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Python bindingslink


IREE offers Python bindings split into several packages, covering different components:

PIP package name Description
iree-compiler IREE's generic compiler tools and helpers
iree-runtime IREE's runtime, including CPU and GPU backends
iree-tools-tf Tools for importing from TensorFlow
iree-tools-tflite Tools for importing from TensorFlow Lite
iree-jax Tools for importing from JAX

Collectively, these packages allow for importing from frontends, compiling towards various targets, and executing compiled code on IREE's backends.


To use IREE's Python bindings, you will first need to install Python 3 and pip, as needed.

Tip - Virtual environments

We recommend using virtual environments to manage python packages, such as through venv (about, tutorial):

python -m venv .venv
source .venv/bin/activate
python -m venv .venv
source .venv/bin/activate
python -m venv .venv

When done, run deactivate.

Installing IREE packageslink

Prebuilt packageslink

Stable release packages are published to PyPI.

python -m pip install \
  iree-compiler \

Nightly releases are published on GitHub releases.

python -m pip install \
  --find-links \
  --upgrade \
  iree-compiler \

Building from sourcelink

See Building Python bindings page for instructions for building from source.


Info - API reference pages

API reference pages for IREE's runtime and compiler Python APIs are hosted on readthedocs.

Documentation for the MLIR compiler Python APIs can be found at

Compile a programlink

Open In Colab

from iree import compiler as ireec

# Compile a module.
module @arithmetic {
  func.func @simple_mul(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
    %0 = arith.mulf %arg0, %arg1 : tensor<4xf32>
    return %0 : tensor<4xf32>

# Compile using the vmvx (reference) target:
compiled_flatbuffer =

Run a compiled programlink

Open In Colab

from iree import runtime as ireert
import numpy as np

# Register the module with a runtime context.
# Use the "local-task" CPU driver, which can load the vmvx executable:
config = ireert.Config("local-task")
ctx = ireert.SystemContext(config=config)
vm_module = ireert.VmModule.copy_buffer(ctx.instance, compiled_flatbuffer)

# Invoke the function and print the result.
print("INVOKE simple_mul")
arg0 = np.array([1., 2., 3., 4.], dtype=np.float32)
arg1 = np.array([4., 5., 6., 7.], dtype=np.float32)
f = ctx.modules.arithmetic["simple_mul"]
results = f(arg0, arg1).to_host()
print("Results:", results)


Check out the samples in IREE's samples/colab/ directory and the iree-experimental repository for examples using the Python APIs.

Console scriptslink

The Python packages include console scripts for most of IREE's native tools like iree-compile and iree-run-module. After installing a package from pip, these should be added to your path automatically:

$ python -m pip install iree-runtime
$ which iree-run-module



The tools in the iree-runtime package support variants:

Variant name Description
default Standard runtime tools
tracy Runtime tools instrumented using the Tracy profiler

Switch between variants of the installed tools using the IREE_PY_RUNTIME environment variable:

IREE_PY_RUNTIME=tracy iree-run-module ...

See the developer documentation page on Profiling with Tracy for information on using Tracy.

Tip - flushing profile data

When writing a Python-based program that you want to profile you may need to insert IREE runtime calls to periodically flush the profile data:

device = ... # HalDevice