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PEP 554 -- Multiple Interpreters in the Stdlib

Title:Multiple Interpreters in the Stdlib
Author:Eric Snow <ericsnowcurrently at>
Type:Standards Track
Post-History:07-Sep-2017, 08-Sep-2017, 13-Sep-2017, 05-Dec-2017, 09-May-2018


CPython has supported multiple interpreters in the same process (AKA "subinterpreters") since version 1.5 (1997). The feature has been available via the C-API. [c-api] Subinterpreters operate in relative isolation from one another, which provides the basis for an alternative concurrency model.

This proposal introduces the stdlib interpreters module. The module will be provisional. It exposes the basic functionality of subinterpreters already provided by the C-API, along with new (basic) functionality for sharing data between interpreters.


The interpreters module will be added to the stdlib. It will provide a high-level interface to subinterpreters and wrap a new low-level _interpreters (in the same way as the threading module). See the Examples section for concrete usage and use cases.

Along with exposing the existing (in CPython) subinterpreter support, the module will also provide a mechanism for sharing data between interpreters. This mechanism centers around "channels", which are similar to queues and pipes.

Note that objects are not shared between interpreters since they are tied to the interpreter in which they were created. Instead, the objects' data is passed between interpreters. See the Shared data section for more details about sharing between interpreters.

At first only the following types will be supported for sharing:

  • None
  • bytes
  • str
  • int
  • PEP 3118 buffer objects (via send_buffer())

Support for other basic types (e.g. bool, float, Ellipsis) will be added later.

API summary for interpreters module

Here is a summary of the API for the interpreters module. For a more in-depth explanation of the proposed classes and functions, see the "interpreters" Module API section below.

For creating and using interpreters:

signature description
list_all() -> [Intepreter] Get all existing interpreters.
get_current() -> Interpreter Get the currently running interpreter.
create() -> Interpreter Initialize a new (idle) Python interpreter.

signature description
class Interpreter(id) A single interpreter.
.id The interpreter's ID (read-only).
.is_running() -> bool Is the interpreter currently executing code?
.destroy() Finalize and destroy the interpreter.
.run(src_str, /, *,
Run the given source code in the interpreter.
(This blocks the current thread until done.)

exception base description
RunFailedError RuntimeError resulted in an uncaught exception.

For sharing data between interpreters:

signature description
is_shareable(obj) -> Bool
Can the object's data be shared
between interpreters?
create_channel() ->
(RecvChannel, SendChannel)
Create a new channel for passing
data between interpreters.
list_all_channels() ->
[(RecvChannel, SendChannel)]
Get all open channels.

signature description
class RecvChannel(id) The receiving end of a channel.
.id The channel's unique ID.
.interpreters The list of associated interpreters.
.recv() -> object
Get the next object from the channel,
and wait if none have been sent.
Associate the interpreter with the channel.
.recv_nowait(default=None) ->
Like recv(), but return the default
instead of waiting.
No longer associate the current interpreter
with the channel (on the receiving end).
Close the channel in all interpreters.

signature description
class SendChannel(id) The sending end of a channel.
.id The channel's unique ID.
.interpreters The list of associated interpreters.
Send the object (i.e. its data) to the
receiving end of the channel and wait.
Associate the interpreter with the channel.
Like send(), but Fail if not received.
Send the object's (PEP 3118) buffer to the
receiving end of the channel and wait.
Associate the interpreter with the channel.
Like send_buffer(), but fail if not received.
No longer associate the current interpreter
with the channel (on the sending end).
Close the channel in all interpreters.

exception base description
ChannelError Exception The base class for channel-related exceptions.
ChannelNotFoundError ChannelError The identified channel was not found.
ChannelEmptyError ChannelError The channel was unexpectedly empty.
ChannelNotEmptyError ChannelError The channel was unexpectedly not empty.
NotReceivedError ChannelError Nothing was waiting to receive a sent object.
ChannelClosedError ChannelError The channel is closed.
ChannelReleasedError ChannelClosedError The channel is released (but not yet closed).


Run isolated code

interp = interpreters.create()

Run in a thread

interp = interpreters.create()
def run():'print("during")')
t = threading.Thread(target=run)

Pre-populate an interpreter

interp = interpreters.create()"""
    import some_lib
    import an_expensive_module

Handling an exception

interp = interpreters.create()
        raise KeyError
except interpreters.RunFailedError as exc:
    print(f"got the error from the subinterpreter: {exc}")

Synchronize using a channel

interp = interpreters.create()
r, s = interpreters.create_channel()
def run():"""
t = threading.Thread(target=run)

Sharing a file descriptor

interp = interpreters.create()
r1, s1 = interpreters.create_channel()
r2, s2 = interpreters.create_channel()
def run():"""
        fd = int.from_bytes(
                reader.recv(), 'big')
        for line in os.fdopen(fd):
t = threading.Thread(target=run)
with open('spamspamspam') as infile:
    fd = infile.fileno().to_bytes(1, 'big')

Passing objects via marshal

interp = interpreters.create()
r, s = interpreters.create_fifo()"""
    import marshal
def run():"""
        data = reader.recv()
        while data:
            obj = marshal.loads(data)
            data = reader.recv()
t = threading.Thread(target=run)
for obj in input:
    data = marshal.dumps(obj)

Passing objects via pickle

interp = interpreters.create()
r, s = interpreters.create_channel()"""
    import pickle
def run():"""
        data = reader.recv()
        while data:
            obj = pickle.loads(data)
            data = reader.recv()
t = threading.Thread(target=run)
for obj in input:
    data = pickle.dumps(obj)

Running a module

interp = interpreters.create()
main_module = mod_name'import runpy; runpy.run_module({main_module!r})')

Running as script (including zip archives & directories)

interp = interpreters.create()
main_script = path_name"import runpy; runpy.run_path({main_script!r})")

Running in a thread pool executor

interps = [interpreters.create() for i in range(5)]
with concurrent.futures.ThreadPoolExecutor(max_workers=len(interps)) as pool:
    for interp in interps:
        pool.submit(, 'print("starting"); print("stopping")'


Running code in multiple interpreters provides a useful level of isolation within the same process. This can be leveraged in a number of ways. Furthermore, subinterpreters provide a well-defined framework in which such isolation may extended.

Nick Coghlan explained some of the benefits through a comparison with multi-processing [benefits]:

[I] expect that communicating between subinterpreters is going
to end up looking an awful lot like communicating between
subprocesses via shared memory.

The trade-off between the two models will then be that one still
just looks like a single process from the point of view of the
outside world, and hence doesn't place any extra demands on the
underlying OS beyond those required to run CPython with a single
interpreter, while the other gives much stricter isolation
(including isolating C globals in extension modules), but also
demands much more from the OS when it comes to its IPC

The security risk profiles of the two approaches will also be quite
different, since using subinterpreters won't require deliberately
poking holes in the process isolation that operating systems give
you by default.

CPython has supported subinterpreters, with increasing levels of support, since version 1.5. While the feature has the potential to be a powerful tool, subinterpreters have suffered from neglect because they are not available directly from Python. Exposing the existing functionality in the stdlib will help reverse the situation.

This proposal is focused on enabling the fundamental capability of multiple isolated interpreters in the same Python process. This is a new area for Python so there is relative uncertainly about the best tools to provide as companions to subinterpreters. Thus we minimize the functionality we add in the proposal as much as possible.


  • "subinterpreters are not worth the trouble"

Some have argued that subinterpreters do not add sufficient benefit to justify making them an official part of Python. Adding features to the language (or stdlib) has a cost in increasing the size of the language. So an addition must pay for itself. In this case, subinterpreters provide a novel concurrency model focused on isolated threads of execution. Furthermore, they provide an opportunity for changes in CPython that will allow simulateous use of multiple CPU cores (currently prevented by the GIL).

Alternatives to subinterpreters include threading, async, and multiprocessing. Threading is limited by the GIL and async isn't the right solution for every problem (nor for every person). Multiprocessing is likewise valuable in some but not all situations. Direct IPC (rather than via the multiprocessing module) provides similar benefits but with the same caveat.

Notably, subinterpreters are not intended as a replacement for any of the above. Certainly they overlap in some areas, but the benefits of subinterpreters include isolation and (potentially) performance. In particular, subinterpreters provide a direct route to an alternate concurrency model (e.g. CSP) which has found success elsewhere and will appeal to some Python users. That is the core value that the interpreters module will provide.

  • "stdlib support for subinterpreters adds extra burden on C extension authors"

In the Interpreter Isolation section below we identify ways in which isolation in CPython's subinterpreters is incomplete. Most notable is extension modules that use C globals to store internal state. PEP 3121 and PEP 489 provide a solution for most of the problem, but one still remains. [petr-c-ext] Until that is resolved, C extension authors will face extra difficulty to support subinterpreters.

Consequently, projects that publish extension modules may face an increased maintenance burden as their users start using subinterpreters, where their modules may break. This situation is limited to modules that use C globals (or use libraries that use C globals) to store internal state. For numpy, the reported-bug rate is one every 6 months. [bug-rate]

Ultimately this comes down to a question of how often it will be a problem in practice: how many projects would be affected, how often their users will be affected, what the additional maintenance burden will be for projects, and what the overall benefit of subinterpreters is to offset those costs. The position of this PEP is that the actual extra maintenance burden will be small and well below the threshold at which subinterpreters are worth it.

About Subinterpreters


Concurrency is a challenging area of software development. Decades of research and practice have led to a wide variety of concurrency models, each with different goals. Most center on correctness and usability.

One class of concurrency models focuses on isolated threads of execution that interoperate through some message passing scheme. A notable example is Communicating Sequential Processes (CSP), upon which Go's concurrency is based. The isolation inherent to subinterpreters makes them well-suited to this approach.

Shared data

Subinterpreters are inherently isolated (with caveats explained below), in contrast to threads. So the same communicate-via-shared-memory approach doesn't work. Without an alternative, effective use of concurrency via subinterpreters is significantly limited.

The key challenge here is that sharing objects between interpreters faces complexity due to various constraints on object ownership, visibility, and mutability. At a conceptual level it's easier to reason about concurrency when objects only exist in one interpreter at a time. At a technical level, CPython's current memory model limits how Python objects may be shared safely between interpreters; effectively objects are bound to the interpreter in which they were created. Furthermore the complexity of object sharing increases as subinterpreters become more isolated, e.g. after GIL removal.

Consequently,the mechanism for sharing needs to be carefully considered. There are a number of valid solutions, several of which may be appropriate to support in Python. This proposal provides a single basic solution: "channels". Ultimately, any other solution will look similar to the proposed one, which will set the precedent. Note that the implementation of can be done in a way that allows for multiple solutions to coexist, but doing so is not technically a part of the proposal here.

Regarding the proposed solution, "channels", it is a basic, opt-in data sharing mechanism that draws inspiration from pipes, queues, and CSP's channels. [fifo]

As simply described earlier by the API summary, channels have two operations: send and receive. A key characteristic of those operations is that channels transmit data derived from Python objects rather than the objects themselves. When objects are sent, their data is extracted. When the "object" is received in the other interpreter, the data is converted back into an object.

To make this work, the mutable shared state will be managed by the Python runtime, not by any of the interpreters. Initially we will support only one type of objects for shared state: the channels provided by create_channel(). Channels, in turn, will carefully manage passing objects between interpreters.

This approach, including keeping the API minimal, helps us avoid further exposing any underlying complexity to Python users. Along those same lines, we will initially restrict the types that may be passed through channels to the following:

  • None
  • bytes
  • str
  • int
  • PEP 3118 buffer objects (via send_buffer())

Limiting the initial shareable types is a practical matter, reducing the potential complexity of the initial implementation. There are a number of strategies we may pursue in the future to expand supported objects and object sharing strategies.

Interpreter Isolation

CPython's interpreters are intended to be strictly isolated from each other. Each interpreter has its own copy of all modules, classes, functions, and variables. The same applies to state in C, including in extension modules. The CPython C-API docs explain more. [caveats]

However, there are ways in which interpreters share some state. First of all, some process-global state remains shared:

  • file descriptors
  • builtin types (e.g. dict, bytes)
  • singletons (e.g. None)
  • underlying static module data (e.g. functions) for builtin/extension/frozen modules

There are no plans to change this.

Second, some isolation is faulty due to bugs or implementations that did not take subinterpreters into account. This includes things like extension modules that rely on C globals. [cryptography] In these cases bugs should be opened (some are already):

Finally, some potential isolation is missing due to the current design of CPython. Improvements are currently going on to address gaps in this area:

  • interpreters share the GIL
  • interpreters share memory management (e.g. allocators, gc)
  • GC is not run per-interpreter [global-gc]
  • at-exit handlers are not run per-interpreter [global-atexit]
  • extensions using the PyGILState_* API are incompatible [gilstate]

Existing Usage

Subinterpreters are not a widely used feature. In fact, the only documented cases of wide-spread usage are mod_wsgi, OpenStack Ceph, and JEP. On the one hand, these cases provide confidence that existing subinterpreter support is relatively stable. On the other hand, there isn't much of a sample size from which to judge the utility of the feature.

Provisional Status

The new interpreters module will be added with "provisional" status (see PEP 411). This allows Python users to experiment with the feature and provide feedback while still allowing us to adjust to that feedback. The module will be provisional in Python 3.8 and we will make a decision before the 3.9 release whether to keep it provisional, graduate it, or remove it.

Alternate Python Implementations

I'll be soliciting feedback from the different Python implementors about subinterpreter support.

Multiple-interpter support in the major Python implementations:


  • jython: yes [jython]
  • ironpython: yes?
  • pypy: maybe not? [pypy]
  • micropython: ???

"interpreters" Module API

The module provides the following functions:


Return a list of all existing interpreters.


Return the currently running interpreter.


Initialize a new Python interpreter and return it.  The
interpreter will be created in the current thread and will remain
idle until something is run in it.  The interpreter may be used
in any thread and will run in whichever thread calls

The module also provides the following class:



   The interpreter's ID (read-only).


   Return whether or not the interpreter is currently executing code.
   Calling this on the current interpreter will always return True.


   Finalize and destroy the interpreter.

   This may not be called on an already running interpreter.  Doing
   so results in a RuntimeError.

run(source_str, /, *, channels=None):

   Run the provided Python source code in the interpreter.  If the
   "channels" keyword argument is provided (and is a mapping of
   attribute names to channels) then it is added to the interpreter's
   execution namespace (the interpreter's "__main__" module).  If any
   of the values are not are not RecvChannel or SendChannel instances
   then ValueError gets raised.

   This may not be called on an already running interpreter.  Doing
   so results in a RuntimeError.

   A "run()" call is similar to a function call.  Once it completes,
   the code that called "run()" continues executing (in the original
   interpreter).  Likewise, if there is any uncaught exception then
   it effectively (see below) propagates into the code where
   ``run()`` was called.  However, unlike function calls (but like
   threads), there is no return value.  If any value is needed, pass
   it out via a channel.

   The big difference from functions is that "run()" executes the
   code in an entirely different interpreter, with entirely separate
   state.  The state of the current interpreter in the current OS
   thread is swapped out with the state of the target interpreter
   (the one that will execute the code).  When the target finishes
   executing, the original interpreter gets swapped back in and its
   execution resumes.

   So calling "run()" will effectively cause the current Python
   thread to pause.  Sometimes you won't want that pause, in which
   case you should make the "run()" call in another thread.  To do
   so, add a function that calls "run()" and then run that function
   in a normal "threading.Thread".

   Note that the interpreter's state is never reset, neither before
   "run()" executes the code nor after.  Thus the interpreter
   state is preserved between calls to "run()".  This includes
   "sys.modules", the "builtins" module, and the internal state
   of C extension modules.

   Also note that "run()" executes in the namespace of the "__main__"
   module, just like scripts, the REPL, "-m", and "-c".  Just as
   the interpreter's state is not ever reset, the "__main__" module
   is never reset.  You can imagine concatenating the code from each
   "run()" call into one long script.  This is the same as how the
   REPL operates.

   Supported code: source text.

Uncaught Exceptions

Regarding uncaught exceptions in, we noted that they are "effectively" propagated into the code where run() was called. To prevent leaking exceptions (and tracebacks) between interpreters, we create a surrogate of the exception and its traceback (see traceback.TracebackException), set it to __cause__ on a new RunFailedError, and raise that.

Raising (a proxy of) the exception is problematic since it's harder to distinguish between an error in the run() call and an uncaught exception from the subinterpreter.

API for sharing data

Subinterpreters are less useful without a mechanism for sharing data between them. Sharing actual Python objects between interpreters, however, has enough potential problems that we are avoiding support for that here. Instead, only mimimum set of types will be supported. Initially this will include None, bytes, str, int, and channels. Further types may be supported later.

The interpreters module provides a way for users to determine whether an object is shareable or not:


Return True if the object may be shared between interpreters.  This
does not necessarily mean that the actual objects will be shared.
Insead, it means that the objects' underlying data will be shared in
a cross-interpreter way, whether via a proxy, a copy, or some other

This proposal provides two ways to do share such objects between interpreters.

First, shareable objects may be passed to run() as keyword arguments, where they are effectively injected into the target interpreter's __main__ module. This is mainly intended for sharing meta-objects (e.g. channels) between interpreters, as it is less useful to pass other objects (like bytes) to run.

Second, the main mechanism for sharing objects (i.e. their data) between interpreters is through channels. A channel is a simplex FIFO similar to a pipe. The main difference is that channels can be associated with zero or more interpreters on either end. Unlike queues, which are also many-to-many, channels have no buffer.


Create a new channel and return (recv, send), the RecvChannel and
SendChannel corresponding to the ends of the channel.  The channel
is not closed and destroyed (i.e. garbage-collected) until the number
of associated interpreters returns to 0 (including when the channel
is explicitly closed).

An interpreter gets associated with a channel by calling its "send()"
or "recv()" method.  That association gets dropped by calling
"release()" on the channel.

Both ends of the channel are supported "shared" objects (i.e. may be
safely shared by different interpreters.  Thus they may be passed as
keyword arguments to "".


Return a list of all open (RecvChannel, SendChannel) pairs.


The receiving end of a channel.  An interpreter may use this to
receive objects from another interpreter.  At first only bytes will
be supported.


   The channel's unique ID.


   The list of associated interpreters: those that have called
   the "recv()" or "__next__()" methods and haven't called
   "release()" (and the channel hasn't been explicitly closed).


   Return the next object (i.e. the data from the sent object) from
   the channel.  If none have been sent then wait until the next
   send.  This associates the current interpreter with the channel.

   If the channel is already closed then raise ChannelClosedError.
   If the channel isn't closed but the current interpreter already
   called the "release()" method (which drops its association with
   the channel) then raise ChannelReleasedError (which is a subclass
   of ChannelClosedError).


   Return the next object from the channel.  If none have been sent
   then return the default.  Otherwise, this is the same as the
   "recv()" method.


   No longer associate the current interpreter with the channel (on
   the receiving end) and block future association (via the "recv()"
   method).  If the interpreter was never associated with the channel
   then still block future association.  Once an interpreter is no
   longer associated with the channel, subsequent (or current) send()
   and recv() calls from that interpreter will raise
   ChannelReleasedError (or ChannelClosedError if the channel
   is actually marked as closed).

   Once the number of associated interpreters on both ends drops
   to 0, the channel is actually marked as closed.  The Python
   runtime will garbage collect all closed channels, though it may
   not be immediately.  Note that "release()" is automatically called
   in behalf of the current interpreter when the channel is no longer
   used (i.e. has no references) in that interpreter.

   This operation is idempotent.  Return True if "release()" has not
   been called before by the current interpreter.


   Close both ends of the channel (in all interpreters).  This means
   that any further use of the channel raises ChannelClosedError.  If
   the channel is not empty then raise ChannelNotEmptyError (if
   "force" is False) or discard the remaining objects (if "force"
   is True) and close it.


The sending end of a channel.  An interpreter may use this to send
objects to another interpreter.  At first only bytes will be


   The channel's unique ID.


   The list of associated interpreters (those that have called
   the "send()" method).


   Send the object (i.e. its data) to the receiving end of the
   channel.  Wait until the object is received.  If the the
   object is not shareable then ValueError is raised.  Currently
   only bytes are supported.

   If the channel is already closed then raise ChannelClosedError.
   If the channel isn't closed but the current interpreter already
   called the "release()" method (which drops its association with
   the channel) then raise ChannelReleasedError.


   Send the object to the receiving end of the channel.  If the other
   end is not currently receiving then raise NotReceivedError.
   Otherwise this is the same as "send()".


   Send a MemoryView of the object rather than the object.  Otherwise
   this is the same as send().  Note that the object must implement
   the PEP 3118 buffer protocol.


   Send a MemoryView of the object rather than the object.  If the
   other end is not currently receiving then raise NotReceivedError.
   Otherwise this is the same as "send_buffer()".


   This is the same as "RecvChannel.release(), but applied to the
   sending end of the channel.


   Close both ends of the channel (in all interpreters).  No matter
   what the "send" end of the channel is immediately closed.  If the
   channel is empty then close the "recv" end immediately too.
   Otherwise wait until the channel is empty before closing it (if
   "force" is False) or discard the remaining items and close
   immediately (if "force" is True).

Note that send_buffer() is similar to how multiprocessing.Connection works. [mp-conn]

Open Questions

  • "force" argument to ch.release()?
  • add a "tp_share" type slot instead of using a global registry for shareable types?

Open Implementation Questions

Does every interpreter think that their thread is the "main" thread?

(This is more of an implementation detail that an issue for the PEP.)

CPython's interpreter implementation identifies the OS thread in which it was started as the "main" thread. The interpreter the has slightly different behavior depending on if the current thread is the main one or not. This presents a problem in cases where "main thread" is meant to imply "main thread in the main interpreter" [main-thread], where the main interpreter is the initial one.

Disallow subinterpreters in the main thread?

(This is more of an implementation detail that an issue for the PEP.)

This is a specific case of the above issue. Currently in CPython, "we need a main *thread* in order to sensibly manage the way signal handling works across different platforms". [main-thread]

Since signal handlers are part of the interpreter state, running a subinterpreter in the main thread means that the main interpreter can no longer properly handle signals (since it's effectively paused).

Furthermore, running a subinterpreter in the main thread would conceivably allow setting signal handlers on that interpreter, which would likewise impact signal handling when that interpreter isn't running or is running in a different thread.

Ultimately, running subinterpreters in the main OS thread introduces complications to the signal handling implementation. So it may make the most sense to disallow running subinterpreters in the main thread. Support for it could be considered later. The downside is that folks wanting to try out subinterpreters would be required to take the extra step of using threads. This could slow adoption and experimentation, whereas without the restriction there's less of an obstacle.

Deferred Functionality

In the interest of keeping this proposal minimal, the following functionality has been left out for future consideration. Note that this is not a judgement against any of said capability, but rather a deferment. That said, each is arguably valid.

It would be convenient to run existing functions in subinterpreters directly. could be adjusted to support this or a call() method could be added:, *args, **kwargs)

This suffers from the same problem as sharing objects between interpreters via queues. The minimal solution (running a source string) is sufficient for us to get the feature out where it can be explored.

timeout arg to recv() and send()

Typically functions that have a block argument also have a timeout argument. It sometimes makes sense to do likewise for functions that otherwise block, like the channel recv() and send() methods. We can add it later if needed.


CPython has a concept of a "main" interpreter. This is the initial interpreter created during CPython's runtime initialization. It may be useful to identify the main interpreter. For instance, the main interpreter should not be destroyed. However, for the basic functionality of a high-level API a get_main() function is not necessary. Furthermore, there is no requirement that a Python implementation have a concept of a main interpreter. So until there's a clear need we'll leave get_main() out.


This method would make a run() call for you in a thread. Doing this using only threading.Thread and run() is relatively trivial so we've left it out.

Synchronization Primitives

The threading module provides a number of synchronization primitives for coordinating concurrent operations. This is especially necessary due to the shared-state nature of threading. In contrast, subinterpreters do not share state. Data sharing is restricted to channels, which do away with the need for explicit synchronization. If any sort of opt-in shared state support is added to subinterpreters in the future, that same effort can introduce synchronization primitives to meet that need.

CSP Library

A csp module would not be a large step away from the functionality provided by this PEP. However, adding such a module is outside the minimalist goals of this proposal.

Syntactic Support

The Go language provides a concurrency model based on CSP, so it's similar to the concurrency model that subinterpreters support. Go provides syntactic support, as well several builtin concurrency primitives, to make concurrency a first-class feature. Conceivably, similar syntactic (and builtin) support could be added to Python using subinterpreters. However, that is way outside the scope of this PEP!


The multiprocessing module could support subinterpreters in the same way it supports threads and processes. In fact, the module's maintainer, Davin Potts, has indicated this is a reasonable feature request. However, it is outside the narrow scope of this PEP.

C-extension opt-in/opt-out

By using the PyModuleDef_Slot introduced by PEP 489, we could easily add a mechanism by which C-extension modules could opt out of support for subinterpreters. Then the import machinery, when operating in a subinterpreter, would need to check the module for support. It would raise an ImportError if unsupported.

Alternately we could support opting in to subinterpreter support. However, that would probably exclude many more modules (unnecessarily) than the opt-out approach.

The scope of adding the ModuleDef slot and fixing up the import machinery is non-trivial, but could be worth it. It all depends on how many extension modules break under subinterpreters. Given the relatively few cases we know of through mod_wsgi, we can leave this for later.

Poisoning channels

CSP has the concept of poisoning a channel. Once a channel has been poisoned, and send() or recv() call on it will raise a special exception, effectively ending execution in the interpreter that tried to use the poisoned channel.

This could be accomplished by adding a poison() method to both ends of the channel. The close() method can be used in this way (mostly), but these semantics are relatively specialized and can wait.

Sending channels over channels

Some advanced usage of subinterpreters could take advantage of the ability to send channels over channels, in addition to bytes. Given that channels will already be multi-interpreter safe, supporting then in RecvChannel.recv() wouldn't be a big change. However, this can wait until the basic functionality has been ironed out.

Reseting __main__

As proposed, every call to will execute in the namespace of the interpreter's existing __main__ module. This means that data persists there between run() calls. Sometimes this isn't desireable and you want to execute in a fresh __main__. Also, you don't necessarily want to leak objects there that you aren't using any more.

Note that the following won't work right because it will clear too much (e.g. __name__ and the other "__dunder__" attributes:'globals().clear()')

Possible solutions include:

  • a create() arg to indicate resetting __main__ after each run call
  • an Interpreter.reset_main flag to support opting in or out after the fact
  • an Interpreter.reset_main() method to opt in when desired
  • importlib.util.reset_globals() [reset_globals]

Also note that reseting __main__ does nothing about state stored in other modules. So any solution would have to be clear about the scope of what is being reset. Conceivably we could invent a mechanism by which any (or every) module could be reset, unlike reload() which does not clear the module before loading into it. Regardless, since __main__ is the execution namespace of the interpreter, resetting it has a much more direct correlation to interpreters and their dynamic state than does resetting other modules. So a more generic module reset mechanism may prove unnecessary.

This isn't a critical feature initially. It can wait until later if desirable.

File descriptors and sockets in channels

Given that file descriptors and sockets are process-global resources, support for passing them through channels is a reasonable idea. They would be a good candidate for the first effort at expanding the types that channels support. They aren't strictly necessary for the initial API.

Integration with async

Per Antoine Pitrou [async]:

Has any thought been given to how FIFOs could integrate with async
code driven by an event loop (e.g. asyncio)?  I think the model of
executing several asyncio (or Tornado) applications each in their
own subinterpreter may prove quite interesting to reconcile multi-
core concurrency with ease of programming.  That would require the
FIFOs to be able to synchronize on something an event loop can wait
on (probably a file descriptor?).

A possible solution is to provide async implementations of the blocking channel methods (__next__(), recv(), and send()). However, the basic functionality of subinterpreters does not depend on async and can be added later.

Support for iteration

Supporting iteration on RecvChannel (via __iter__() or _next__()) may be useful. A trivial implementation would use the recv() method, similar to how files do iteration. Since this isn't a fundamental capability and has a simple analog, adding iteration support can wait until later.

Channel context managers

Context manager support on RecvChannel and SendChannel may be helpful. The implementation would be simple, wrapping a call to close() (or maybe release()) like files do. As with iteration, this can wait.

Pipes and Queues

With the proposed object passing machanism of "channels", other similar basic types aren't required to achieve the minimal useful functionality of subinterpreters. Such types include pipes (like channels, but one-to-one) and queues (like channels, but buffered). See below in Rejected Ideas for more information.

Even though these types aren't part of this proposal, they may still be useful in the context of concurrency. Adding them later is entirely reasonable. The could be trivially implemented as wrappers around channels. Alternatively they could be implemented for efficiency at the same low level as channels.


The proposed channels are unbuffered. This simplifies the API and implementation. If buffering is desireable we can add it later.

Return a lock from send()

When sending an object through a channel, you don't have a way of knowing when the object gets received on the other end. One way to work around this is to return a locked threading.Lock from SendChannel.send() that unlocks once the object is received.

This matters for buffered channels (i.e. queues). For unbuffered channels it is a non-issue. So this can be dealt with once channels support buffering.

Add a "reraise" method to RunFailedError

While having __cause__ set on RunFailedError helps produce a more useful traceback, it's less helpful when handling the original error. To help facilitate this, we could add RunFailedError.reraise(). This method would enable the following pattern:

except RunFailedError as exc:
    except MyException:

This would be made even simpler if there existed a __reraise__ protocol.

Support prioritization in channels

A simple example is queue.PriorityQueue in the stdlib.

Rejected Ideas

Explicit channel association

Interpreters are implicitly associated with channels upon recv() and send() calls. They are de-associated with release() calls. The alternative would be explicit methods. It would be either add_channel() and remove_channel() methods on Interpreter objects or something similar on channel objects.

In practice, this level of management shouldn't be necessary for users. So adding more explicit support would only add clutter to the API.

Use pipes instead of channels

A pipe would be a simplex FIFO between exactly two interpreters. For most use cases this would be sufficient. It could potentially simplify the implementation as well. However, it isn't a big step to supporting a many-to-many simplex FIFO via channels. Also, with pipes the API ends up being slightly more complicated, requiring naming the pipes.

Use queues instead of channels

The main difference between queues and channels is that queues support buffering. This would complicate the blocking semantics of recv() and send(). Also, queues can be built on top of channels.


The list_all() function provides the list of all interpreters. In the threading module, which partly inspired the proposed API, the function is called enumerate(). The name is different here to avoid confusing Python users that are not already familiar with the threading API. For them "enumerate" is rather unclear, whereas "list_all" is clear.

Alternate solutions to prevent leaking exceptions across interpreters

In function calls, uncaught exceptions propagate to the calling frame. The same approach could be taken with run(). However, this would mean that exception objects would leak across the inter-interpreter boundary. Likewise, the frames in the traceback would potentially leak.

While that might not be a problem currently, it would be a problem once interpreters get better isolation relative to memory management (which is necessary to stop sharing the GIL between interpreters). We've resolved the semantics of how the exceptions propagate by raising a RunFailedError instead, for which __cause__ wraps a safe proxy for the original exception and traceback.

Rejected possible solutions:

  • reproduce the exception and traceback in the original interpreter and raise that.
  • raise a subclass of RunFailedError that proxies the original exception and traceback.
  • raise RuntimeError instead of RunFailedError
  • convert at the boundary (a la subprocess.CalledProcessError) (requires a cross-interpreter representation)
  • support customization via Interpreter.excepthook (requires a cross-interpreter representation)
  • wrap in a proxy at the boundary (including with support for something like err.raise() to propagate the traceback).
  • return the exception (or its proxy) from run() instead of raising it
  • return a result object (like subprocess does) [result-object] (unecessary complexity?)
  • throw the exception away and expect users to deal with unhandled exceptions explicitly in the script they pass to run() (they can pass error info out via channels); with threads you have to do something similar


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