values.into() {
@@ -372,9 +430,9 @@ impl Session {
 	/// Asynchronously run input data through the ONNX graph, performing inference, with the given [`RunOptions`].
 	/// See [`Session::run_with_options`] and [`Session::run_async`] for more details.
 	#[cfg(feature = "std")]
-	#[cfg_attr(docsrs, doc(cfg(feature = "std")))] // TODO: parking_lot
+	#[cfg_attr(docsrs, doc(cfg(feature = "std")))]
 	pub fn run_async_with_options<'s, 'i, 'v: 'i + 's, 'r, O: SelectedOutputMarker, const N: usize>(
-		&'s self,
+		&'s mut self,
 		input_values: impl Into<SessionInputs<'i, 'v, N>>,
 		run_options: &'r RunOptions<O>
 	) -> Result<InferenceFut<'s, 'r, 'v, O>> {
@@ -468,7 +526,7 @@ impl Session {
 	/// Ends profiling for this session.
 	///
 	/// Note that this must be explicitly called at the end of profiling, otherwise the profiling file will be empty.
-	pub fn end_profiling(&self) -> Result<String> {
+	pub fn end_profiling(&mut self) -> Result<String> {
 		let mut profiling_name: *mut c_char = ptr::null_mut();
 
 		ortsys![unsafe SessionEndProfiling(self.inner.session_ptr.as_ptr(), self.inner.allocator.ptr().cast_mut(), &mut profiling_name)];
@@ -483,7 +541,7 @@ impl Session {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{run_options::RunOptions, Session, WorkloadType}, tensor::TensorElementType, value::{Value, ValueType, TensorRef}};
 	/// # fn main() -> ort::Result<()> {
-	/// let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+	/// let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 	/// session.set_workload_type(WorkloadType::Efficient)?;
 	///
 	/// let input = ndarray::Array4::<f32>::zeros((1, 64, 64, 3));
@@ -491,7 +549,7 @@ impl Session {
 	/// # 	Ok(())
 	/// # }
 	/// ```
-	pub fn set_workload_type(&self, workload_type: WorkloadType) -> Result<()> {
+	pub fn set_workload_type(&mut self, workload_type: WorkloadType) -> Result<()> {
 		static KEY: &[u8] = b"ep.dynamic.workload_type\0";
 		match workload_type {
 			WorkloadType::Default => self.set_dynamic_option(KEY.as_ptr().cast(), b"Default\0".as_ptr().cast()),
@@ -499,7 +557,7 @@ impl Session {
 		}
 	}
 
-	pub(crate) fn set_dynamic_option(&self, key: *const c_char, value: *const c_char) -> Result<()> {
+	pub(crate) fn set_dynamic_option(&mut self, key: *const c_char, value: *const c_char) -> Result<()> {
 		ortsys![unsafe SetEpDynamicOptions(self.inner.session_ptr.as_ptr(), &key, &value, 1)?];
 		Ok(())
 	}
diff --git a/src/session/output.rs b/src/session/output.rs
index 81ff92cc..f2faa9a0 100644
--- a/src/session/output.rs
+++ b/src/session/output.rs
@@ -19,7 +19,7 @@ use crate::{
 /// ```
 /// # use ort::{value::TensorRef, session::{builder::GraphOptimizationLevel, Session}};
 /// # fn main() -> ort::Result<()> {
-/// let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+/// let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 /// let input = ndarray::Array4::<f32>::zeros((1, 64, 64, 3));
 /// let outputs = session.run(ort::inputs![TensorRef::from_array_view(&input)?])?;
 ///
diff --git a/src/session/run_options.rs b/src/session/run_options.rs
index 0f79d5b6..ca722487 100644
--- a/src/session/run_options.rs
+++ b/src/session/run_options.rs
@@ -22,7 +22,7 @@ use crate::{
 /// # use std::sync::Arc;
 /// # use ort::{session::{Session, run_options::{RunOptions, OutputSelector}}, memory::Allocator, value::Tensor};
 /// # fn main() -> ort::Result<()> {
-/// let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+/// let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 /// let input = Tensor::<f32>::new(&Allocator::default(), [1, 64, 64, 3])?;
 ///
 /// let output0 = session.outputs[0].name.as_str();
@@ -101,7 +101,7 @@ impl OutputSelector {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{Session, run_options::{RunOptions, OutputSelector}}, memory::Allocator, value::Tensor};
 	/// # fn main() -> ort::Result<()> {
-	/// let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+	/// let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 	/// let input = Tensor::<f32>::new(&Allocator::default(), [1, 64, 64, 3])?;
 	///
 	/// let output0 = session.outputs[0].name.as_str();
@@ -199,7 +199,7 @@ impl<O: SelectedOutputMarker> RunOptions<O> {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{Session, run_options::{RunOptions, OutputSelector}}, memory::Allocator, value::Tensor};
 	/// # fn main() -> ort::Result<()> {
-	/// let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+	/// let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 	/// let input = Tensor::<f32>::new(&Allocator::default(), [1, 64, 64, 3])?;
 	///
 	/// let output0 = session.outputs[0].name.as_str();
@@ -254,7 +254,7 @@ impl<O: SelectedOutputMarker> RunOptions<O> {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{Session, run_options::{RunOptions, OutputSelector}}, value::Value};
 	/// # fn main() -> ort::Result<()> {
-	/// # 	let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+	/// # 	let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 	/// # 	let input = Value::from_array(ndarray::Array4::<f32>::zeros((1, 64, 64, 3)))?;
 	/// let run_options = Arc::new(RunOptions::new()?);
 	///
@@ -283,7 +283,7 @@ impl<O: SelectedOutputMarker> RunOptions<O> {
 	/// # use std::sync::Arc;
 	/// # use ort::{session::{Session, run_options::{RunOptions, OutputSelector}}, value::Value};
 	/// # fn main() -> ort::Result<()> {
-	/// # 	let session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+	/// # 	let mut session = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 	/// # 	let input = Value::from_array(ndarray::Array4::<f32>::zeros((1, 64, 64, 3)))?;
 	/// let run_options = Arc::new(RunOptions::new()?);
 	///
diff --git a/src/value/mod.rs b/src/value/mod.rs
index 2a7e85e8..a31b8fbf 100644
--- a/src/value/mod.rs
+++ b/src/value/mod.rs
@@ -203,7 +203,7 @@ impl<Type: ValueTypeMarker + ?Sized> DerefMut for ValueRefMut<'_, Type> {
 /// ```
 /// # use ort::{session::Session, value::Tensor};
 /// # fn main() -> ort::Result<()> {
-/// # 	let upsample = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
+/// # 	let mut upsample = Session::builder()?.commit_from_file("tests/data/upsample.onnx")?;
 /// // Create a Tensor value from a raw data vector
 /// let value = Tensor::from_array(([1usize, 1, 1, 3], vec![1.0_f32, 2.0, 3.0].into_boxed_slice()))?;
 ///
diff --git a/tests/mnist.rs b/tests/mnist.rs
index 49b1f6dd..b3813c1d 100644
--- a/tests/mnist.rs
+++ b/tests/mnist.rs
@@ -15,21 +15,25 @@ fn mnist_5() -> ort::Result<()> {
 
 	ort::init().with_name("integration_test").commit()?;
 
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level1)?
 		.with_intra_threads(1)?
 		.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/mnist.onnx")
 		.expect("Could not download model from file");
 
-	let metadata = session.metadata()?;
-	assert_eq!(metadata.name()?, "CNTKGraph");
-	assert_eq!(metadata.producer()?, "CNTK");
+	let input0_shape = {
+		let metadata = session.metadata()?;
+		assert_eq!(metadata.name()?, "CNTKGraph");
+		assert_eq!(metadata.producer()?, "CNTK");
 
-	let input0_shape: &Vec<i64> = session.inputs[0].input_type.tensor_dimensions().expect("input0 to be a tensor type");
-	let output0_shape: &Vec<i64> = session.outputs[0].output_type.tensor_dimensions().expect("output0 to be a tensor type");
+		let input0_shape: &Vec<i64> = session.inputs[0].input_type.tensor_dimensions().expect("input0 to be a tensor type");
+		let output0_shape: &Vec<i64> = session.outputs[0].output_type.tensor_dimensions().expect("output0 to be a tensor type");
 
-	assert_eq!(input0_shape, &[1, 1, 28, 28]);
-	assert_eq!(output0_shape, &[1, 10]);
+		assert_eq!(input0_shape, &[1, 1, 28, 28]);
+		assert_eq!(output0_shape, &[1, 10]);
+
+		input0_shape
+	};
 
 	// Load image and resize to model's shape, converting to RGB format
 	let image_buffer: ImageBuffer<Luma<u8>, Vec<u8>> = image::open(Path::new(env!("CARGO_MANIFEST_DIR")).join("tests").join("data").join(IMAGE_TO_LOAD))
diff --git a/tests/squeezenet.rs b/tests/squeezenet.rs
index fd4fac05..0c52e48f 100644
--- a/tests/squeezenet.rs
+++ b/tests/squeezenet.rs
@@ -21,23 +21,27 @@ fn squeezenet_mushroom() -> ort::Result<()> {
 
 	ort::init().with_name("integration_test").commit()?;
 
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level1)?
 		.with_intra_threads(1)?
 		.commit_from_url("https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/ex_models/squeezenet.onnx")
 		.expect("Could not download model from file");
 
-	let metadata = session.metadata()?;
-	assert_eq!(metadata.name()?, "main_graph");
-	assert_eq!(metadata.producer()?, "pytorch");
-
 	let class_labels = get_imagenet_labels()?;
 
-	let input0_shape: &Vec<i64> = session.inputs[0].input_type.tensor_dimensions().expect("input0 to be a tensor type");
-	let output0_shape: &Vec<i64> = session.outputs[0].output_type.tensor_dimensions().expect("output0 to be a tensor type");
+	let input0_shape = {
+		let metadata = session.metadata()?;
+		assert_eq!(metadata.name()?, "main_graph");
+		assert_eq!(metadata.producer()?, "pytorch");
+
+		let input0_shape: &Vec<i64> = session.inputs[0].input_type.tensor_dimensions().expect("input0 to be a tensor type");
+		let output0_shape: &Vec<i64> = session.outputs[0].output_type.tensor_dimensions().expect("output0 to be a tensor type");
+
+		assert_eq!(input0_shape, &[1, 3, 224, 224]);
+		assert_eq!(output0_shape, &[1, 1000]);
 
-	assert_eq!(input0_shape, &[1, 3, 224, 224]);
-	assert_eq!(output0_shape, &[1, 1000]);
+		input0_shape
+	};
 
 	// Load image and resize to model's shape, converting to RGB format
 	let image_buffer: ImageBuffer<Rgb<u8>, Vec<u8>> = image::open(Path::new(env!("CARGO_MANIFEST_DIR")).join("tests").join("data").join(IMAGE_TO_LOAD))
diff --git a/tests/upsample.rs b/tests/upsample.rs
index 47d12d76..f81a9c10 100644
--- a/tests/upsample.rs
+++ b/tests/upsample.rs
@@ -52,18 +52,20 @@ fn upsample() -> ort::Result<()> {
 
 	let session_data =
 		std::fs::read(Path::new(env!("CARGO_MANIFEST_DIR")).join("tests").join("data").join("upsample.onnx")).expect("Could not open model from file");
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level1)?
 		.with_intra_threads(1)?
 		.commit_from_memory(&session_data)
 		.expect("Could not read model from memory");
 
-	let metadata = session.metadata()?;
-	assert_eq!(metadata.name()?, "tf2onnx");
-	assert_eq!(metadata.producer()?, "tf2onnx");
+	{
+		let metadata = session.metadata()?;
+		assert_eq!(metadata.name()?, "tf2onnx");
+		assert_eq!(metadata.producer()?, "tf2onnx");
 
-	assert_eq!(session.inputs[0].input_type.tensor_dimensions().expect("input0 to be a tensor type"), &[-1, -1, -1, 3]);
-	assert_eq!(session.outputs[0].output_type.tensor_dimensions().expect("output0 to be a tensor type"), &[-1, -1, -1, 3]);
+		assert_eq!(session.inputs[0].input_type.tensor_dimensions().expect("input0 to be a tensor type"), &[-1, -1, -1, 3]);
+		assert_eq!(session.outputs[0].output_type.tensor_dimensions().expect("output0 to be a tensor type"), &[-1, -1, -1, 3]);
+	}
 
 	// Load image, converting to RGB format
 	let image_buffer = load_input_image(IMAGE_TO_LOAD);
@@ -93,7 +95,7 @@ fn upsample_with_ort_model() -> ort::Result<()> {
 
 	let session_data =
 		std::fs::read(Path::new(env!("CARGO_MANIFEST_DIR")).join("tests").join("data").join("upsample.ort")).expect("Could not open model from file");
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level1)?
 		.with_intra_threads(1)?
 		.commit_from_memory_directly(&session_data) // Zero-copy.
diff --git a/tests/vectorizer.rs b/tests/vectorizer.rs
index 00810857..aa01d082 100644
--- a/tests/vectorizer.rs
+++ b/tests/vectorizer.rs
@@ -12,17 +12,19 @@ use test_log::test;
 
 #[test]
 fn vectorizer() -> ort::Result<()> {
-	let session = Session::builder()?
+	let mut session = Session::builder()?
 		.with_optimization_level(GraphOptimizationLevel::Level1)?
 		.with_intra_threads(1)?
 		.commit_from_file(Path::new(env!("CARGO_MANIFEST_DIR")).join("tests").join("data").join("vectorizer.onnx"))
 		.expect("Could not load model");
 
-	let metadata = session.metadata()?;
-	assert_eq!(metadata.producer()?, "skl2onnx");
-	assert_eq!(metadata.description()?, "test description");
-	assert_eq!(metadata.custom_keys()?, ["custom_key"]);
-	assert_eq!(metadata.custom("custom_key")?.as_deref(), Some("custom_value"));
+	{
+		let metadata = session.metadata()?;
+		assert_eq!(metadata.producer()?, "skl2onnx");
+		assert_eq!(metadata.description()?, "test description");
+		assert_eq!(metadata.custom_keys()?, ["custom_key"]);
+		assert_eq!(metadata.custom("custom_key")?.as_deref(), Some("custom_value"));
+	}
 
 	let array = ndarray::CowArray::from(ndarray::Array::from_shape_vec((1,), vec!["document".to_owned()]).unwrap());