Multiple interpreters in a Python process
*****************************************

While in most uses, you will only embed a single Python interpreter,
there are cases where you need to create several independent
interpreters in the same process and perhaps even in the same thread.
Sub-interpreters allow you to do that.

The "main" interpreter is the first one created when the runtime
initializes. It is usually the only Python interpreter in a process.
Unlike sub-interpreters, the main interpreter has unique process-
global responsibilities like signal handling.  It is also responsible
for execution during runtime initialization and is usually the active
interpreter during runtime finalization.  The
"PyInterpreterState_Main()" function returns a pointer to its state.

You can switch between sub-interpreters using the
"PyThreadState_Swap()" function. You can create and destroy them using
the following functions:

type PyInterpreterConfig

   Structure containing most parameters to configure a sub-
   interpreter. Its values are used only in
   "Py_NewInterpreterFromConfig()" and never modified by the runtime.

   Ajouté dans la version 3.12.

   Structure fields:

   int use_main_obmalloc

      If this is "0" then the sub-interpreter will use its own
      "object" allocator state. Otherwise it will use (share) the main
      interpreter's.

      If this is "0" then "check_multi_interp_extensions" must be "1"
      (non-zero). If this is "1" then "gil" must not be
      "PyInterpreterConfig_OWN_GIL".

   int allow_fork

      If this is "0" then the runtime will not support forking the
      process in any thread where the sub-interpreter is currently
      active. Otherwise fork is unrestricted.

      Note that the "subprocess" module still works when fork is
      disallowed.

   int allow_exec

      If this is "0" then the runtime will not support replacing the
      current process via exec (e.g. "os.execv()") in any thread where
      the sub-interpreter is currently active. Otherwise exec is
      unrestricted.

      Note that the "subprocess" module still works when exec is
      disallowed.

   int allow_threads

      If this is "0" then the sub-interpreter's "threading" module
      won't create threads. Otherwise threads are allowed.

   int allow_daemon_threads

      If this is "0" then the sub-interpreter's "threading" module
      won't create daemon threads. Otherwise daemon threads are
      allowed (as long as "allow_threads" is non-zero).

   int check_multi_interp_extensions

      If this is "0" then all extension modules may be imported,
      including legacy (single-phase init) modules, in any thread
      where the sub-interpreter is currently active. Otherwise only
      multi-phase init extension modules (see **PEP 489**) may be
      imported. (Also see "Py_mod_multiple_interpreters".)

      This must be "1" (non-zero) if "use_main_obmalloc" is "0".

   int gil

      This determines the operation of the GIL for the sub-
      interpreter. It may be one of the following:

      PyInterpreterConfig_DEFAULT_GIL

         Use the default selection ("PyInterpreterConfig_SHARED_GIL").

      PyInterpreterConfig_SHARED_GIL

         Use (share) the main interpreter's GIL.

      PyInterpreterConfig_OWN_GIL

         Use the sub-interpreter's own GIL.

      If this is "PyInterpreterConfig_OWN_GIL" then
      "PyInterpreterConfig.use_main_obmalloc" must be "0".

PyStatus Py_NewInterpreterFromConfig(PyThreadState **tstate_p, const PyInterpreterConfig *config)

   Create a new sub-interpreter.  This is an (almost) totally separate
   environment for the execution of Python code.  In particular, the
   new interpreter has separate, independent versions of all imported
   modules, including the fundamental modules "builtins", "__main__"
   and "sys".  The table of loaded modules ("sys.modules") and the
   module search path ("sys.path") are also separate.  The new
   environment has no "sys.argv" variable.  It has new standard I/O
   stream file objects "sys.stdin", "sys.stdout" and "sys.stderr"
   (however these refer to the same underlying file descriptors).

   The given *config* controls the options with which the interpreter
   is initialized.

   Upon success, *tstate_p* will be set to the first *thread state*
   created in the new sub-interpreter.  This thread state is
   *attached*. Note that no actual thread is created; see the
   discussion of thread states below.  If creation of the new
   interpreter is unsuccessful, *tstate_p* is set to "NULL"; no
   exception is set since the exception state is stored in the
   *attached thread state*, which might not exist.

   Like all other Python/C API functions, an *attached thread state*
   must be present before calling this function, but it might be
   detached upon returning. On success, the returned thread state will
   be *attached*. If the sub-interpreter is created with its own *GIL*
   then the *attached thread state* of the calling interpreter will be
   detached. When the function returns, the new interpreter's *thread
   state* will be *attached* to the current thread and the previous
   interpreter's *attached thread state* will remain detached.

   Ajouté dans la version 3.12.

   Sub-interpreters are most effective when isolated from each other,
   with certain functionality restricted:

      PyInterpreterConfig config = {
          .use_main_obmalloc = 0,
          .allow_fork = 0,
          .allow_exec = 0,
          .allow_threads = 1,
          .allow_daemon_threads = 0,
          .check_multi_interp_extensions = 1,
          .gil = PyInterpreterConfig_OWN_GIL,
      };
      PyThreadState *tstate = NULL;
      PyStatus status = Py_NewInterpreterFromConfig(&tstate, &config);
      if (PyStatus_Exception(status)) {
          Py_ExitStatusException(status);
      }

   Note that the config is used only briefly and does not get
   modified. During initialization the config's values are converted
   into various "PyInterpreterState" values.  A read-only copy of the
   config may be stored internally on the "PyInterpreterState".

   Extension modules are shared between (sub-)interpreters as follows:

   * For modules using multi-phase initialization, e.g.
     "PyModule_FromDefAndSpec()", a separate module object is created
     and initialized for each interpreter. Only C-level static and
     global variables are shared between these module objects.

   * For modules using legacy single-phase initialization, e.g.
     "PyModule_Create()", the first time a particular extension is
     imported, it is initialized normally, and a (shallow) copy of its
     module's dictionary is squirreled away. When the same extension
     is imported by another (sub-)interpreter, a new module is
     initialized and filled with the contents of this copy; the
     extension's "init" function is not called. Objects in the
     module's dictionary thus end up shared across (sub-)interpreters,
     which might cause unwanted behavior (see Bugs and caveats below).

     Note that this is different from what happens when an extension
     is imported after the interpreter has been completely re-
     initialized by calling "Py_FinalizeEx()" and "Py_Initialize()";
     in that case, the extension's "initmodule" function *is* called
     again. As with multi-phase initialization, this means that only
     C-level static and global variables are shared between these
     modules.

PyThreadState *Py_NewInterpreter(void)
    * Fait partie de l' ABI stable.*

   Create a new sub-interpreter.  This is essentially just a wrapper
   around "Py_NewInterpreterFromConfig()" with a config that preserves
   the existing behavior.  The result is an unisolated sub-interpreter
   that shares the main interpreter's GIL, allows fork/exec, allows
   daemon threads, and allows single-phase init modules.

void Py_EndInterpreter(PyThreadState *tstate)
    * Fait partie de l' ABI stable.*

   Destroy the (sub-)interpreter represented by the given *thread
   state*. The given thread state must be *attached*. When the call
   returns, there will be no *attached thread state*. All thread
   states associated with this interpreter are destroyed.

   "Py_FinalizeEx()" will destroy all sub-interpreters that haven't
   been explicitly destroyed at that point.


A per-interpreter GIL
=====================

Ajouté dans la version 3.12.

Using "Py_NewInterpreterFromConfig()" you can create a sub-interpreter
that is completely isolated from other interpreters, including having
its own GIL.  The most important benefit of this isolation is that
such an interpreter can execute Python code without being blocked by
other interpreters or blocking any others.  Thus a single Python
process can truly take advantage of multiple CPU cores when running
Python code.  The isolation also encourages a different approach to
concurrency than that of just using threads. (See **PEP 554** and
**PEP 684**.)

Using an isolated interpreter requires vigilance in preserving that
isolation.  That especially means not sharing any objects or mutable
state without guarantees about thread-safety.  Even objects that are
otherwise immutable (e.g. "None", "(1, 5)") can't normally be shared
because of the refcount.  One simple but less-efficient approach
around this is to use a global lock around all use of some state (or
object). Alternately, effectively immutable objects (like integers or
strings) can be made safe in spite of their refcounts by making them
*immortal*. In fact, this has been done for the builtin singletons,
small integers, and a number of other builtin objects.

If you preserve isolation then you will have access to proper multi-
core computing without the complications that come with free-
threading. Failure to preserve isolation will expose you to the full
consequences of free-threading, including races and hard-to-debug
crashes.

Aside from that, one of the main challenges of using multiple isolated
interpreters is how to communicate between them safely (not break
isolation) and efficiently.  The runtime and stdlib do not provide any
standard approach to this yet.  A future stdlib module would help
mitigate the effort of preserving isolation and expose effective tools
for communicating (and sharing) data between interpreters.


Bugs and caveats
================

Because sub-interpreters (and the main interpreter) are part of the
same process, the insulation between them isn't perfect --- for
example, using low-level file operations like "os.close()" they can
(accidentally or maliciously) affect each other's open files.  Because
of the way extensions are shared between (sub-)interpreters, some
extensions may not work properly; this is especially likely when using
single-phase initialization or (static) global variables. It is
possible to insert objects created in one sub-interpreter into a
namespace of another (sub-)interpreter; this should be avoided if
possible.

Special care should be taken to avoid sharing user-defined functions,
methods, instances or classes between sub-interpreters, since import
operations executed by such objects may affect the wrong
(sub-)interpreter's dictionary of loaded modules. It is equally
important to avoid sharing objects from which the above are reachable.

Also note that combining this functionality with "PyGILState_*" APIs
is delicate, because these APIs assume a bijection between Python
thread states and OS-level threads, an assumption broken by the
presence of sub-interpreters. It is highly recommended that you don't
switch sub-interpreters between a pair of matching
"PyGILState_Ensure()" and "PyGILState_Release()" calls. Furthermore,
extensions (such as "ctypes") using these APIs to allow calling of
Python code from non-Python created threads will probably be broken
when using sub-interpreters.


High-level APIs
===============

type PyInterpreterState
    * Fait partie de l' ABI stable (sous forme d'une *struct*
   opaque).*

   This data structure represents the state shared by a number of
   cooperating threads.  Threads belonging to the same interpreter
   share their module administration and a few other internal items.
   There are no public members in this structure.

   Threads belonging to different interpreters initially share
   nothing, except process state like available memory, open file
   descriptors and such.  The global interpreter lock is also shared
   by all threads, regardless of to which interpreter they belong.

   Modifié dans la version 3.12: **PEP 684** introduced the
   possibility of a per-interpreter GIL. See
   "Py_NewInterpreterFromConfig()".

PyInterpreterState *PyInterpreterState_Get(void)
    * Fait partie de l' ABI stable depuis la version 3.9.*

   Get the current interpreter.

   Issue a fatal error if there is no *attached thread state*. It
   cannot return NULL.

   Ajouté dans la version 3.9.

int64_t PyInterpreterState_GetID(PyInterpreterState *interp)
    * Fait partie de l' ABI stable depuis la version 3.7.*

   Return the interpreter's unique ID.  If there was any error in
   doing so then "-1" is returned and an error is set.

   The caller must have an *attached thread state*.

   Ajouté dans la version 3.7.

PyObject *PyInterpreterState_GetDict(PyInterpreterState *interp)
    *Valeur de retour : référence empruntée.** Fait partie de l' ABI
   stable depuis la version 3.8.*

   Return a dictionary in which interpreter-specific data may be
   stored. If this function returns "NULL" then no exception has been
   raised and the caller should assume no interpreter-specific dict is
   available.

   This is not a replacement for "PyModule_GetState()", which
   extensions should use to store interpreter-specific state
   information.

   The returned dictionary is borrowed from the interpreter and is
   valid until interpreter shutdown.

   Ajouté dans la version 3.8.

typedef PyObject *(*_PyFrameEvalFunction)(PyThreadState *tstate, _PyInterpreterFrame *frame, int throwflag)

   Type of a frame evaluation function.

   The *throwflag* parameter is used by the "throw()" method of
   generators: if non-zero, handle the current exception.

   Modifié dans la version 3.9: The function now takes a *tstate*
   parameter.

   Modifié dans la version 3.11: The *frame* parameter changed from
   "PyFrameObject*" to "_PyInterpreterFrame*".

_PyFrameEvalFunction _PyInterpreterState_GetEvalFrameFunc(PyInterpreterState *interp)

   Get the frame evaluation function.

   See the **PEP 523** "Adding a frame evaluation API to CPython".

   Ajouté dans la version 3.9.

void _PyInterpreterState_SetEvalFrameFunc(PyInterpreterState *interp, _PyFrameEvalFunction eval_frame)

   Set the frame evaluation function.

   See the **PEP 523** "Adding a frame evaluation API to CPython".

   Ajouté dans la version 3.9.

void _PyInterpreterState_SetEvalFrameAllowSpecialization(PyInterpreterState *interp, int allow_specialization)

   Enables or disables specialization why a custom frame evaluator is
   in place.

   If *allow_specialization* is non-zero, the adaptive specializer
   will continue to specialize bytecodes even though a custom eval
   frame function is set. When *allow_specialization* is zero, setting
   a custom eval frame disables specialization. The standard
   interpreter loop will continue to deopt while a frame evaluation
   API is in place - the frame evaluation function needs to handle the
   specialized opcodes to take advantage of this.

   Ajouté dans la version 3.15.

int _PyInterpreterState_IsSpecializationEnabled(PyInterpreterState *interp)

   Return non-zero if adaptive specialization is enabled for the
   interpreter. Specialization is enabled when no custom eval frame
   function is set, or when one is set with *allow_specialization*
   enabled.

   Ajouté dans la version 3.15.


Low-level APIs
==============

All of the following functions must be called after "Py_Initialize()".

Modifié dans la version 3.7: "Py_Initialize()" now initializes the
*GIL* and sets an *attached thread state*.

PyInterpreterState *PyInterpreterState_New()
    * Fait partie de l' ABI stable.*

   Create a new interpreter state object.  An *attached thread state*
   is not needed, but may optionally exist if it is necessary to
   serialize calls to this function.

   Raises an auditing event "cpython.PyInterpreterState_New" with no
   arguments.

void PyInterpreterState_Clear(PyInterpreterState *interp)
    * Fait partie de l' ABI stable.*

   Reset all information in an interpreter state object.  There must
   be an *attached thread state* for the interpreter.

   Raises an auditing event "cpython.PyInterpreterState_Clear" with no
   arguments.

void PyInterpreterState_Delete(PyInterpreterState *interp)
    * Fait partie de l' ABI stable.*

   Destroy an interpreter state object.  There **should not** be an
   *attached thread state* for the target interpreter. The interpreter
   state must have been reset with a previous call to
   "PyInterpreterState_Clear()".


Advanced debugger support
=========================

These functions are only intended to be used by advanced debugging
tools.

PyInterpreterState *PyInterpreterState_Head()

   Return the interpreter state object at the head of the list of all
   such objects.

PyInterpreterState *PyInterpreterState_Main()

   Return the main interpreter state object.

PyInterpreterState *PyInterpreterState_Next(PyInterpreterState *interp)

   Return the next interpreter state object after *interp* from the
   list of all such objects.

PyThreadState *PyInterpreterState_ThreadHead(PyInterpreterState *interp)

   Return the pointer to the first "PyThreadState" object in the list
   of threads associated with the interpreter *interp*.

PyThreadState *PyThreadState_Next(PyThreadState *tstate)

   Return the next thread state object after *tstate* from the list of
   all such objects belonging to the same "PyInterpreterState" object.
