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 it 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.

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.

Schedule

This feature will be removed with respect to this schedule:

  • 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. Alternatively, to accommodate the scheduled deprecations, 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.

Deprecation of the inspect_ptx() method

The undocumented inspect_ptx() method of functions decorated with @cuda.jit(device=True) is sometimes used to compile a Python function to PTX for use outside of Numba. An interface for this specific purpose is provided in the compile_ptx() function. inspect_ptx() has one or two longstanding issues and presents a maintenance burden for upcoming changes in the CUDA target, so it is deprecated and will be removed in favor of the use of compile_ptx().

Recommendations

Replace any code that compiles device functions to PTX using the following pattern:

@cuda.jit(signature, device=True)
def func(args):
    ...

ptx_code = func.inspect_ptx(nvvm_options=nvvm_options).decode()

with:

def func(args):
    ...

ptx_code, return_type = compile_ptx(func, signature, device=True, nvvm_options=nvvm_options)

Schedule

  • In Numba 0.54: inspect_ptx() was deprecated.

  • In Numba 0.55: inspect_ptx() was removed.

Deprecation of eager compilation of CUDA device functions

In future versions of Numba, the device kwarg to the @cuda.jit decorator will be obviated, and whether a device function or global kernel is compiled will be inferred from the context. With respect to kernel / device functions and lazy / eager compilation, four cases were handled:

  1. device=True, eager compilation with a signature provided

  2. device=False, eager compilation with a signature provided

  3. device=True, lazy compilation with no signature

  4. device=False, lazy compilation with no signature

The latter two cases can be differentiated without the device kwarg, because it can be inferred from the calling context - if the call is from the host, then a global kernel should be compiled, and if the call is from a kernel or another device function, then a device function should be compiled.

The first two cases cannot be differentiated in the absence of the device kwarg - without it, it will not be clear from a signature alone whether a device function or global kernel should be compiled. In order to resolve this, device functions will no longer be eagerly compiled. When a signature is provided to a device function, it will only be used to enforce the types of arguments that the function accepts.

Note

In previous releases this notice stated that support for providing signatures to device functions would be removed completely - however, this precludes the common use case of enforcing the types that can be passed to a device function (and the automatic insertion of casts that it implies) so this notice has been updated to retain support for passing signatures.

Schedule

  • In Numba 0.54: Eager compilation of device functions will be deprecated.

  • In Numba 0.55: Eager compilation of device functions will be unsupported and the provision of signatures for device functions will only enforce casting.

Deprecation and removal of numba.core.base.BaseContext.add_user_function()

add_user_function() offered the same functionality as insert_user_function(), only with a check that the function has already been inserted at least once. It is now removed as it was no longer used internally and it was expected that it was not used externally.

Recommendations

Replace any uses of add_user_function() with insert_user_function().

Schedule

  • In Numba 0.55: add_user_function() was deprecated.

  • In Numba 0.56: add_user_function() was removed.

Deprecation and removal of CUDA Toolkits < 10.2 and devices with CC < 5.3

  • Support for CUDA toolkits less than 10.2 was deprecated and removed.

  • Support for devices with Compute Capability < 5.3 is deprecated and will be removed in the future.

Recommendations

  • For devices of Compute Capability 3.0 and 3.2, Numba 0.55.1 or earlier will be required.

  • CUDA toolkit 10.2 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 CC < 5.3 will be removed.