2.5. Environment variables

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

2.5.1. Errors and warnings display

NUMBA_WARNINGS

If set to non-zero, printout of Numba warnings is enabled, otherwise the warnings are suppressed. The warnings can give insight into the compilation process.

2.5.2. 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 not set or set to zero, show user level help information. Defaults to the negation of the value set by NUMBA_DEVELOPER_MODE.

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_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_DEBUGINFO

if sets 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_DEBUG_TYPEINFER

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

NUMBA_DEBUG_CACHE

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

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_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_ANNOTATION

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

NUMBA_DUMP_LLVM

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

NUMBA_DUMP_FUNC_OPT

Dump the LLVM assembler 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 assembler 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_DUMP_ASSEMBLY

Dump the native assembler code of compiled functions.

2.5.3. Compilation options

NUMBA_OPT

The optimization level; this option is passed straight to LLVM.

Default value: 3

NUMBA_LOOP_VECTORIZE

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

Default value: 1 (except on 32-bit Windows)

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_COMPATIBILITY_MODE

If set to non-zero, compilation of JIT functions will never entirely fail, but instead generate a fallback that simply interprets the function. This is only to be used if you are migrating a large codebase from an old Numba version (before 0.12), and want to avoid breaking everything at once. Otherwise, please don’t use this.

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 and 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.

Default value: 128

2.5.4. 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_ENABLE_CUDASIM

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

2.5.5. 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.

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.