Configuration Settings and Compiling Modes

Configuration Settings and Compiling Modes#


The aesara.config module contains several attributes that modify Aesara’s behavior. Many of these attributes are examined during the import of the aesara module and several are assumed to be read-only.

As a rule, the attributes in the aesara.config module should not be modified inside the user code.

Aesara’s code comes with default values for these attributes, but you can override them from your .aesararc file, and override those values in turn by the AESARA_FLAGS environment variable.

The order of precedence is:

  1. values of aesara.config properties,

  2. values specified in AESARA_FLAGS, and

  3. values specified in the .aesararc file (or the file indicated in AESARARC).

You can display the current/effective configuration at any time by printing aesara.config. For example, to see a list of all active configuration variables, type this from the command-line:

python -c 'import aesara; print(aesara.config)' | less

For more detail, see Configuration in the library.


Consider the logistic regression:

import numpy as np
import aesara
import aesara.tensor as at

rng = np.random.default_rng(2498)

N = 400
feats = 784
D = (rng.standard_normal((N, feats)).astype(aesara.config.floatX),
rng.integers(size=N,low=0, high=2).astype(aesara.config.floatX))
training_steps = 10000

# Declare Aesara symbolic variables
x = at.matrix("x")
y = at.vector("y")
w = aesara.shared(rng.standard_normal(feats).astype(aesara.config.floatX), name="w")
b = aesara.shared(np.asarray(0., dtype=aesara.config.floatX), name="b")
x.tag.test_value = D[0]
y.tag.test_value = D[1]

# Construct Aesara expression graph
p_1 = 1 / (1 + at.exp(, w)-b)) # Probability of having a one
prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
xent = -y*at.log(p_1) - (1-y)*at.log(1-p_1) # Cross-entropy
cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
gw,gb = at.grad(cost, [w,b])

# Compile expressions to functions
train = aesara.function(
    outputs=[prediction, xent],
    updates=[(w, w-0.01*gw), (b, b-0.01*gb)],
    name = "train"
predict = aesara.function(
    inputs=[x], outputs=prediction,
    name = "predict"

if any(x.op.__class__.__name__ in ['Gemv', 'CGemv', 'Gemm', 'CGemm']
       for x in train.maker.fgraph.toposort()):
    print('Used the cpu')
    print('ERROR, not able to tell if aesara used the cpu or another device')

for i in range(training_steps):
    pred, err = train(D[0], D[1])

print("target values for D")

print("prediction on D")

Modify and execute this example to run on CPU (the default) with floatX=float32 and time the execution using the command line time python Save your code as it will be useful later on.


  • Apply the Aesara flag floatX=float32 (through aesara.config.floatX) in your code.

  • Cast inputs before storing them into a shared variable.

  • Circumvent the automatic cast of int32 with float32 to float64:

    • Insert manual cast in your code or use [u]int{8,16}.

    • Insert manual cast around the mean operator (this involves division by length, which is an int64).

    • Note that a new casting mechanism is being developed.


Default Modes#

Every time aesara.function is called, the symbolic relationships between the input and output Aesara variables are rewritten and compiled. The way this compilation occurs is controlled by the value of the mode parameter and/or aesara.config.mode value.

Aesara defines the following modes by name:

  • 'FAST_COMPILE': Apply just a few graph optimizations and only use Python implementations.

  • 'FAST_RUN': Apply all optimizations and use C implementations where possible.

  • 'DebugMode': Verify the correctness of all optimizations, and compare C and Python

    implementations. This mode can take much longer than the other modes, but can identify several kinds of problems.

  • 'NanGuardMode': Same optimization as FAST_RUN, but check if a node generate nans.

The default mode is typically 'FAST_RUN', but it can be controlled via the configuration variable aesara.config.mode.

short name

Full constructor

What does it do?


compile.mode.Mode(linker='py', optimizer='fast_compile')

Python implementations only, quick and cheap graph transformations


compile.mode.Mode(linker='cvm', optimizer='fast_run')

C implementations where available, all available graph transformations.



Both implementations where available, all available graph transformations.


For debugging purpose, there also exists a MonitorMode (which has no short name). It can be used to step through the execution of a function: see the debugging FAQ for details.

Default Linkers#

A Mode object is composed of two things: an optimizer and a linker. Some modes, like NanGuardMode and DebugMode, add logic around the optimizer and linker. DebugMode uses its own linker.

You can select which linker to use with the Aesara flag config.linker. Here is a table to compare the different linkers.


gc [1]

Raise error by op







As c|py, but the runtime algo to execute the code is in c





As cvm, but without gc

c|py [2]




Try C code. If none exists for an op, use Python





As c|py, but without gc





Use only C code (if none available for an op, raise an error)





Use only Python code





Check if nodes generate NaN





Make many checks on what Aesara computes

For more detail, see Mode in the library.

Default Optimizers#

Aesara allows compilations with a number of predefined rewrites that are expected to improve graph evaluation performance on average. An optimizer is technically just a Rewriter, or an object that indicates a particular set of rewrites (e.g. a string used to query optdb for a Rewriter).

The optimizers Aesara provides are summarized below to indicate the trade-offs one might make between compilation time and execution time.

These optimizers can be enabled globally with the Aesara config flag optimizer=name, or per call to Aesara functions with function(...mode=Mode(optimizer="name")).


Compile time

Execution time





Applies none of Aesara’s rewrites

o1 (fast_compile)



Applies only basic rewrites




Applies few basic rewrites and some that compile fast




Applies all rewrites except ones that compile slower

o4 (fast_run)



Applies all rewrites




Applies all rewrites, and removes safety checks




Only applies stability rewrites

For a detailed list of the specific rewrites applied for each of these optimizers, see Optimizations.

Using DebugMode#

While normally you should use the FAST_RUN or FAST_COMPILE mode, it is useful at first–especially when you are defining new kinds of expressions or new rewrites–to run your code using the DebugMode (available via mode='DebugMode). The DebugMode is designed to run several self-checks and assertions that can help diagnose possible programming errors leading to incorrect output. Note that DebugMode is much slower than FAST_RUN or FAST_COMPILE, so use it only during development.

DebugMode is used as follows:

x = at.dvector('x')

f = aesara.function([x], 10 * x, mode='DebugMode')


If any problem is detected, DebugMode will raise an exception according to what went wrong, either at call time (e.g. f(5)) or compile time ( f = aesara.function(x, 10 * x, mode='DebugMode')). These exceptions should not be ignored; talk to your local Aesara guru or email the users list if you cannot make the exception go away.

Some kinds of errors can only be detected for certain input value combinations. In the example above, there is no way to guarantee that a future call to, say f(-1), won’t cause a problem. DebugMode is not a silver bullet.

If you instantiate DebugMode using the constructor (see DebugMode) rather than the keyword DebugMode you can configure its behaviour via constructor arguments. The keyword version of DebugMode (which you get by using mode='DebugMode') is quite strict.

For more detail, see DebugMode in the library.