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 <section id="module-multiprocessing.shared_memory">
<h1><a class="reference internal" href="#module-multiprocessing.shared_memory" title="multiprocessing.shared_memory: Provides shared memory for direct access across processes."><code class="xref py py-mod docutils literal notranslate">multiprocessing.shared_memory</code></a> — Shared memory for direct access across processes<a class="headerlink" href="#module-multiprocessing.shared_memory" title="Permalink to this headline">¶</a></h1>
Source code: <a class="reference external" href="https://github.com/python/cpython/tree/3.10/Lib/multiprocessing/shared_memory.py">Lib/multiprocessing/shared_memory.py</a>
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</div>
<hr class="docutils" id="index-0" />
This module provides a class, <a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory" title="multiprocessing.shared_memory.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a>, for the allocation
and management of shared memory to be accessed by one or more processes
on a multicore or symmetric multiprocessor (SMP) machine. To assist with
the life-cycle management of shared memory especially across distinct
processes, a <a class="reference internal" href="multiprocessing.html#multiprocessing.managers.BaseManager" title="multiprocessing.managers.BaseManager"><code class="xref py py-class docutils literal notranslate">BaseManager</code></a> subclass,
<code class="xref py py-class docutils literal notranslate">SharedMemoryManager</code>, is also provided in the
<code class="docutils literal notranslate">multiprocessing.managers</code> module.
In this module, shared memory refers to “System V style” shared memory blocks
(though is not necessarily implemented explicitly as such) and does not refer
to “distributed shared memory”. This style of shared memory permits distinct
processes to potentially read and write to a common (or shared) region of
volatile memory. Processes are conventionally limited to only have access to
their own process memory space but shared memory permits the sharing
of data between processes, avoiding the need to instead send messages between
processes containing that data. Sharing data directly via memory can provide
significant performance benefits compared to sharing data via disk or socket
or other communications requiring the serialization/deserialization and
copying of data.
<dl class="py class">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.SharedMemory">
class multiprocessing.shared_memory.SharedMemory(name=None, create=False, size=0)<a class="headerlink" href="#multiprocessing.shared_memory.SharedMemory" title="Permalink to this definition">¶</a></dt>
<dd>Creates a new shared memory block or attaches to an existing shared
memory block. Each shared memory block is assigned a unique name.
In this way, one process can create a shared memory block with a
particular name and a different process can attach to that same shared
memory block using that same name.
As a resource for sharing data across processes, shared memory blocks
may outlive the original process that created them. When one process
no longer needs access to a shared memory block that might still be
needed by other processes, the <a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory.close" title="multiprocessing.shared_memory.SharedMemory.close"><code class="xref py py-meth docutils literal notranslate">close()</code></a> method should be called.
When a shared memory block is no longer needed by any process, the
<a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory.unlink" title="multiprocessing.shared_memory.SharedMemory.unlink"><code class="xref py py-meth docutils literal notranslate">unlink()</code></a> method should be called to ensure proper cleanup.
name is the unique name for the requested shared memory, specified as
a string. When creating a new shared memory block, if <code class="docutils literal notranslate">None</code> (the
default) is supplied for the name, a novel name will be generated.
create controls whether a new shared memory block is created (<code class="docutils literal notranslate">True</code>)
or an existing shared memory block is attached (<code class="docutils literal notranslate">False</code>).
size specifies the requested number of bytes when creating a new shared
memory block. Because some platforms choose to allocate chunks of memory
based upon that platform’s memory page size, the exact size of the shared
memory block may be larger or equal to the size requested. When attaching
to an existing shared memory block, the <code class="docutils literal notranslate">size</code> parameter is ignored.
<dl class="py method">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.SharedMemory.close">
close()<a class="headerlink" href="#multiprocessing.shared_memory.SharedMemory.close" title="Permalink to this definition">¶</a></dt>
<dd>Closes access to the shared memory from this instance. In order to
ensure proper cleanup of resources, all instances should call
<code class="docutils literal notranslate">close()</code> once the instance is no longer needed. Note that calling
<code class="docutils literal notranslate">close()</code> does not cause the shared memory block itself to be
destroyed.
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.SharedMemory.unlink">
unlink()<a class="headerlink" href="#multiprocessing.shared_memory.SharedMemory.unlink" title="Permalink to this definition">¶</a></dt>
<dd>Requests that the underlying shared memory block be destroyed. In
order to ensure proper cleanup of resources, <code class="docutils literal notranslate">unlink()</code> should be
called once (and only once) across all processes which have need
for the shared memory block. After requesting its destruction, a
shared memory block may or may not be immediately destroyed and
this behavior may differ across platforms. Attempts to access data
inside the shared memory block after <code class="docutils literal notranslate">unlink()</code> has been called may
result in memory access errors. Note: the last process relinquishing
its hold on a shared memory block may call <code class="docutils literal notranslate">unlink()</code> and
<a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory.close" title="multiprocessing.shared_memory.SharedMemory.close"><code class="xref py py-meth docutils literal notranslate">close()</code></a> in either order.
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.SharedMemory.buf">
buf<a class="headerlink" href="#multiprocessing.shared_memory.SharedMemory.buf" title="Permalink to this definition">¶</a></dt>
<dd>A memoryview of contents of the shared memory block.
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.SharedMemory.name">
name<a class="headerlink" href="#multiprocessing.shared_memory.SharedMemory.name" title="Permalink to this definition">¶</a></dt>
<dd>Read-only access to the unique name of the shared memory block.
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.SharedMemory.size">
size<a class="headerlink" href="#multiprocessing.shared_memory.SharedMemory.size" title="Permalink to this definition">¶</a></dt>
<dd>Read-only access to size in bytes of the shared memory block.
</dd></dl>

</dd></dl>

The following example demonstrates low-level use of <a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory" title="multiprocessing.shared_memory.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a>
instances:
<div class="highlight-python3 notranslate"><div class="highlight"><pre>&gt;&gt;&gt; from multiprocessing import shared_memory
&gt;&gt;&gt; shm_a = shared_memory.SharedMemory(create=True, size=10)
&gt;&gt;&gt; type(shm_a.buf)
&lt;class &#39;memoryview&#39;&gt;
&gt;&gt;&gt; buffer = shm_a.buf
&gt;&gt;&gt; len(buffer)
10
&gt;&gt;&gt; buffer[:4] = bytearray([22, 33, 44, 55]) # Modify multiple at once
&gt;&gt;&gt; buffer[4] = 100 # Modify single byte at a time
&gt;&gt;&gt; # Attach to an existing shared memory block
&gt;&gt;&gt; shm_b = shared_memory.SharedMemory(shm_a.name)
&gt;&gt;&gt; import array
&gt;&gt;&gt; array.array(&#39;b&#39;, shm_b.buf[:5]) # Copy the data into a new array.array
array(&#39;b&#39;, [22, 33, 44, 55, 100])
&gt;&gt;&gt; shm_b.buf[:5] = b&#39;howdy&#39; # Modify via shm_b using bytes
&gt;&gt;&gt; bytes(shm_a.buf[:5]) # Access via shm_a
b&#39;howdy&#39;
&gt;&gt;&gt; shm_b.close() # Close each SharedMemory instance
&gt;&gt;&gt; shm_a.close()
&gt;&gt;&gt; shm_a.unlink() # Call unlink only once to release the shared memory
</pre></div>
</div>
The following example demonstrates a practical use of the <a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory" title="multiprocessing.shared_memory.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a>
class with <a class="reference external" href="https://numpy.org/">NumPy arrays</a>, accessing the
same <code class="docutils literal notranslate">numpy.ndarray</code> from two distinct Python shells:
<div class="highlight-pycon3 notranslate"><div class="highlight"><pre>&gt;&gt;&gt; # In the first Python interactive shell
&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; a = np.array([1, 1, 2, 3, 5, 8]) # Start with an existing NumPy array
&gt;&gt;&gt; from multiprocessing import shared_memory
&gt;&gt;&gt; shm = shared_memory.SharedMemory(create=True, size=a.nbytes)
&gt;&gt;&gt; # Now create a NumPy array backed by shared memory
&gt;&gt;&gt; b = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)
&gt;&gt;&gt; b[:] = a[:] # Copy the original data into shared memory
&gt;&gt;&gt; b
array([1, 1, 2, 3, 5, 8])
&gt;&gt;&gt; type(b)
&lt;class &#39;numpy.ndarray&#39;&gt;
&gt;&gt;&gt; type(a)
&lt;class &#39;numpy.ndarray&#39;&gt;
&gt;&gt;&gt; shm.name # We did not specify a name so one was chosen for us
&#39;psm_21467_46075&#39;

&gt;&gt;&gt; # In either the same shell or a new Python shell on the same machine
&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; from multiprocessing import shared_memory
&gt;&gt;&gt; # Attach to the existing shared memory block
&gt;&gt;&gt; existing_shm = shared_memory.SharedMemory(name=&#39;psm_21467_46075&#39;)
&gt;&gt;&gt; # Note that a.shape is (6,) and a.dtype is np.int64 in this example
&gt;&gt;&gt; c = np.ndarray((6,), dtype=np.int64, buffer=existing_shm.buf)
&gt;&gt;&gt; c
array([1, 1, 2, 3, 5, 8])
&gt;&gt;&gt; c[-1] = 888
&gt;&gt;&gt; c
array([ 1, 1, 2, 3, 5, 888])

&gt;&gt;&gt; # Back in the first Python interactive shell, b reflects this change
&gt;&gt;&gt; b
array([ 1, 1, 2, 3, 5, 888])

&gt;&gt;&gt; # Clean up from within the second Python shell
&gt;&gt;&gt; del c # Unnecessary; merely emphasizing the array is no longer used
&gt;&gt;&gt; existing_shm.close()

&gt;&gt;&gt; # Clean up from within the first Python shell
&gt;&gt;&gt; del b # Unnecessary; merely emphasizing the array is no longer used
&gt;&gt;&gt; shm.close()
&gt;&gt;&gt; shm.unlink() # Free and release the shared memory block at the very end
</pre></div>
</div>
<dl class="py class">
<dt class="sig sig-object py" id="multiprocessing.managers.SharedMemoryManager">
class multiprocessing.managers.SharedMemoryManager([address[, authkey]])<a class="headerlink" href="#multiprocessing.managers.SharedMemoryManager" title="Permalink to this definition">¶</a></dt>
<dd>A subclass of <a class="reference internal" href="multiprocessing.html#multiprocessing.managers.BaseManager" title="multiprocessing.managers.BaseManager"><code class="xref py py-class docutils literal notranslate">BaseManager</code></a> which can be
used for the management of shared memory blocks across processes.
A call to <a class="reference internal" href="multiprocessing.html#multiprocessing.managers.BaseManager.start" title="multiprocessing.managers.BaseManager.start"><code class="xref py py-meth docutils literal notranslate">start()</code></a> on a
<a class="reference internal" href="#multiprocessing.managers.SharedMemoryManager" title="multiprocessing.managers.SharedMemoryManager"><code class="xref py py-class docutils literal notranslate">SharedMemoryManager</code></a> instance causes a new process to be started.
This new process’s sole purpose is to manage the life cycle
of all shared memory blocks created through it. To trigger the release
of all shared memory blocks managed by that process, call
<a class="reference internal" href="multiprocessing.html#multiprocessing.managers.BaseManager.shutdown" title="multiprocessing.managers.BaseManager.shutdown"><code class="xref py py-meth docutils literal notranslate">shutdown()</code></a> on the instance.
This triggers a <code class="xref py py-meth docutils literal notranslate">SharedMemory.unlink()</code> call on all of the
<a class="reference internal" href="#multiprocessing.managers.SharedMemoryManager.SharedMemory" title="multiprocessing.managers.SharedMemoryManager.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a> objects managed by that process and then
stops the process itself. By creating <code class="docutils literal notranslate">SharedMemory</code> instances
through a <code class="docutils literal notranslate">SharedMemoryManager</code>, we avoid the need to manually track
and trigger the freeing of shared memory resources.
This class provides methods for creating and returning <a class="reference internal" href="#multiprocessing.managers.SharedMemoryManager.SharedMemory" title="multiprocessing.managers.SharedMemoryManager.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a>
instances and for creating a list-like object (<a class="reference internal" href="#multiprocessing.managers.SharedMemoryManager.ShareableList" title="multiprocessing.managers.SharedMemoryManager.ShareableList"><code class="xref py py-class docutils literal notranslate">ShareableList</code></a>)
backed by shared memory.
Refer to <a class="reference internal" href="multiprocessing.html#multiprocessing.managers.BaseManager" title="multiprocessing.managers.BaseManager"><code class="xref py py-class docutils literal notranslate">multiprocessing.managers.BaseManager</code></a> for a description
of the inherited address and authkey optional input arguments and how
they may be used to connect to an existing <code class="docutils literal notranslate">SharedMemoryManager</code> service
from other processes.
<dl class="py method">
<dt class="sig sig-object py" id="multiprocessing.managers.SharedMemoryManager.SharedMemory">
SharedMemory(size)<a class="headerlink" href="#multiprocessing.managers.SharedMemoryManager.SharedMemory" title="Permalink to this definition">¶</a></dt>
<dd>Create and return a new <a class="reference internal" href="#multiprocessing.managers.SharedMemoryManager.SharedMemory" title="multiprocessing.managers.SharedMemoryManager.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a> object with the
specified <code class="docutils literal notranslate">size</code> in bytes.
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="multiprocessing.managers.SharedMemoryManager.ShareableList">
ShareableList(sequence)<a class="headerlink" href="#multiprocessing.managers.SharedMemoryManager.ShareableList" title="Permalink to this definition">¶</a></dt>
<dd>Create and return a new <a class="reference internal" href="#multiprocessing.managers.SharedMemoryManager.ShareableList" title="multiprocessing.managers.SharedMemoryManager.ShareableList"><code class="xref py py-class docutils literal notranslate">ShareableList</code></a> object, initialized
by the values from the input <code class="docutils literal notranslate">sequence</code>.
</dd></dl>

</dd></dl>

The following example demonstrates the basic mechanisms of a
<code class="xref py py-class docutils literal notranslate">SharedMemoryManager</code>:
<div class="highlight-pycon3 notranslate"><div class="highlight"><pre>&gt;&gt;&gt; from multiprocessing.managers import SharedMemoryManager
&gt;&gt;&gt; smm = SharedMemoryManager()
&gt;&gt;&gt; smm.start() # Start the process that manages the shared memory blocks
&gt;&gt;&gt; sl = smm.ShareableList(range(4))
&gt;&gt;&gt; sl
ShareableList([0, 1, 2, 3], name=&#39;psm_6572_7512&#39;)
&gt;&gt;&gt; raw_shm = smm.SharedMemory(size=128)
&gt;&gt;&gt; another_sl = smm.ShareableList(&#39;alpha&#39;)
&gt;&gt;&gt; another_sl
ShareableList([&#39;a&#39;, &#39;l&#39;, &#39;p&#39;, &#39;h&#39;, &#39;a&#39;], name=&#39;psm_6572_12221&#39;)
&gt;&gt;&gt; smm.shutdown() # Calls unlink() on sl, raw_shm, and another_sl
</pre></div>
</div>
The following example depicts a potentially more convenient pattern for using
<code class="xref py py-class docutils literal notranslate">SharedMemoryManager</code> objects via the <a class="reference internal" href="../reference/compound_stmts.html#with"><code class="xref std std-keyword docutils literal notranslate">with</code></a> statement to
ensure that all shared memory blocks are released after they are no longer
needed:
<div class="highlight-pycon3 notranslate"><div class="highlight"><pre>&gt;&gt;&gt; with SharedMemoryManager() as smm:
... sl = smm.ShareableList(range(2000))
... # Divide the work among two processes, storing partial results in sl
... p1 = Process(target=do_work, args=(sl, 0, 1000))
... p2 = Process(target=do_work, args=(sl, 1000, 2000))
... p1.start()
... p2.start() # A multiprocessing.Pool might be more efficient
... p1.join()
... p2.join() # Wait for all work to complete in both processes
... total_result = sum(sl) # Consolidate the partial results now in sl
</pre></div>
</div>
When using a <code class="xref py py-class docutils literal notranslate">SharedMemoryManager</code> in a <a class="reference internal" href="../reference/compound_stmts.html#with"><code class="xref std std-keyword docutils literal notranslate">with</code></a> statement, the
shared memory blocks created using that manager are all released when the
<a class="reference internal" href="../reference/compound_stmts.html#with"><code class="xref std std-keyword docutils literal notranslate">with</code></a> statement’s code block finishes execution.
<dl class="py class">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.ShareableList">
class multiprocessing.shared_memory.ShareableList(sequence=None, *, name=None)<a class="headerlink" href="#multiprocessing.shared_memory.ShareableList" title="Permalink to this definition">¶</a></dt>
<dd>Provides a mutable list-like object where all values stored within are
stored in a shared memory block. This constrains storable values to
only the <code class="docutils literal notranslate">int</code>, <code class="docutils literal notranslate">float</code>, <code class="docutils literal notranslate">bool</code>, <code class="docutils literal notranslate">str</code> (less than 10M bytes each),
<code class="docutils literal notranslate">bytes</code> (less than 10M bytes each), and <code class="docutils literal notranslate">None</code> built-in data types.
It also notably differs from the built-in <code class="docutils literal notranslate">list</code> type in that these
lists can not change their overall length (i.e. no append, insert, etc.)
and do not support the dynamic creation of new <a class="reference internal" href="#multiprocessing.shared_memory.ShareableList" title="multiprocessing.shared_memory.ShareableList"><code class="xref py py-class docutils literal notranslate">ShareableList</code></a>
instances via slicing.
sequence is used in populating a new <code class="docutils literal notranslate">ShareableList</code> full of values.
Set to <code class="docutils literal notranslate">None</code> to instead attach to an already existing
<code class="docutils literal notranslate">ShareableList</code> by its unique shared memory name.
name is the unique name for the requested shared memory, as described
in the definition for <a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory" title="multiprocessing.shared_memory.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a>. When attaching to an
existing <code class="docutils literal notranslate">ShareableList</code>, specify its shared memory block’s unique
name while leaving <code class="docutils literal notranslate">sequence</code> set to <code class="docutils literal notranslate">None</code>.
<dl class="py method">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.ShareableList.count">
count(value)<a class="headerlink" href="#multiprocessing.shared_memory.ShareableList.count" title="Permalink to this definition">¶</a></dt>
<dd>Returns the number of occurrences of <code class="docutils literal notranslate">value</code>.
</dd></dl>

<dl class="py method">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.ShareableList.index">
index(value)<a class="headerlink" href="#multiprocessing.shared_memory.ShareableList.index" title="Permalink to this definition">¶</a></dt>
<dd>Returns first index position of <code class="docutils literal notranslate">value</code>. Raises <a class="reference internal" href="exceptions.html#ValueError" title="ValueError"><code class="xref py py-exc docutils literal notranslate">ValueError</code></a> if
<code class="docutils literal notranslate">value</code> is not present.
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.ShareableList.format">
format<a class="headerlink" href="#multiprocessing.shared_memory.ShareableList.format" title="Permalink to this definition">¶</a></dt>
<dd>Read-only attribute containing the <a class="reference internal" href="struct.html#module-struct" title="struct: Interpret bytes as packed binary data."><code class="xref py py-mod docutils literal notranslate">struct</code></a> packing format used by
all currently stored values.
</dd></dl>

<dl class="py attribute">
<dt class="sig sig-object py" id="multiprocessing.shared_memory.ShareableList.shm">
shm<a class="headerlink" href="#multiprocessing.shared_memory.ShareableList.shm" title="Permalink to this definition">¶</a></dt>
<dd>The <a class="reference internal" href="#multiprocessing.shared_memory.SharedMemory" title="multiprocessing.shared_memory.SharedMemory"><code class="xref py py-class docutils literal notranslate">SharedMemory</code></a> instance where the values are stored.
</dd></dl>

</dd></dl>

The following example demonstrates basic use of a <a class="reference internal" href="#multiprocessing.shared_memory.ShareableList" title="multiprocessing.shared_memory.ShareableList"><code class="xref py py-class docutils literal notranslate">ShareableList</code></a>
instance:
<div class="doctest highlight-default notranslate"><div class="highlight"><pre>&gt;&gt;&gt; from multiprocessing import shared_memory
&gt;&gt;&gt; a = shared_memory.ShareableList([&#39;howdy&#39;, b&#39;HoWdY&#39;, -273.154, 100, None, True, 42])
&gt;&gt;&gt; [ type(entry) for entry in a ]
[&lt;class &#39;str&#39;&gt;, &lt;class &#39;bytes&#39;&gt;, &lt;class &#39;float&#39;&gt;, &lt;class &#39;int&#39;&gt;, &lt;class &#39;NoneType&#39;&gt;, &lt;class &#39;bool&#39;&gt;, &lt;class &#39;int&#39;&gt;]
&gt;&gt;&gt; a[2]
-273.154
&gt;&gt;&gt; a[2] = -78.5
&gt;&gt;&gt; a[2]
-78.5
&gt;&gt;&gt; a[2] = &#39;dry ice&#39; # Changing data types is supported as well
&gt;&gt;&gt; a[2]
&#39;dry ice&#39;
&gt;&gt;&gt; a[2] = &#39;larger than previously allocated storage space&#39;
Traceback (most recent call last):
 ...
ValueError: exceeds available storage for existing str
&gt;&gt;&gt; a[2]
&#39;dry ice&#39;
&gt;&gt;&gt; len(a)
7
&gt;&gt;&gt; a.index(42)
6
&gt;&gt;&gt; a.count(b&#39;howdy&#39;)
0
&gt;&gt;&gt; a.count(b&#39;HoWdY&#39;)
1
&gt;&gt;&gt; a.shm.close()
&gt;&gt;&gt; a.shm.unlink()
&gt;&gt;&gt; del a # Use of a ShareableList after call to unlink() is unsupported
</pre></div>
</div>
The following example depicts how one, two, or many processes may access the
same <a class="reference internal" href="#multiprocessing.shared_memory.ShareableList" title="multiprocessing.shared_memory.ShareableList"><code class="xref py py-class docutils literal notranslate">ShareableList</code></a> by supplying the name of the shared memory block
behind it:
<div class="doctest highlight-default notranslate"><div class="highlight"><pre>&gt;&gt;&gt; b = shared_memory.ShareableList(range(5)) # In a first process
&gt;&gt;&gt; c = shared_memory.ShareableList(name=b.shm.name) # In a second process
&gt;&gt;&gt; c
ShareableList([0, 1, 2, 3, 4], name=&#39;...&#39;)
&gt;&gt;&gt; c[-1] = -999
&gt;&gt;&gt; b[-1]
-999
&gt;&gt;&gt; b.shm.close()
&gt;&gt;&gt; c.shm.close()
&gt;&gt;&gt; c.shm.unlink()
</pre></div>
</div>
The following examples demonstrates that <code class="docutils literal notranslate">ShareableList</code>
(and underlying <code class="docutils literal notranslate">SharedMemory</code>) objects
can be pickled and unpickled if needed.
Note, that it will still be the same shared object.
This happens, because the deserialized object has
the same unique name and is just attached to an existing
object with the same name (if the object is still alive):
<div class="doctest highlight-default notranslate"><div class="highlight"><pre>&gt;&gt;&gt; import pickle
&gt;&gt;&gt; from multiprocessing import shared_memory
&gt;&gt;&gt; sl = shared_memory.ShareableList(range(10))
&gt;&gt;&gt; list(sl)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre>&gt;&gt;&gt; deserialized_sl = pickle.loads(pickle.dumps(sl))
&gt;&gt;&gt; list(deserialized_sl)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre>&gt;&gt;&gt; sl[0] = -1
&gt;&gt;&gt; deserialized_sl[1] = -2
&gt;&gt;&gt; list(sl)
[-1, -2, 2, 3, 4, 5, 6, 7, 8, 9]
&gt;&gt;&gt; list(deserialized_sl)
[-1, -2, 2, 3, 4, 5, 6, 7, 8, 9]
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre>&gt;&gt;&gt; sl.shm.close()
&gt;&gt;&gt; sl.shm.unlink()
</pre></div>
</div>
</section>

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