# 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 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 11.2 has been removed.

- Support for devices with Compute Capability < 5.0 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 11.2 or later should be installed.

### Schedule

- In Numba 0.55.1: support for CC < 5.0 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 CUDA toolkits 11.0 and 11.1 was removed.

- In a future release: Support for CC < 5.0 will be removed.

## Deprecation of old-style `NUMBA_CAPTURED_ERRORS`

The use of the `NUMBA_CAPTURED_ERRORS` environment variable is deprecated and removed.

### Reason for deprecation

Previously, this variable allowed controlling how Numba handles exceptions during compilation that do not inherit from `numba.core.errors.NumbaError`. The default “old_style” behavior was to capture and wrap these errors, often obscuring the original exception.

The new “new_style” option treats non-`NumbaError` exceptions as hard errors, propagating them without capturing. This differentiates compilation errors from unintended exceptions during compilation.

The old style was removed in favor of the new behavior.

### Impact

The impact of this deprecation will only affect those who are extending Numba functionality.

### Recommendations

- Modify any code that raises a non-`NumbaError` to indicate a compilation error to raise a subclass of `NumbaError` instead. For example, instead of raising a `TypeError`, raise a `numba.core.errors.NumbaTypeError`.

### Schedule

- In Numba 0.58: `NUMBA_CAPTURED_ERRORS=old_style` was deprecated. Warnings will be raised when old_style error capturing is used.

- In Numba 0.59: explicitly setting `NUMBA_CAPTURED_ERRORS=old_style` will raise deprecation warnings.

- In Numba 0.60: `NUMBA_CAPTURED_ERRORS=new_style` became the default.

- In Numba 0.61: support for `NUMBA_CAPTURED_ERRORS=old_style` was removed.

## Deprecation of the built-in CUDA target

The CUDA target is now maintained in a separate package, numba-cuda, and the built-in CUDA target is deprecated.

### Reason for deprecation

Development of the CUDA target has been moved to the `numba-cuda` package to proceed independently of Numba development. See Built-in CUDA target deprecation and maintenance status.

### Impact

The built-in CUDA target is still supported by Numba 0.61 and will continue to be provided through at least Numba 0.62, but new changes to the built-in target are not expected; bug fixes and new features will be added in `numba-cuda`. No code changes are required to any code that uses the CUDA target.

### Recommendations

Users should install the `numba-cuda` package when using the CUDA target.

To install `numba-cuda` with `pip`:

    pip install numba-cuda

To install `numba-cuda` with `conda`, for example from the `conda-forge` channel:

    conda install conda-forge::numba-cuda

Maintainers of packages that use the CUDA target should add `numba-cuda` as a dependency in addition to `numba`, or replace the `numba` dependency with `numba-cuda` if the CUDA target is used exclusively.

### Schedule

- In Numba 0.61: The built-in CUDA target is deprecated.

- In Numba 0.63: Use of the CUDA target when the `numba-cuda` package is not installed will cause the emission of a warning prompting the installation of `numba-cuda`.

- In a future version of Numba no less than 0.63: The built-in CUDA target will be removed, and use of the CUDA target in the absence of the `numba-cuda` package will raise an error.

## Deprecation of macOS x86-64 (Intel) platform support

Official support for the Intel x86-64 macOS platform (`osx-64`) has been deprecated and moved to Tier 2 under the Support Tiers policy.

### Reason for deprecation

GitHub Actions retired the `macos-13` Intel Mac runners, which were the only free-tier Intel Mac images available when the tiered support policy was established. This violates the Tier 1 requirement that “the operating system and hardware target must be supported by GitHub Actions on the free-tier” (see Support Tiers). Additionally, conda-based Python distribution support for Intel Mac (`osx-64`) has been discontinued.

### Impact

- No CI testing or binary artifacts will be released for `osx-64` starting with Numba 0.63.0

- Platform code remains in source tree and may continue to work

- Bug reports and PRs for Intel Macs will not block releases

### Recommendations

- Pin dependency to `numba<0.63` to use the last officially supported release for Intel Macs

### Schedule

- Numba 0.62.x: Last release with official Intel macOS support

- Numba 0.63.0+: Intel macOS deprecated, moved to Tier 2 (community support only)

See issue #10187 for details.