# Environment variables

Note

This section relates to environment variables that impact Numba’s runtime, for compile time environment variables see Build time environment variables and configuration of optional components.

Numba allows its behaviour to be changed through the use of environment variables. Unless otherwise mentioned, those variables have integer values and default to zero.

For convenience, Numba also supports the use of a configuration file to persist configuration settings. Note: To use this feature `pyyaml` must be installed.

The configuration file must be named `.numba_config.yaml` and be present in the directory from which the Python interpreter is invoked. The configuration file, if present, is read for configuration settings before the environment variables are searched. This means that the environment variable settings will override the settings obtained from a configuration file (the configuration file is for setting permanent preferences whereas the environment variables are for ephemeral preferences).

The format of the configuration file is a dictionary in `YAML` format that maps the environment variables below (without the `NUMBA_` prefix) to a desired value. For example, to permanently switch on developer mode (`NUMBA_DEVELOPER_MODE` environment variable) and control flow graph printing (`NUMBA_DUMP_CFG` environment variable), create a configuration file with the contents:

    developer_mode: 1
    dump_cfg: 1

This can be especially useful in the case of wanting to use a set color scheme based on terminal background color. For example, if the terminal background color is black, the `dark_bg` color scheme would be well suited and can be set for permanent use by adding:

    color_scheme: dark_bg

## Jit flags

These variables globally override flags to the `jit()` decorator.

NUMBA_BOUNDSCHECK

  If set to 0 or 1, globally disable or enable bounds checking, respectively. The default if the variable is not set or set to an empty string is to use the `boundscheck` flag passed to the `jit()` decorator for a given function. See the documentation of @jit for more information.

  Note, due to limitations in numba, the bounds checking currently produces exception messages that do not match those from NumPy. If you set `NUMBA_FULL_TRACEBACKS=1`, the full exception message with the axis, index, and shape information will be printed to the terminal.

## Debugging

These variables influence what is printed out during compilation of JIT functions.

NUMBA_DEVELOPER_MODE

  If set to non-zero, developer mode produces full tracebacks and disables help instructions. Default is zero.

NUMBA_FULL_TRACEBACKS

  If set to non-zero, enable full tracebacks when an exception occurs. Defaults to the value set by NUMBA_DEVELOPER_MODE.

NUMBA_SHOW_HELP

  If set to non-zero, show resources for getting help. Default is zero.

NUMBA_DISABLE_ERROR_MESSAGE_HIGHLIGHTING

  If set to non-zero error message highlighting is disabled. This is useful for running the test suite on CI systems.

NUMBA_COLOR_SCHEME

  Alters the color scheme used in error reporting (requires the `colorama` package to be installed to work). Valid values are:

  - `no_color` No color added, just bold font weighting.

  - `dark_bg` Suitable for terminals with a dark background.

  - `light_bg` Suitable for terminals with a light background.

  - `blue_bg` Suitable for terminals with a blue background.

  - `jupyter_nb` Suitable for use in Jupyter Notebooks.

  Default value: `no_color`. The type of the value is `string`.

NUMBA_HIGHLIGHT_DUMPS

  If set to non-zero and `pygments` is installed, syntax highlighting is applied to Numba IR, LLVM IR and assembly dumps. Default is zero.

NUMBA_DISABLE_PERFORMANCE_WARNINGS

  If set to non-zero the issuing of performance warnings is disabled. Default is zero.

NUMBA_DEBUG

  If set to non-zero, print out all possible debugging information during function compilation. Finer-grained control can be obtained using other variables below.

NUMBA_DEBUG_FRONTEND

  If set to non-zero, print out debugging information during operation of the compiler frontend, up to and including generation of the Numba Intermediate Representation.

NUMBA_DEBUG_NRT

  If set to non-zero, print out debugging information at runtime about the use of Numba run time (NRT) reference count operations. If set to non-zero, this also switches on the filling of all NRT allocated regions with an identifiable “marker” byte pattern, `0xCB` on allocation and `0xDE` on deallocation, both to help with debugging memory leaks.

NUMBA_NRT_STATS

  If set to non-zero, enable the Numba run time (NRT) statistics counters. These counters are enabled process wide on import of Numba and are atomic.

NUMBA_DEBUGINFO

  If set to non-zero, enable debug for the full application by setting the default value of the `debug` option in `jit`. Beware that enabling debug info significantly increases the memory consumption for each compiled function. Default value equals to the value of NUMBA_ENABLE_PROFILING.

NUMBA_EXTEND_VARIABLE_LIFETIMES

  If set to non-zero, extend the lifetime of variables to the end of the block in which their lifetime ends. This is particularly useful in conjunction with `NUMBA_DEBUGINFO` as it helps with introspection of values. Default is zero.

NUMBA_GDB_BINARY

  Set the `gdb` binary for use in Numba’s `gdb` support. This takes one of two forms: 1) a path and full name of the binary to explicitly express which binary to use 2) just the name of the binary and the current path will be searched using the standard path resolution rules. For example: `/path/from/root/to/binary/name_of_gdb_binary` or `custom_gdb_binary_name`. This is to permit the use of a `gdb` from a non-default location with a non-default name. The default value is `gdb`.

NUMBA_DEBUG_TYPEINFER

  If set to non-zero, print out debugging information about type inference.

NUMBA_DISABLE_TYPEINFER_FAIL_CACHE

  If set to truthy value, disable the cache of failed function resolutions in the type inference. The default value is false.

  Disabling the cache is not recommended for normal use. The cache should only be disabled temporarily for debugging purposes. Relying on disabled cache behavior is not supported and could break in future releases.

NUMBA_ENABLE_SYS_MONITORING

  Controls support for Python’s `sys.monitoring` feature in Numba. Disabled (set to zero) by default. When enabled (set to non-zero), allows profiling tools that use `sys.monitoring` to work with Numba code. Currently tested with `cProfile`, other monitoring tools may work but are not guaranteed.

  Only available for Python 3.12 and above. Otherwise, it has no effect.

  It also has no effect on Python 3.14.4 and newer: dispatcher support for `sys.monitoring` is disabled, see this issue for more details (#10538). A `UserWarning` is emitted if this variable is set to a non-zero value.

NUMBA_ENABLE_PROFILING

  Enables JIT events of LLVM in order to support profiling of jitted functions. This option is automatically enabled under certain profilers.

NUMBA_TRACE

  If set to non-zero, trace certain function calls (function entry and exit events, including arguments and return values).

NUMBA_CHROME_TRACE

  If defined, chrome tracing is enabled and this variable specifies the filepath of the chrome tracing json file output. The emitted file can be opened by a Chromium-based browser using the profile viewer at chrome://tracing/.

  Warning

  This feature is not supported in multi-process applications.

NUMBA_DUMP_BYTECODE

  If set to non-zero, print out the Python bytecode of compiled functions.

NUMBA_DUMP_CFG

  If set to non-zero, print out information about the Control Flow Graph of compiled functions.

NUMBA_DUMP_IR

  If set to non-zero, print out the Numba Intermediate Representation of compiled functions.

NUMBA_DUMP_SSA

  If set to non-zero, print out the Numba Intermediate Representation of compiled functions after conversion to Static Single Assignment (SSA) form.

NUMBA_DEBUG_PRINT_AFTER

  Dump the Numba IR after declared pass(es). This is useful for debugging IR changes made by given passes. Accepted values are:

  - Any pass name (as given by the `.name()` method on the class)

  - Multiple pass names as a comma separated list, i.e. `"foo_pass,bar_pass"`

  - The token `"all"`, which will print after all passes.

  The default value is `"none"` so as to prevent output.

NUMBA_DUMP_ANNOTATION

  If set to non-zero, print out types annotations for compiled functions.

NUMBA_DUMP_LLVM

  Dump the unoptimized LLVM assembly source of compiled functions. Unoptimized code is usually very verbose; therefore, `NUMBA_DUMP_OPTIMIZED` is recommended instead.

NUMBA_DUMP_FUNC_OPT

  Dump the LLVM assembly source after the LLVM “function optimization” pass, but before the “module optimization” pass. This is useful mostly when developing Numba itself, otherwise use `NUMBA_DUMP_OPTIMIZED`.

NUMBA_DUMP_OPTIMIZED

  Dump the LLVM assembly source of compiled functions after all optimization passes. The output includes the raw function as well as its CPython-compatible wrapper (whose name begins with `wrapper.`). Note that the function is often inlined inside the wrapper, as well.

NUMBA_DEBUG_ARRAY_OPT

  Dump debugging information related to the processing associated with the `parallel=True` jit decorator option.

NUMBA_DEBUG_ARRAY_OPT_RUNTIME

  Dump debugging information related to the runtime scheduler associated with the `parallel=True` jit decorator option.

NUMBA_DEBUG_ARRAY_OPT_STATS

  Dump statistics about how many operators/calls are converted to parallel for-loops and how many are fused together, which are associated with the `parallel=True` jit decorator option.

NUMBA_PARALLEL_DIAGNOSTICS

  If set to an integer value between 1 and 4 (inclusive) diagnostic information about parallel transforms undertaken by Numba will be written to STDOUT. The higher the value set the more detailed the information produced.

NUMBA_DUMP_ASSEMBLY

  Dump the native assembly code of compiled functions.

NUMBA_LLVM_PASS_TIMINGS

  Set to `1` to enable recording of pass timings in LLVM; e.g. `NUMBA_LLVM_PASS_TIMINGS=1`. See Notes on timing LLVM.

  Default value: `0` (Off)

NUMBA_JIT_COVERAGE

  Set to `1` to enable coverage data reporting by the JIT compiler on compiled source lines. Default to `0` (Off).

See also

Troubleshooting and tips and Numba architecture.

## Compilation options

NUMBA_OPT

  The optimization level; typically this option is passed straight to LLVM. It may take one of the values `0`, `1`, `2` or `3` which correspond approximately to the `-O{value}` flag found in many command line compilation tools. The value `max` is also supported, this is Numba specific, it has the effect of running with the optimization level set at `3` both before and after a pass which in which reference count operation pruning takes place. In some cases this may increase performance, in other cases it may impede performance, the same can be said for compilation time. This option is present to give users the opportunity to choose a value suitable for their application.

  Default value: 3

NUMBA_LOOP_VECTORIZE

  If set to non-zero, enable LLVM loop vectorization.

  Default value: 1

NUMBA_SLP_VECTORIZE

  If set to non-zero, enable LLVM superword-level parallelism vectorization. Note that use of this feature has occasionally resulted in LLVM producing miscompilations, hence it is off by default.

  Default value: 0

NUMBA_ENABLE_AVX

  If set to non-zero, enable AVX optimizations in LLVM. This is disabled by default on Sandy Bridge and Ivy Bridge architectures as it can sometimes result in slower code on those platforms.

NUMBA_DISABLE_INTEL_SVML

  If set to non-zero and Intel SVML is available, the use of SVML will be disabled.

NUMBA_DISABLE_JIT

  Disable JIT compilation entirely. The `jit()` decorator acts as if it performs no operation, and the invocation of decorated functions calls the original Python function instead of a compiled version. This can be useful if you want to run the Python debugger over your code.

NUMBA_CPU_NAME

NUMBA_CPU_FEATURES

  Override CPU and CPU features detection. By setting `NUMBA_CPU_NAME=generic`, a generic CPU model is picked for the CPU architecture and the feature list (`NUMBA_CPU_FEATURES`) defaults to empty. CPU features must be listed with the format `+feature1,-feature2` where `+` indicates enable and `-` indicates disable. For example, `+sse,+sse2,-avx,-avx2` enables SSE and SSE2, and disables AVX and AVX2.

  These settings are passed to LLVM for configuring the compilation target. To get a list of available options, use the `llc` commandline tool from LLVM, for example:

      llc -march=x86 -mattr=help

  Tip

  To force all caching functions (`@jit(cache=True)`) to emit portable code (portable within the same architecture and OS), simply set `NUMBA_CPU_NAME=generic`.

NUMBA_FUNCTION_CACHE_SIZE

  Override the size of the function cache for retaining recently deserialized functions in memory. In systems like Dask, it is common for functions to be deserialized multiple times. Numba will cache functions as long as there is a reference somewhere in the interpreter. This cache size variable controls how many functions that are no longer referenced will also be retained, just in case they show up in the future. The implementation of this is not a true LRU, but the large size of the cache should be sufficient for most situations.

  Note: this is unrelated to the compilation cache.

  Default value: 128

NUMBA_LLVM_REFPRUNE_PASS

  Turns on the LLVM pass level reference-count pruning pass and disables the regex based implementation in Numba.

  Default value: 1 (On)

NUMBA_LLVM_REFPRUNE_FLAGS

  When `NUMBA_LLVM_REFPRUNE_PASS` is on, this allows configuration of subpasses in the reference-count pruning LLVM pass.

  Valid values are any combinations of the below separated by , (case-insensitive):

  - `all`: enable all subpasses.

  - `per_bb`: enable per-basic-block level pruning, which is same as the old regex based implementation.

  - `diamond`: enable inter-basic-block pruning that is a diamond shape pattern, i.e. a single-entry single-exit CFG subgraph where has an incref in the entry and a corresponding decref in the exit.

  - `fanout`: enable inter-basic-block pruning that has a fanout pattern, i.e. a single-entry multiple-exit CFG subgraph where the entry has an incref and every exit has a corresponding decref.

  - `fanout_raise`: same as `fanout` but allow subgraph exit nodes to be raising an exception and not have a corresponding decref.

  For example, `all` is the same as `per_bb, diamond, fanout, fanout_raise`

  Default value: “all”

NUMBA_USE_LLVMLITE_MEMORY_MANAGER

  Whether llvmlite’s built-in memory manager is enabled. The default is to enable it on 64-bit ARM platforms (macOS on Apple Silicon and Linux on AArch64), where it is needed to ensure ABI compliance, specifically conformance with the requirements for GOT and text segment placement in the large code model.

  This environment variable can be used to override the default setting and force it to be enabled (`1`) or disabled (`0`). This should not normally be required, but it is provided as an option for debugging and potential workaround situations.

  Default value: None (Use the default for the system)

## Caching options

Options for the compilation cache.

NUMBA_DEBUG_CACHE

  If set to non-zero, print out information about operation of the JIT compilation cache.

NUMBA_CACHE_DIR

  Override the location of the cache directory. If defined, this should be a valid directory path.

  If not defined, Numba picks the cache directory in the following order:

  1. In-tree cache. Put the cache next to the corresponding source file under a `__pycache__` directory following how `.pyc` files are stored.

  2. User-wide cache. Put the cache in the user’s application directory using `appdirs.user_cache_dir` from the Appdirs package.

  3. IPython cache. Put the cache in an IPython specific application directory. Stores are made under the `numba_cache` in the directory returned by `IPython.paths.get_ipython_cache_dir()`.

  Also see docs on cache sharing and docs on cache clearing

NUMBA_CACHE_LOCATOR_CLASSES

  Override the default cache locator classes and their order. If defined, this should be a comma-separated list of cache locator class names.

  Available locator classes include:

  - `InTreeCacheLocator` - Cache next to source files in `__pycache__`

  - `InTreeCacheLocatorFsAgnostic` - Like `InTreeCacheLocator` but agnostic to filesystem timestamp precision differences

  - `UserWideCacheLocator` - Cache in user-wide application directory

  - `IPythonCacheLocator` - Cache in IPython-specific directory

  - `ZipCacheLocator` - Cache for functions in zip files

  Custom locator classes can also be specified using their full module path (e.g., `mymodule.MyCustomLocator`).

  If not defined, Numba uses the default locator order.

## GPU support

NUMBA_DISABLE_CUDA

  If set to non-zero, disable CUDA support.

NUMBA_FORCE_CUDA_CC

  If set, force the CUDA compute capability to the given version (a string of the type `major.minor`), regardless of attached devices.

NUMBA_CUDA_DEFAULT_PTX_CC

  The default compute capability (a string of the type `major.minor`) to target when compiling to PTX using `cuda.compile_ptx`. The default is 5.0, which is the lowest non-deprecated compute capability in the most recent version of the CUDA toolkit supported (12.4 at present).

NUMBA_ENABLE_CUDASIM

  If set, don’t compile and execute code for the GPU, but use the CUDA Simulator instead. For debugging purposes.

NUMBA_CUDA_ARRAY_INTERFACE_SYNC

  Whether to synchronize on streams provided by objects imported using the CUDA Array Interface. This defaults to 1. If set to 0, then no synchronization takes place, and the user of Numba (and other CUDA libraries) is responsible for ensuring correctness with respect to synchronization on streams.

NUMBA_CUDA_LOG_LEVEL

  For debugging purposes. If no other logging is configured, the value of this variable is the logging level for CUDA API calls. The default value is `CRITICAL` - to trace all API calls on standard error, set this to `DEBUG`.

NUMBA_CUDA_LOG_API_ARGS

  By default the CUDA API call logs only give the names of functions called. Setting this variable to 1 also includes the values of arguments to Driver API calls in the logs.

NUMBA_CUDA_DRIVER

  Path of the directory in which the CUDA driver libraries are to be found. Normally this should not need to be set as Numba can locate the driver in standard locations. However, this variable can be used if the driver is in a non-standard location.

NUMBA_CUDA_LOG_SIZE

  Buffer size for logs produced by CUDA driver API operations. This defaults to 1024 and should not normally need to be modified - however, if an error in an API call produces a large amount of output that appears to be truncated (perhaps due to multiple long function names, for example) then this variable can be used to increase the buffer size and view the full error message.

NUMBA_CUDA_VERBOSE_JIT_LOG

  Whether the CUDA driver should produce verbose log messages. Defaults to 1, indicating that verbose messaging is enabled. This should not need to be modified under normal circumstances.

NUMBA_CUDA_PER_THREAD_DEFAULT_STREAM

  When set to 1, the default stream is the per-thread default stream. When set to 0, the default stream is the legacy default stream. This defaults to 0, for the legacy default stream. See Stream Synchronization Behavior for an explanation of the legacy and per-thread default streams.

  This variable only takes effect when using Numba’s internal CUDA bindings; when using the NVIDIA bindings, use the environment variable `CUDA_PYTHON_CUDA_PER_THREAD_DEFAULT_STREAM` instead.

  See also

  The Default Stream section in the NVIDIA Bindings documentation.

NUMBA_CUDA_LOW_OCCUPANCY_WARNINGS

  Enable warnings if the grid size is too small relative to the number of streaming multiprocessors (SM). This option is on by default (default value is 1).

  The heuristic checked is whether `gridsize < 2 * (number of SMs)`. NOTE: The absence of a warning does not imply a good gridsize relative to the number of SMs. Disabling this warning will reduce the number of CUDA API calls (during JIT compilation), as the heuristic needs to check the number of SMs available on the device in the current context.

NUMBA_CUDA_WARN_ON_IMPLICIT_COPY

  Enable warnings if a kernel is launched with host memory which forces a copy to and from the device. This option is on by default (default value is 1).

NUMBA_CUDA_USE_NVIDIA_BINDING

  When set to 1, Numba will attempt to use the NVIDIA CUDA Python binding to make calls to the driver API instead of using its own ctypes binding. This defaults to 0 (off), as the NVIDIA binding is currently missing support for Per-Thread Default Streams and the profiler APIs.

NUMBA_CUDA_INCLUDE_PATH

  The location of the CUDA include files. This is used when linking CUDA C/C++ sources to Python kernels, and needs to be correctly set for CUDA includes to be available to linked C/C++ sources. On Linux, it defaults to `/usr/local/cuda/include`. On Windows, the default is `$env:CUDA_PATH\include`.

## Threading Control

NUMBA_NUM_THREADS

  If set, the number of threads in the thread pool for the parallel CPU target will take this value. Must be greater than zero. This value is independent of `OMP_NUM_THREADS` and `MKL_NUM_THREADS`.

  Default value: The number of CPU cores on the system as determined at run time. This can be accessed via `numba.config.NUMBA_DEFAULT_NUM_THREADS`.

  See also the section on Setting the Number of Threads for information on how to set the number of threads at runtime.

NUMBA_THREADING_LAYER

  This environment variable controls the library used for concurrent execution for the CPU parallel targets (`@vectorize(target='parallel')`, `@guvectorize(target='parallel')` and `@njit(parallel=True)`). The variable type is string and by default is `default` which will select a threading layer based on what is available in the runtime. The valid values are (for more information about these see the threading layer documentation):

  - `default` - select a threading layer based on what is available in the current runtime.

  - `safe` - select a threading layer that is both fork and thread safe (requires the TBB package).

  - `forksafe` - select a threading layer that is fork safe.

  - `threadsafe` - select a threading layer that is thread safe.

  - `tbb` - A threading layer backed by Intel TBB.

  - `omp` - A threading layer backed by OpenMP.

  - `workqueue` - A simple built-in work-sharing task scheduler.

NUMBA_THREADING_LAYER_PRIORITY

  This environment variable controls the order in which the libraries used for concurrent execution, for the CPU parallel targets (`@vectorize(target='parallel')`, `@guvectorize(target='parallel')` and `@njit(parallel=True)`), are prioritized for use. The variable type is string and by default is `tbb omp workqueue`, with the priority taken based on position from the left of the string, left most being the highest. Valid values are any permutation of the three choices (for more information about these see the threading layer documentation.)