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.
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.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.
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.
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 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.
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.
This feature will be removed with respect to this schedule:
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.
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:
typed.Dict will be able to use these containers as values.@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.
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.
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.
This feature will be removed with respect to this schedule:
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.