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[project]
name = "demo-concurrence"
version = "0.1.0"
description = "Démonstration sur la concurrence en Python (AsyncIO et Multiprocessing)"
authors = [
{name = "Your Name", email = "you@example.com"}
]
readme = "README.md"
classifiers = [
"Programming Language :: Python :: 3.13",
]
keywords = ["asyncio", "concurrency", "multiprocessing"]
requires-python = ">=3.13"
[tool.poetry]
package-mode = false
[tool.poetry.dependencies]
python = "^3.13"
requests = "^2.31.0"
aiohttp = "^3.9.1"
aiofiles = "^23.2.1"
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

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# Démonstration : introduction à la programmation concurrente en Python
## Objectifs Pédagogiques
À la fin de cette démonstration, vous serez capable de :
1. Identifier la différence fondamentale entre un problème **I/O-bound** (limité par les attentes) et un problème **CPU-bound** (limité par la puissance de calcul).
2. Mettre en œuvre une solution de **concurrence** avec `asyncio` pour accélérer les tâches I/O-bound.
3. Mettre en œuvre une solution de **parallélisme** avec `multiprocessing` pour accélérer les tâches CPU-bound.
4. Comprendre quand utiliser l'un ou l'autre de ces modèles.
5. Initiation au débogage via l'IDE PyCharm.
---
## Partie 1 : le problème I/O-Bound (concurrence avec `asyncio`)
**Scénario :** nous avons une série de tâches qui passent la majorité de leur temps à attendre une ressource externe (par exemple, une réponse d'API, une requête de base de données, ou une lecture sur un disque lent). Nous allons simuler cette attente avec une pause.
### Exercice 1.1 : l'approche séquentielle (le point de départ)
1. Ouvrez le fichier `1_sync_simulation.py`.
2. Lisez attentivement le code, en particulier la fonction `worker_task`. Notez l'utilisation de `time.sleep()`, une fonction **bloquante** qui met en pause tout le programme.
```mermaid
sequenceDiagram
participant main as "Programme Principal"
participant task1 as "Tâche 1"
participant task2 as "Tâche 2"
participant task3 as "Tâche 3 (etc...)"
main->>task1: Démarrer
note right of task1: "time.sleep(1s) (BLOQUANT)"
task1-->>main: Fini
main->>task2: Démarrer
note right of task2: "time.sleep(1s) (BLOQUANT)"
task2-->>main: Fini
main->>task3: Démarrer
note right of task3: "time.sleep(1s) (BLOQUANT)"
task3-->>main: Fini
```
3. Exécutez le script via **le** bouton Run de PyCharm.
4. **Observez le temps d'exécution total.** Il devrait être approximativement égal à `NUM_TASKS * SLEEP_DURATION`. Prenez note de cette valeur.
### Exercice 1.2 : l'approche Asynchrone (à compléter)
Maintenant, nous allons modifier le script pour qu'il exécute ces mêmes tâches d'attente de manière concurrente.
1. Ouvrez le fichier `2_async_simulation.py`. Ce fichier est incomplet et contient plusieurs `TODO`.
2. Suivez les instructions ci-dessous pour le compléter :
* **`TODO 1` dans la fonction `worker_task_async` :** une fonction qui utilise `await` doit être déclarée comme une "coroutine".
> Modifiez la signature de la fonction `worker_task_async` pour en faire une coroutine native.
* **`TODO 2` dans la fonction `worker_task_async` :** nous devons remplacer l'attente bloquante par son équivalent non-bloquant du module `asyncio`.
> Trouvez la fonction adéquate dans le module `asyncio` pour simuler une attente et utilisez `await` pour l'appeler.
* **`TODO 3` dans la fonction `main` :** la puissance de `asyncio` vient de sa capacité à lancer de nombreuses tâches et à les gérer simultanément.
> Utilisez la fonction `asyncio.gather()` pour lancer toutes les coroutines de la liste `tasks` en concurrence. N'oubliez pas d'utiliser `await` pour attendre que `gather()` se termine.
<!-- end list -->
```mermaid
sequenceDiagram
participant main as "Programme Principal"
participant eventLoop as "Event Loop (asyncio)"
participant task1 as "Tâche 1"
participant task2 as "Tâche 2"
participant task3 as "Tâche 3"
activate eventLoop
main->>eventLoop: "asyncio.run(main)"
eventLoop->>task1: Lancer (via gather)
eventLoop->>task2: Lancer (via gather)
eventLoop->>task3: Lancer (via gather)
note over task1,task3: Les tâches démarrent quasi-simultanément
task1->>eventLoop: "await asyncio.sleep(1s)" (NON-BLOQUANT)
note right of task1: Rend le contrôle
task2->>eventLoop: "await asyncio.sleep(1s)" (NON-BLOQUANT)
note right of task2: Rend le contrôle
task3->>eventLoop: "await asyncio.sleep(1s)" (NON-BLOQUANT)
note right of task3: Rend le contrôle
eventLoop-->>task1: Reprendre (après 1s)
task1-->>eventLoop: Fini
eventLoop-->>task2: Reprendre (après 1s)
task2-->>eventLoop: Fini
eventLoop-->>task3: Reprendre (après 1s)
task3-->>eventLoop: Fini
eventLoop-->>main: Terminé
deactivate eventLoop
```
3. Exécutez le script via **le** bouton Run de PyCharm.
4. **Comparez le temps d'exécution** avec la version synchrone. Que remarquez-vous ? Pourquoi une telle différence ?
---
## Partie 2 : le problème CPU-Bound (parallélisme avec `multiprocessing`)
**Scénario :** nous changeons de problème. Nous avons maintenant des tâches qui ne consistent pas à attendre, mais à effectuer des calculs mathématiques très intensifs. Chaque tâche utilise 100% d'un cœur de processeur.
### Exercice 2.1 : l'approche séquentielle et parallèle (à compléter)
1. Ouvrez le fichier `3_multiprocessing_demo.py`.
* La fonction `heavy_analysis` simule notre tâche de calcul lourd.
* La fonction `run_sequential` est déjà complète. Elle exécute les tâches sur un seul cœur, l'une après l'autre.
<!-- end list -->
```mermaid
graph TD
subgraph "Exécution Séquentielle"
subgraph "CPU Core 1"
A[Tâche 1: Calcul lourd] --> B[Tâche 2: Calcul lourd] --> C[Tâche 3: Calcul lourd] --> D[Fin]
end
subgraph "Autres Cores"
idle1[Inutilisé]
idle2[Inutilisé]
idle3[Inutilisé]
end
end
```
* La fonction `run_parallel` est incomplète. C'est elle que nous allons modifier.
2. Suivez les instructions pour compléter la fonction `run_parallel` :
* **`TODO 1` :** pour paralléliser le travail sur plusieurs cœurs, nous avons besoin d'un "pool de processus". La bibliothèque `concurrent.futures` fournit un gestionnaire de contexte parfait pour cela.
> Instanciez un `ProcessPoolExecutor` en utilisant un bloc `with`. Vous pouvez laisser le nombre de `workers` par défaut pour qu'il utilise tous vos cœurs CPU.
* **`TODO 2` :** une fois le pool de processus créé, il faut lui donner la liste des tâches à exécuter.
> Utilisez la méthode `.map()` de l'exécuteur pour appliquer la `worker_function` à chaque élément de la liste `tasks`. Cette méthode distribue le travail et retourne les résultats.
<!-- end list -->
```mermaid
graph TD
subgraph "Exécution Parallèle"
pool("ProcessPoolExecutor")
subgraph "CPU Core 1"
T1[Tâche 1: Calcul]
end
subgraph "CPU Core 2"
T2[Tâche 2: Calcul]
end
subgraph "CPU Core 3"
T3[Tâche 3: Calcul]
end
subgraph "CPU Core N..."
TN[Tâche N: Calcul]
end
pool --> T1
pool --> T2
pool --> T3
pool --> TN
T1 --> R[Collecte des résultats]
T2 --> R
T3 --> R
TN --> R
end
```
3. Exécutez le script via **le** bouton Run de PyCharm.
4. Le script va d'abord lancer la version séquentielle, puis la version parallèle, et enfin afficher un résumé. **Analysez le facteur d'accélération (Speedup).** Est-il proche du nombre de cœurs de votre processeur ?
---
## Conclusion et synthèse
Discutez des résultats que vous avez obtenus. Vous devriez maintenant être capable de répondre à la question fondamentale :
**"Mon programme est lent. Dois-je utiliser `asyncio` ou `multiprocessing` ?"**
| Type de Problème | Goulot d'étranglement | Solution | Mots-clés |
|:-----------------|:-------------------------------|:-------------------------------------|:------------------------------|
| **I/O-Bound** | Attente (Réseau, Disque, BDD) | **Asynchronisme (`asyncio`)** | `async`, `await`, `gather` |
| **CPU-Bound** | Calculs intensifs (CPU à 100%) | **Parallélisme (`multiprocessing`)** | `ProcessPoolExecutor`, `.map` |
Dans certains cas, une combinaison des deux approches peut être nécessaire, mais cela sort du cadre de cette introduction.
Vous avez maintenant exploré les deux piliers de la programmation performante en Python. N'hésitez pas à modifier les variables comme `NUM_TASKS` dans les scripts pour voir comment les temps d'exécution évoluent.
---
## Bonus : débogage avec PyCharm
Sur l'ensemble de la suite d'IDE JetBrains (WebStorm pour Javascript, PyCharm pour Python, etc.), le débogage est un outil puissant pour comprendre le flux d'exécution et inspecter les variables en temps réel.
1. Mettez un point d'arrêt (breakpoint) par exemple à la ligne 42 du fichier `1_sync_simulation.py`. Pour cela, cliquez dans la marge gauche à côté du numéro de ligne.
2. Lancez le mode débogage via le bouton Debug de PyCharm.
3. Utilisez les commandes de débogage pour exécuter le code pas à pas (Step Into My Code, etc.) et observer les valeurs des variables en temps réel

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import logging
import sys
import time
# --- Configuration ---
NUM_TASKS = 15
SLEEP_DURATION = 0.2 # Chaque tâche "attendra" 0.2 seconde
# --- Fonctions ---
def setup_logging():
"""Configure un logging simple pour la sortie console."""
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
)
def worker_task(task_id: int):
"""
Une fonction qui simule un travail bloquant (ex: une longue requête réseau).
time.sleep() gèle le thread et bloque l'exécution de tout autre code.
"""
logging.info(f"Tâche {task_id}: Démarrage...")
time.sleep(SLEEP_DURATION)
logging.info(f"Tâche {task_id}: Terminée.")
return f"Résultat de la tâche {task_id}"
def main():
"""Fonction principale du script synchrone."""
setup_logging()
logging.info("--- DÉMARRAGE DE LA SIMULATION SYNCHRONE ---")
start_time = time.perf_counter()
# Les tâches sont exécutées l'une après l'autre.
# La suivante ne commence que lorsque la précédente est terminée.
results = [worker_task(i) for i in range(NUM_TASKS)]
duration = time.perf_counter() - start_time
logging.info("-" * 50)
logging.info(f"Exécution SYNCHRONE terminée en {duration:.2f} secondes.")
expected_time = NUM_TASKS * SLEEP_DURATION
logging.info(f"Temps attendu : {NUM_TASKS} tâches * {SLEEP_DURATION}s = {expected_time:.2f}s.")
logging.info("Les temps d'attente se sont additionnés.")
logging.info("-" * 50)
if __name__ == "__main__":
main()

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import logging
import sys
import time
# --- Configuration ---
NUM_TASKS = 15
SLEEP_DURATION = 0.2 # Chaque tâche "attendra" 0.2 seconde
# --- Fonctions ---
def setup_logging():
"""Configure un logging simple pour la sortie console."""
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
)
def worker_task(task_id: int):
"""
Une fonction qui simule un travail bloquant (ex: une longue requête réseau).
time.sleep() gèle le thread et bloque l'exécution de tout autre code.
"""
logging.info(f"Tâche {task_id}: Démarrage...")
time.sleep(SLEEP_DURATION)
logging.info(f"Tâche {task_id}: Terminée.")
return f"Résultat de la tâche {task_id}"
def main():
"""Fonction principale du script synchrone."""
setup_logging()
logging.info("--- DÉMARRAGE DE LA SIMULATION SYNCHRONE ---")
start_time = time.perf_counter()
# Les tâches sont exécutées l'une après l'autre.
# La suivante ne commence que lorsque la précédente est terminée.
results = [worker_task(i) for i in range(NUM_TASKS)]
duration = time.perf_counter() - start_time
logging.info("-" * 50)
logging.info(f"Exécution SYNCHRONE terminée en {duration:.2f} secondes.")
expected_time = NUM_TASKS * SLEEP_DURATION
logging.info(f"Temps attendu : {NUM_TASKS} tâches * {SLEEP_DURATION}s = {expected_time:.2f}s.")
logging.info("Les temps d'attente se sont additionnés.")
logging.info("-" * 50)
if __name__ == "__main__":
main()

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import asyncio
import logging
import sys
import time
# --- Configuration ---
NUM_TASKS = 15
SLEEP_DURATION = 0.2 # Chaque tâche "attendra" 0.2 seconde
# --- Fonctions ---
def setup_logging():
"""Configure un logging simple pour la sortie console."""
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
)
# TODO 1 : remplacer "def" par "async def"
def worker_task_async(task_id: int):
"""
Une coroutine qui simule un travail non-bloquant.
await asyncio.sleep() ne gèle pas le programme. Il notifie la boucle
d'événements qu'elle peut exécuter autre chose pendant cette pause.
"""
logging.info(f"Tâche {task_id}: Démarrage...")
# TODO 2 : remplacer "time.sleep" par "await asyncio.sleep"
logging.info(f"Tâche {task_id}: Terminée.")
return f"Résultat de la tâche {task_id}"
async def main():
"""Fonction principale (coroutine) du script asynchrone."""
setup_logging()
logging.info("--- DÉMARRAGE DE LA SIMULATION ASYNCHRONE ---")
start_time = time.perf_counter()
# TODO 3 : créer une liste de tâches en appelant worker_task_async, puis utiliser asyncio.gather
duration = time.perf_counter() - start_time
logging.info("-" * 50)
logging.info(f"Exécution ASYNCHRONE terminée en {duration:.2f} secondes.")
logging.info(f"Temps attendu : proche de la durée d'une seule tâche ({SLEEP_DURATION}s).")
logging.info("Les temps d'attente ont été gérés en parallèle.")
logging.info("-" * 50)
if __name__ == "__main__":
asyncio.run(main())

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import logging
import multiprocessing
import sys
import time
from concurrent.futures import ProcessPoolExecutor
from typing import List, Tuple, Any, Callable
# --- CONFIGURATION ---
# Le nombre de tâches de calcul à simuler.
# Augmentez ce nombre pour mieux voir la différence de performance.
NUM_TASKS = 20
# --- LE WORKER (la tâche CPU-Bound) ---
def heavy_analysis(args: Tuple[int, int]) -> Tuple[int, float]:
"""
Une fonction qui simule un calcul scientifique ou une analyse de données complexe.
C'est une tâche purement "CPU-bound".
Elle prend un tuple en argument pour s'aligner sur le pattern utilisé dans le TP, que vous traiterez plus tard.
Paramètres:
args (Tuple[int, int]): Un tuple contenant:
- task_id (int): L'identifiant de la tâche.
- complexity (int): Un paramètre pour faire varier la durée du calcul.
Retourne:
Tuple[int, float]: Un tuple avec l'ID de la tâche et le résultat du calcul.
"""
task_id, complexity = args
logging.info(f"Tâche {task_id}: Démarrage du calcul (complexité={complexity})...")
# Simulation d'un calcul intensif : une boucle qui fait des opérations mathématiques.
result = 0
# La limite de la boucle dépend de la complexité pour que les tâches n'aient pas toutes la même durée.
limit = 2_000_000 + (complexity * 500_000)
for i in range(limit):
result += (i ** 0.5) / (i + 1) # Opération mathématique arbitraire
logging.info(f"Tâche {task_id}: Calcul terminé.")
return (task_id, result)
# --- LES LANCEURS ---
def run_sequential(worker_function: Callable, tasks: List[Any]) -> float:
"""Exécute les tâches de manière séquentielle."""
logging.info("--- DÉMARRAGE DU MODE SÉQUENTIEL ---")
start_time = time.perf_counter()
results = [worker_function(task) for task in tasks]
duration = time.perf_counter() - start_time
logging.info(f"--- MODE SÉQUENTIEL terminé en {duration:.2f} secondes. ---\n")
return duration
def run_parallel(worker_function: Callable, tasks: List[Any]) -> float:
"""Exécute les tâches en parallèle en utilisant tous les cœurs CPU disponibles."""
# On utilise tous les cœurs disponibles pour maximiser le parallélisme.
worker_count = multiprocessing.cpu_count()
logging.info(f"--- DÉMARRAGE DU MODE PARALLÈLE (avec {worker_count} processus) ---")
start_time = time.perf_counter()
# TODO 1 : instancier un ProcessPoolExecutor avec un nombre de workers donné, et en utilisant le gestionnaire de contexte `with`.
# TODO 2 : utiliser la méthode `map` de l'executor pour exécuter `worker_function` sur chaque tâche dans `tasks`.
duration = time.perf_counter() - start_time
logging.info(f"--- MODE PARALLÈLE terminé en {duration:.2f} secondes. ---\n")
return duration
# --- FONCTION PRINCIPALE ---
def main():
"""Orchestre la démonstration."""
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(processName)s - %(message)s",
handlers=[logging.StreamHandler(sys.stdout)],
)
# Création de la liste de tâches à exécuter.
# Chaque tâche est un tuple d'arguments pour notre fonction `heavy_analysis`.
# On fait varier la complexité pour simuler des données hétérogènes.
tasks_to_run = [(i, (i % 5) + 1) for i in range(NUM_TASKS)]
logging.info(f"{len(tasks_to_run)} tâches de calcul vont être lancées.")
# Lancement séquentiel
seq_duration = run_sequential(heavy_analysis, tasks_to_run)
# Lancement parallèle
par_duration = run_parallel(heavy_analysis, tasks_to_run)
# Résumé
logging.info("--- RÉSUMÉ DE LA COMPARAISON ---")
logging.info(f"Temps d'exécution séquentiel : {seq_duration:.2f}s")
logging.info(f"Temps d'exécution parallèle : {par_duration:.2f}s")
if par_duration > 0:
speedup = seq_duration / par_duration
logging.info(f"Facteur d'accélération (Speedup) : {speedup:.2f}x")
logging.info("----------------------------------")
if __name__ == "__main__":
# Nécessaire pour que multiprocessing fonctionne correctement sur certaines plateformes (Windows)
multiprocessing.freeze_support()
main()

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[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
propcache = ">=0.2.1"
[metadata]
lock-version = "2.1"
python-versions = "^3.13"
content-hash = "bfcb19653a47b3712dc1705d6b81313cccc184e99ff9e104f29f687f6e17e158"

26
pyproject.toml Normal file
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@@ -0,0 +1,26 @@
[project]
name = "demo-concurrence"
version = "0.1.0"
description = "Démonstration sur la concurrence en Python (AsyncIO et Multiprocessing)"
authors = [
{name = "Your Name", email = "you@example.com"}
]
readme = "README.md"
classifiers = [
"Programming Language :: Python :: 3.13",
]
keywords = ["asyncio", "concurrency", "multiprocessing"]
requires-python = ">=3.13"
[tool.poetry]
package-mode = false
[tool.poetry.dependencies]
python = "^3.13"
requests = "^2.31.0"
aiohttp = "^3.9.1"
aiofiles = "^23.2.1"
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"