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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.
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.
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)¶
- Part of the Stable ABI.
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)¶
- Part of the Stable ABI.
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¶
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¶
- Part of the Stable ABI (as an opaque struct).
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.
-
PyInterpreterState *PyInterpreterState_Get(void)¶
- Part of the Stable ABI since version 3.9.
Get the current interpreter.
Issue a fatal error if there is no attached thread state.
It cannot return NULL.
-
int64_t PyInterpreterState_GetID(PyInterpreterState *interp)¶
- Part of the Stable ABI since 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.
-
PyObject *PyInterpreterState_GetDict(PyInterpreterState *interp)¶
- Return value: Borrowed reference. Part of the Stable ABI since 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.
-
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.
Changed in version 3.9: The function now takes a tstate parameter.
Changed in 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”.
-
void _PyInterpreterState_SetEvalFrameFunc(PyInterpreterState *interp, _PyFrameEvalFunction eval_frame)¶
Set the frame evaluation function.
See the PEP 523 “Adding a frame evaluation API to CPython”.
-
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.
-
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.
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.
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Last updated on Nov 12, 2026 (08:53 UTC).
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