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<article id="content">
<header>
<h1 class="title">Module <code>tinytroupe.utils.parallel</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from concurrent.futures import ThreadPoolExecutor
from typing import List, Any, Callable, Optional, Dict, Tuple, TypeVar, Iterator, Iterable
from itertools import product
def parallel_map(
objects: List[Any],
operation: Callable[[Any], Any],
max_workers: Optional[int] = None
) -> List[Any]:
"""
Execute operations on multiple objects in parallel and return the results.
Args:
objects: List of objects to process
operation: A callable (typically a lambda) that takes each object and returns a result
max_workers: Maximum number of threads to use for parallel execution
(None means use the default, which is min(32, os.cpu_count() + 4))
Returns:
List of results in the same order as the input objects
Example:
# For propositions p1, p2, p3
results = parallel_map([p1, p2, p3], lambda p: p.check())
# With arguments
results = parallel_map(
[p1, p2, p3],
lambda p: p.check(additional_context="Some context", return_full_response=True)
)
# Works with any operation
scores = parallel_map([p1, p2, p3], lambda p: p.score())
"""
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(operation, objects))
return results
K = TypeVar('K') # Key type
V = TypeVar('V') # Value type
R = TypeVar('R') # Result type
def parallel_map_dict(
dictionary: Dict[K, V],
operation: Callable[[Tuple[K, V]], R],
max_workers: Optional[int] = None
) -> Dict[K, R]:
"""
Execute operations on dictionary items in parallel and return results as a dictionary.
Args:
dictionary: Dictionary whose items will be processed
operation: A callable that takes a (key, value) tuple and returns a result
max_workers: Maximum number of threads to use
Returns:
Dictionary mapping original keys to operation results
Example:
# For environment propositions
results = parallel_map_dict(
environment_propositions,
lambda item: item[1].score(world, return_full_response=True)
)
"""
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Create a list of (key, result) tuples
items = list(dictionary.items())
results = list(executor.map(operation, items))
# Combine original keys with results
return {item[0]: result for item, result in zip(items, results)}
def parallel_map_cross(
iterables: List[Iterable],
operation: Callable[..., R],
max_workers: Optional[int] = None
) -> List[R]:
"""
Apply operation to each combination of elements from the iterables in parallel.
This is similar to using nested loops.
Args:
iterables: List of iterables to generate combinations from
operation: A callable that takes elements from each iterable and returns a result
max_workers: Maximum number of threads to use
Returns:
List of results from applying operation to each combination
Example:
# For every agent and proposition
results = parallel_map_cross(
[agents, agent_propositions.items()],
lambda agent, prop_item: (prop_item[0], prop_item[1].score(agent))
)
"""
combinations = list(product(*iterables))
def apply_to_combination(combo):
return operation(*combo)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(apply_to_combination, combinations))
return results</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="tinytroupe.utils.parallel.parallel_map"><code class="name flex">
<span>def <span class="ident">parallel_map</span></span>(<span>objects: List[Any], operation: Callable[[Any], Any], max_workers: Optional[int] = None) ‑> List[Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Execute operations on multiple objects in parallel and return the results.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>objects</code></strong></dt>
<dd>List of objects to process</dd>
<dt><strong><code>operation</code></strong></dt>
<dd>A callable (typically a lambda) that takes each object and returns a result</dd>
<dt><strong><code>max_workers</code></strong></dt>
<dd>Maximum number of threads to use for parallel execution
(None means use the default, which is min(32, os.cpu_count() + 4))</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>List of results in the same order as the input objects</p>
<h2 id="example">Example</h2>
<h1 id="for-propositions-p1-p2-p3">For propositions p1, p2, p3</h1>
<p>results = parallel_map([p1, p2, p3], lambda p: p.check())</p>
<h1 id="with-arguments">With arguments</h1>
<p>results = parallel_map(
[p1, p2, p3],
lambda p: p.check(additional_context="Some context", return_full_response=True)
)</p>
<h1 id="works-with-any-operation">Works with any operation</h1>
<p>scores = parallel_map([p1, p2, p3], lambda p: p.score())</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def parallel_map(
objects: List[Any],
operation: Callable[[Any], Any],
max_workers: Optional[int] = None
) -> List[Any]:
"""
Execute operations on multiple objects in parallel and return the results.
Args:
objects: List of objects to process
operation: A callable (typically a lambda) that takes each object and returns a result
max_workers: Maximum number of threads to use for parallel execution
(None means use the default, which is min(32, os.cpu_count() + 4))
Returns:
List of results in the same order as the input objects
Example:
# For propositions p1, p2, p3
results = parallel_map([p1, p2, p3], lambda p: p.check())
# With arguments
results = parallel_map(
[p1, p2, p3],
lambda p: p.check(additional_context="Some context", return_full_response=True)
)
# Works with any operation
scores = parallel_map([p1, p2, p3], lambda p: p.score())
"""
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(operation, objects))
return results</code></pre>
</details>
</dd>
<dt id="tinytroupe.utils.parallel.parallel_map_cross"><code class="name flex">
<span>def <span class="ident">parallel_map_cross</span></span>(<span>iterables: List[Iterable], operation: Callable[..., ~R], max_workers: Optional[int] = None) ‑> List[~R]</span>
</code></dt>
<dd>
<div class="desc"><p>Apply operation to each combination of elements from the iterables in parallel.
This is similar to using nested loops.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>iterables</code></strong></dt>
<dd>List of iterables to generate combinations from</dd>
<dt><strong><code>operation</code></strong></dt>
<dd>A callable that takes elements from each iterable and returns a result</dd>
<dt><strong><code>max_workers</code></strong></dt>
<dd>Maximum number of threads to use</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>List of results from applying operation to each combination</p>
<h2 id="example">Example</h2>
<h1 id="for-every-agent-and-proposition">For every agent and proposition</h1>
<p>results = parallel_map_cross(
[agents, agent_propositions.items()],
lambda agent, prop_item: (prop_item[0], prop_item[1].score(agent))
)</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def parallel_map_cross(
iterables: List[Iterable],
operation: Callable[..., R],
max_workers: Optional[int] = None
) -> List[R]:
"""
Apply operation to each combination of elements from the iterables in parallel.
This is similar to using nested loops.
Args:
iterables: List of iterables to generate combinations from
operation: A callable that takes elements from each iterable and returns a result
max_workers: Maximum number of threads to use
Returns:
List of results from applying operation to each combination
Example:
# For every agent and proposition
results = parallel_map_cross(
[agents, agent_propositions.items()],
lambda agent, prop_item: (prop_item[0], prop_item[1].score(agent))
)
"""
combinations = list(product(*iterables))
def apply_to_combination(combo):
return operation(*combo)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
results = list(executor.map(apply_to_combination, combinations))
return results</code></pre>
</details>
</dd>
<dt id="tinytroupe.utils.parallel.parallel_map_dict"><code class="name flex">
<span>def <span class="ident">parallel_map_dict</span></span>(<span>dictionary: Dict[~K, ~V], operation: Callable[[Tuple[~K, ~V]], ~R], max_workers: Optional[int] = None) ‑> Dict[~K, ~R]</span>
</code></dt>
<dd>
<div class="desc"><p>Execute operations on dictionary items in parallel and return results as a dictionary.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>dictionary</code></strong></dt>
<dd>Dictionary whose items will be processed</dd>
<dt><strong><code>operation</code></strong></dt>
<dd>A callable that takes a (key, value) tuple and returns a result</dd>
<dt><strong><code>max_workers</code></strong></dt>
<dd>Maximum number of threads to use</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>Dictionary mapping original keys to operation results</p>
<h2 id="example">Example</h2>
<h1 id="for-environment-propositions">For environment propositions</h1>
<p>results = parallel_map_dict(
environment_propositions,
lambda item: item[1].score(world, return_full_response=True)
)</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def parallel_map_dict(
dictionary: Dict[K, V],
operation: Callable[[Tuple[K, V]], R],
max_workers: Optional[int] = None
) -> Dict[K, R]:
"""
Execute operations on dictionary items in parallel and return results as a dictionary.
Args:
dictionary: Dictionary whose items will be processed
operation: A callable that takes a (key, value) tuple and returns a result
max_workers: Maximum number of threads to use
Returns:
Dictionary mapping original keys to operation results
Example:
# For environment propositions
results = parallel_map_dict(
environment_propositions,
lambda item: item[1].score(world, return_full_response=True)
)
"""
with ThreadPoolExecutor(max_workers=max_workers) as executor:
# Create a list of (key, result) tuples
items = list(dictionary.items())
results = list(executor.map(operation, items))
# Combine original keys with results
return {item[0]: result for item, result in zip(items, results)}</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="tinytroupe.utils" href="index.html">tinytroupe.utils</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="tinytroupe.utils.parallel.parallel_map" href="#tinytroupe.utils.parallel.parallel_map">parallel_map</a></code></li>
<li><code><a title="tinytroupe.utils.parallel.parallel_map_cross" href="#tinytroupe.utils.parallel.parallel_map_cross">parallel_map_cross</a></code></li>
<li><code><a title="tinytroupe.utils.parallel.parallel_map_dict" href="#tinytroupe.utils.parallel.parallel_map_dict">parallel_map_dict</a></code></li>
</ul>
</li>
</ul>
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