Deprecation Notices

This section contains information about deprecation of behaviours, features and APIs that have become undesirable/obsolete. Any information about the schedule for their deprecation and reasoning behind the changes, along with examples, is provided. However, first is a small section on how to suppress deprecation warnings that may be raised from Numba so as to prevent warnings propagating into code that is consuming Numba.

Suppressing Deprecation warnings

All Numba deprecations are issued via NumbaDeprecationWarning or NumbaPendingDeprecationWarning s, to suppress the reporting of these the following code snippet can be used:

from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
import warnings

warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning)

The action used above is 'ignore', other actions are available, see The Warnings Filter documentation for more information.

Note

It is strongly recommended that applications and libraries which choose to suppress these warnings should pin their Numba dependency to a suitable version because their users will no longer be aware of the coming incompatibility.

Deprecation of reflection for List and Set types

Reflection (reflection) is the jargon used in Numba to describe the process of ensuring that changes made by compiled code to arguments that are mutable Python container data types are visible in the Python interpreter when the compiled function returns. Numba has for some time supported reflection of list and set data types and it is support for this reflection that is scheduled for deprecation with view to replace with a better implementation.

Reason for deprecation

First recall that for Numba to be able to compile a function in nopython mode all the variables must have a concrete type ascertained through type inference. In simple cases, it is clear how to reflect changes to containers inside nopython mode back to the original Python containers. However, reflecting changes to complex data structures with nested container types (for example, lists of lists of integers) quickly becomes impossible to do efficiently and consistently. After a number of years of experience with this problem, it is clear that providing this behaviour is both fraught with difficulty and often leads to code which does not have good performance (all reflected data has to go through special APIs to convert the data to native formats at call time and then back to CPython formats at return time). As a result of this, the sheer number of reported problems in the issue tracker, and how well a new approach that was taken with typed.Dict (typed dictionaries) has gone, the core developers have decided to deprecate the noted reflection behaviour.

Example(s) of the impact

At present only a warning of the upcoming change is issued. In future code such as:

from numba import njit

@njit
def foo(x):
    x.append(10)

a = [1, 2, 3]
foo(a)

will require adjustment to use a typed.List instance, this typed container is synonymous to the Typed Dict. An example of translating the above is:

from numba import njit
from numba.typed import List

@njit
def foo(x):
    x.append(10)

a = [1, 2, 3]
typed_a = List()
[typed_a.append(x) for x in a]
foo(typed_a)

For more information about typed.List see Typed List. Further usability enhancements for this feature were made in the 0.47.0 release cycle.

Schedule

This feature will be removed with respect to this schedule:

Pending-deprecation warnings will be issued in version 0.44.0
Prominent notice will be given for a minimum of two releases prior to full removal.

Recommendations

Projects that need/rely on the deprecated behaviour should pin their dependency on Numba to a version prior to removal of this behaviour, or consider following replacement instructions that will be issued outlining how to adjust to the change.

Expected Replacement

As noted above typed.List will be used to permit similar functionality to reflection in the case of list s, a typed.Set will provide the equivalent for set (not implemented yet!). The advantages to this approach are:

That the containers are typed means type inference has to work less hard.
Nested containers (containers of containers of …) are more easily supported.
Performance penalties currently incurred translating data to/from native formats are largely avoided.
Numba’s typed.Dict will be able to use these containers as values.

Deprecation of object mode fall-back behaviour when using `@jit`

The numba.jit decorator has for a long time followed the behaviour of first attempting to compile the decorated function in nopython mode and should this compilation fail it will fall-back and try again to compile but this time in object mode. It is this fall-back behaviour which is being deprecated, the result of which will be that numba.jit will by default compile in nopython mode and object mode compilation will become opt-in only.

Note

It is relatively common for the numba.jit decorator to be used within other decorators to provide an easy path to compilation. Due to this change, deprecation warnings may be raised from such call sites. To avoid these warnings, it’s recommended to either suppress them if the application does not rely on object mode fall-back or to check the documentation for the decorator to see how to pass application appropriate options through to the wrapped numba.jit decorator. An example of this within the Numba API would be numba.vectorize. This decorator simply forwards keyword arguments to the internal numba.jit decorator call site such that e.g. @vectorize(nopython=True) would be an appropriate declaration for a nopython=True mode use of @vectorize.

Reason for deprecation

The fall-back has repeatedly caused confusion for users as seemingly innocuous changes in user code can lead to drastic performance changes as code which may have once compiled in nopython mode mode may silently switch to compiling in object mode e.g:

from numba import jit

@jit
def foo():
    l = []
    for x in range(10):
        l.append(x)
    return l

foo()

assert foo.nopython_signatures # this was compiled in nopython mode

@jit
def bar():
    l = []
    for x in range(10):
        l.append(x)
    return reversed(l) # innocuous change, but no reversed support in nopython mode

bar()

assert not bar.nopython_signatures # this was not compiled in nopython mode

Another reason to remove the fall-back is that it is confusing for the compiler engineers developing Numba as it causes internal state problems that are really hard to debug and it makes manipulating the compiler pipelines incredibly challenging.

Further, it has long been considered best practice that the nopython mode keyword argument in the numba.jit decorator is set to True and that any user effort spent should go into making code work in this mode as there’s very little gain if it does not. The result is that, as Numba has evolved, the amount of use object mode gets in practice and its general utility has decreased. It can be noted that there are some minor improvements available through the notion of loop-lifting, the cases of this being used in practice are, however, rare and often a legacy from use of less-recent Numba whereby such behaviour was better accommodated/the use of @jit with fall-back was recommended.

Example(s) of the impact

At present a warning of the upcoming change is issued if @jit decorated code uses the fall-back compilation path. In future code such as:

@jit
def bar():
    l = []
    for x in range(10):
        l.append(x)
    return reversed(l)

bar()

will simply not compile, a TypingError would be raised.

A further consequence of this change is that the nopython keyword argument will become redundant as nopython mode will be the default. As a result, following this change, supplying the keyword argument as nopython=False will trigger a warning stating that the implicit default has changed to True. Essentially this keyword will have no effect following removal of this feature.

Schedule

This feature will be removed with respect to this schedule:

Deprecation warnings will be issued in version 0.44.0.
Prominent notice is given in 0.57.0.
Removal will take place in version 0.59.0.

Recommendations

Projects that need/rely on the deprecated behaviour should pin their dependency on Numba to a version prior to removal of this behaviour.

General advice to accommodate the scheduled deprecation:

Users with code compiled at present with @jit can supply the nopython=True keyword argument, if the code continues to compile then the code is already ready for this change. If the code does not compile, continue using the @jit decorator without nopython=True and profile the performance of the function. Then remove the decorator and again check the performance of the function. If there is no benefit to having the @jit decorator present consider removing it! If there is benefit to having the @jit decorator present, then to be future proof supply the keyword argument forceobj=True to ensure the function is always compiled in object mode.

Advice for users of the “loop-lifting” feature:

If object mode compilation with loop-lifting is needed it should be explicitly declared through supplying the keyword arguments forceobj=True and looplift=True to the @jit decorator.

Advice for users setting nopython=False:

This is essentially specifying the implicit default prior to removal of this feature, either remove the keyword argument or change the value to True.

Deprecation of `generated_jit`

The top level API function numba.generated_jit provides functionality that allows users to write JIT compilable functions that have different implementations based on the types of the arguments to the function. This is a hugely useful concept and is also key to Numba’s internal implementation.

Reason for deprecation

There are a number of reasons for this deprecation.

First, generated_jit breaks the concept of “JIT transparency” in that if the JIT compiler is disabled, the source code does not execute the same way as it would were the JIT compiler present.

Second, internally Numba uses the numba.extending.overload family of decorators to access an equivalent functionality to generated_jit. The overload family of decorators are more powerful than generated_jit as they support far more options and both the CPU and CUDA targets. Essentially a replacement for generated_jit already exists and has been recommended and preferred for a long while.

Third, the public extension API decorators are far better maintained than generated_jit. This is an important consideration due to Numba’s limited resources, fewer duplicated pieces of functionality to maintain will reduce pressure on these resources.

For more information on the overload family of decorators see the high level extension API documentation.

Example(s) of the impact

Any source code using generated_jit would fail to work once the functionality has been removed.

Schedule

This feature will be removed with respect to this schedule:

Deprecation warnings will be issued in version 0.57.0.
Removal will take place in version 0.59.0.

Recommendations

Projects that need/rely on the deprecated behaviour should pin their dependency on Numba to a version prior to removal of this behaviour, or consider following replacement instructions below that outline how to adjust to the change.

Replacement

The overload decorator offers a replacement for the functionality available through generated_jit. An example follows of translating from one to the other. First define a type specialised function dispatch with the generated_jit decorator:

from numba import njit, generated_jit, types

@generated_jit
def select(x):
    if isinstance(x, types.Float):
        def impl(x):
            return x + 1
        return impl
    elif isinstance(x, types.UnicodeType):
        def impl(x):
            return x + " the number one"
        return impl
    else:
        raise TypeError("Unsupported Type")

@njit
def foo(x):
    return select(x)

print(foo(1.))
print(foo("a string"))

Conceptually, generated_jit is like overload, but with generated_jit the overloaded function is the decorated function. Taking the example above and adjusting it to use the overload API:

from numba import njit, types
from numba.extending import overload

# A pure python implementation that will run if the JIT compiler is disabled.
def select(x):
    if isinstance(x, float):
        return x + 1
    elif isinstance(x, str):
        return x + " the number one"
    else:
        raise TypeError("Unsupported Type")

# An overload for the `select` function cf. generated_jit
@overload(select)
def ol_select(x):
    if isinstance(x, types.Float):
        def impl(x):
            return x + 1
        return impl
    elif isinstance(x, types.UnicodeType):
        def impl(x):
            return x + " the number one"
        return impl
    else:
        raise TypeError("Unsupported Type")

@njit
def foo(x):
    return select(x)

print(foo(1.))
print(foo("a string"))

Further, users that are using generated_jit to dispatch on some of the more primitive types may find that Numba’s support for isinstance is sufficient, for example:

@njit # NOTE: standard @njit decorator.
def select(x):
    if isinstance(x, float):
        return x + 1
    elif isinstance(x, str):
        return x + " the number one"
    else:
        raise TypeError("Unsupported Type")

@njit
def foo(x):
    return select(x)

print(foo(1.))
print(foo("a string"))

Deprecation of the `numba.pycc` module

Numba has supported some degree of Ahead-of-Time (AOT) compilation through the use of the tools in the numba.pycc module. This capability is very important to the Numba project and following an assessment of the viability of the current approach, it was decided to deprecate it in favour of developing new technology to better meet current needs.

Reason for deprecation

There are a number of reasons for this deprecation.

numba.pycc tools create C-Extensions that have symbols that are only usable from the Python interpreter, they are not compatible with calls made from within code compiled using Numba’s JIT compiler. This drastically reduces the utility of AOT compiled functions.
numba.pycc has some reliance on setuptools (and distutils) which is something Numba is trying to reduce, particularly due to the upcoming removal of distutils in Python 3.12.
The numba.pycc compilation chain is very limited in terms of its feature set in comparison to Numba’s JIT compiler, it also has numerous technical issues to do with declaring and linking both internal and external libraries.
The number of users of numba.pycc is assumed to be quite small, this was indicated through discussions at a Numba public meeting on 2022-10-04 and issue #8509.
The Numba project is working on new innovations in the AOT compiler space and the maintainers consider it a better use of resources to develop these than maintain and develop numba.pycc.

Example(s) of the impact

Any source code using numba.pycc would fail to work once the functionality has been removed.

Schedule

This feature will be removed with respect to this schedule:

Pending-deprecation warnings will be issued in version 0.57.0.
Deprecation warnings will be issued once a replacement is developed.
Deprecation warnings will be given for a minimum of two releases prior to full removal.

Recommendations

Projects that need/rely on the deprecated behaviour should pin their dependency on Numba to a version prior to removal of this behaviour, or consider following replacement instructions below that outline how to adjust to the change.

Replacement

A replacement for this functionality is being developed as part of the Numba 2023 development focus. The numba.pycc module will not be removed until this replacement functionality is able to provide similar utility and offer an upgrade path. At the point of the new technology being deemed suitable, replacement instructions will be issued.

Deprecation and removal of CUDA Toolkits < 11.2 and devices with CC < 5.0

Support for CUDA toolkits less than 11.0 has been removed.
Support for CUDA toolkits less than 11.2 will be removed in future.
Support for devices with Compute Capability < 5.0 is deprecated and will be removed in the future.
Previous deprecation notices stated that support for Compute Capability < 5.3 was deprecated - this has now been modified such that 5.0 - 5.2 is undeprecated, and only support for devices with CC < 5.0 is deprecated.

Recommendations

For devices of Compute Capability 3.0 and 3.2, Numba 0.55.1 or earlier will be required.
CUDA toolkit 11.0 or later (ideally 11.2 or later) should be installed.

Schedule

In Numba 0.55.1: support for CC < 5.3 and CUDA toolkits < 10.2 was deprecated.
In Numba 0.56: support for CC < 3.5 and CUDA toolkits < 10.2 was removed.
In Numba 0.57: Support for CUDA toolkit 10.2 was removed.
In Numba 0.58: Support for CC < 5.0 and CUDA toolkits 11.0 and 11.1 will be removed.

Deprecation Notices

Suppressing Deprecation warnings

Deprecation of reflection for List and Set types

Reason for deprecation

Example(s) of the impact

Schedule

Recommendations

Expected Replacement

Deprecation of object mode fall-back behaviour when using @jit

Reason for deprecation

Example(s) of the impact

Schedule

Recommendations

Deprecation of generated_jit

Reason for deprecation

Example(s) of the impact

Schedule

Recommendations

Replacement

Deprecation of the numba.pycc module

Reason for deprecation

Example(s) of the impact

Schedule

Recommendations

Replacement

Deprecation and removal of CUDA Toolkits < 11.2 and devices with CC < 5.0

Recommendations

Schedule

Deprecation of object mode fall-back behaviour when using `@jit`

Deprecation of `generated_jit`

Deprecation of the `numba.pycc` module