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-
jax.debug.printandjax.debug.breakpoint -
The
checkifytransformation - JAX debugging flags
- GPU performance tips
- Persistent Compilation Cache
- Understanding Jaxprs
- External Callbacks in JAX
- Type promotion semantics
- Pytrees
- Ahead-of-time lowering and compilation
-
Exporting and serialization
- Exporting and serializing staged-out computations
- Shape polymorphism
- Interoperation with TensorFlow
- JAX Errors
- Transfer guard
-
Pallas: a JAX kernel language
- Pallas Quickstart
- Pallas Design
- Grids and BlockSpecs
-
Pallas TPU
- Writing TPU kernels with Pallas
- Pipelining
- Pallas Changelog
-
Advanced Tutorials
- Training a Simple Neural Network, with tensorflow/datasets Data Loading
- Training a Simple Neural Network, with PyTorch Data Loading
- Autobatching for Bayesian Inference
- Using JAX in multi-host and multi-process environments
- Distributed arrays and automatic parallelization
-
SPMD multi-device parallelism with
shard_map - Distributed data loading in a multi-host/multi-process environment
-
Named axes and easy-to-revise parallelism with
xmap - The Autodiff Cookbook
- Custom derivative rules for JAX-transformable Python functions
-
Control autodiff’s saved values with
jax.checkpoint(akajax.remat) - How JAX primitives work
- Writing custom Jaxpr interpreters in JAX
- Custom operations for GPUs with C++ and CUDA
- Generalized Convolutions in JAX
-
Developer Documentation
- Contributing to JAX
- Building from source
- Internal APIs
- Autodidax: JAX core from scratch
-
JAX Enhancement Proposals (JEPs)
- 263: JAX PRNG Design
- 2026: Custom JVP/VJP rules for JAX-transformable functions
- 4008: Custom VJP and `nondiff_argnums` update
- 4410: Omnistaging
- 9263: Typed keys & pluggable RNGs
- 9407: Design of Type Promotion Semantics for JAX
- 9419: Jax and Jaxlib versioning
- 10657: Sequencing side-effects in JAX
- 11830: `jax.remat` / `jax.checkpoint` new implementation
- 12049: Type Annotation Roadmap for JAX
- 14273: `shard_map` (`shmap`) for simple per-device code
- 15856: `jax.extend`, an extensions module
- 17111: Efficient transposition of `shard_map` (and other maps)
- 18137: Scope of JAX NumPy & SciPy Wrappers
- Investigating a regression
- Building on JAX
-
Notes
- API compatibility
- Python and NumPy version support policy
- jax.Array migration
- Asynchronous dispatch
- Concurrency
- GPU memory allocation
- Rank promotion warning
-
Public API: jax package
-
jax.numpymodule- jax.numpy.fft.fft
- jax.numpy.fft.fft2
- jax.numpy.fft.fftfreq
- jax.numpy.fft.fftn
- jax.numpy.fft.fftshift
- jax.numpy.fft.hfft
- jax.numpy.fft.ifft
- jax.numpy.fft.ifft2
- jax.numpy.fft.ifftn
- jax.numpy.fft.ifftshift
- jax.numpy.fft.ihfft
- jax.numpy.fft.irfft
- jax.numpy.fft.irfft2
- jax.numpy.fft.irfftn
- jax.numpy.fft.rfft
- jax.numpy.fft.rfft2
- jax.numpy.fft.rfftfreq
- jax.numpy.fft.rfftn
-
jax.scipymodule- jax.scipy.stats.bernoulli.logpmf
- jax.scipy.stats.bernoulli.pmf
- jax.scipy.stats.bernoulli.cdf
- jax.scipy.stats.bernoulli.ppf
-
jax.laxmodule -
jax.randommodule -
jax.shardingmodule -
jax.debugmodule -
jax.dlpackmodule -
jax.distributedmodule -
jax.dtypesmodule -
jax.flatten_utilmodule -
jax.imagemodule -
jax.nnmodule-
jax.nn.initializersmodule -
jax.opsmodule -
jax.profilermodule -
jax.stagesmodule -
jax.treemodule -
jax.tree_utilmodule -
jax.typingmodule -
jax.exportmodule -
jax.extendmodule-
jax.extend.ffimodule -
jax.extend.linear_utilmodule -
jax.extend.mlirmodule -
jax.extend.randommodule -
jax.example_librariesmodule-
jax.example_libraries.optimizersmodule -
jax.example_libraries.staxmodule -
jax.experimentalmodule-
jax.experimental.array_apimodule -
jax.experimental.checkifymodule -
jax.experimental.host_callbackmodule -
jax.experimental.mapsmodule -
jax.experimental.pjitmodule -
jax.experimental.sparsemodule- jax.experimental.sparse.BCOO
- jax.experimental.sparse.bcoo_broadcast_in_dim
- jax.experimental.sparse.bcoo_concatenate
- jax.experimental.sparse.bcoo_dot_general
- jax.experimental.sparse.bcoo_dot_general_sampled
- jax.experimental.sparse.bcoo_dynamic_slice
- jax.experimental.sparse.bcoo_extract
- jax.experimental.sparse.bcoo_fromdense
- jax.experimental.sparse.bcoo_gather
- jax.experimental.sparse.bcoo_multiply_dense
- jax.experimental.sparse.bcoo_multiply_sparse
- jax.experimental.sparse.bcoo_update_layout
- jax.experimental.sparse.bcoo_reduce_sum
- jax.experimental.sparse.bcoo_reshape
- jax.experimental.sparse.bcoo_slice
- jax.experimental.sparse.bcoo_sort_indices
- jax.experimental.sparse.bcoo_squeeze
- jax.experimental.sparse.bcoo_sum_duplicates
- jax.experimental.sparse.bcoo_todense
- jax.experimental.sparse.bcoo_transpose
-
jax.experimental.jetmodule -
jax.experimental.custom_partitioningmodule -
jax.experimental.multihost_utilsmodule -
jax.experimental.compilation_cachemodule -
jax.experimental.key_reusemodule -
jax.experimental.mesh_utilsmodule -
jax.experimental.serialize_executablemodule -
jax.experimental.shard_mapmodule -
jax.experimental.pallasmodule -
jax.libmodule
jax.scipy.special.
gamma
(
x
)
[source]
#
-
The gamma function.
JAX implementation of
scipy.special.gamma.The gamma function is defined for \(\Re(z)>0\) as
\[\mathrm{gamma}(z) = \Gamma(z) = \int_0^\infty t^{z-1}e^{-t}\mathrm{d}t\]and is extended by analytic continuation to arbitrary complex values z . For positive integers n , the gamma function is related to the
\[\Gamma(n) = (n - 1)!\]factorial()function via the following identity: - Parameters :
-
x ( ArrayLike ) – arraylike, real valued.
- Returns :
-
array containing the values of the gamma function
- Return type :
-
See also
jax.scipy.special.factorial(): the factorial function.jax.scipy.special.gammaln(): the natural log of the gamma functionjax.scipy.special.gammasgn(): the sign of the gamma functionNotes
Unlike the scipy version, JAX’s
gammadoes not support complex-valued inputs.
-
-
-
-
-
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