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IRON Python Configurations

There are several options that exist to configure the IRON Python programming environment.

Default IRON Tensor Class

This is a variable that controls the types of aie.utils.Tensors that are produced by the utility functions tensor, ones, etc. Right now there are two tensor implementations: CPUOnlyTensor and XRTTensor.

By default, if pyxrt is available, the DEFAULT_TENSOR_CLASS is set to XRTTensor. However, you can also manually set this value through the set_tensor_class(), e.g.:

>>> import numpy as np
>>> print(aie.utils.tensor.DEFAULT_TENSOR_CLASS.__name__)
XRTTensor
>>> type(iron.tensor((2, 2), np.int32))
<class 'aie.utils.xrtruntime.tensor.XRTTensor'>
>>> aie.utils.set_tensor_class(aie.utils.tensor.CPUOnlyTensor)
>>> print(aie.utils.tensor.DEFAULT_TENSOR_CLASS.__name__)
CPUOnlyTensor
>>> type(aie.utils.tensor((2, 2), np.int32))
<class 'aie.utils.tensor.CPUOnlyTensor'>

Default IRON Device

If the IRON device is not set, many designs will try it fetch it on demand using the utility function detect_npu_device(). However, this can be overriden by calling the set_current_device() function, which takes as an argument the new device and returns the previous device:

>>> import aie.iron as iron
>>> iron.set_current_device(iron.device.NPU1())
<abc.NPU2 object at 0x722a659826c0>
>>> iron.get_current_device()
<abc.NPU1 object at 0x722a65903a10>

IRON Cache Location

The IRON jit feature caches compiled objects in a directory defined by NPU_CACHE_DIR. By default this value is the user's home directory.

IRON XRT Runtime Cache Size

The CachedXRTRuntime caches XRT contexts to improve performance. The size of this cache can be configured using the XRT_CONTEXT_CACHE_SIZE environment variable. This is particularly useful in CI environments where multiple tests run in parallel and might exhaust the available NPU contexts.

export XRT_CONTEXT_CACHE_SIZE=1

Diagnostic Output and Log Level

The aie library uses Python's standard logging module for all diagnostic output. Set AIE_LOG_LEVEL to control verbosity. Valid values: DEBUG, INFO, WARNING (default), ERROR, CRITICAL.

AIE_LOG_LEVEL=DEBUG python my_script.py    # show debug messages
AIE_LOG_LEVEL=INFO python my_script.py     # show info and above
AIE_LOG_LEVEL=ERROR python my_script.py    # errors only

For per-module control or routing to a file, use the logging API directly:

import logging

logging.getLogger("aie").setLevel(logging.ERROR)

# Route aie logs to a file instead of the console
handler = logging.FileHandler("aie.log")
handler.setFormatter(logging.Formatter("%(asctime)s %(name)s %(levelname)s %(message)s"))
logging.getLogger("aie").addHandler(handler)
logging.getLogger("aie").propagate = False  # don't also send to root logger