# Python Joblib Module

Joblib is a set of tools to provide lightweight pipelining in Python. It is particularly optimized for fast cloning of objects (like large NumPy arrays) and easy parallel computing.

## Installation

```bash
pip install joblib
```

## Caching (Memoization)

Joblib provides a simple way to cache the results of functions to disk. This is useful for expensive computations.

```python
from joblib import Memory

location = './cachedir'
memory = Memory(location, verbose=0)

@memory.cache
def expensive_function(x):
    print(f"Computing {x}...")
    return x * x

print(expensive_function(2)) # Computes
print(expensive_function(2)) # Returns cached result
```

## Parallelism

Joblib provides a simple helper class `Parallel` to write parallel for loops using multiprocessing.

```python
from joblib import Parallel, delayed
import math

# A function to be executed in parallel
def sqrt_func(i):
    return math.sqrt(i)

# Run 2 jobs in parallel
results = Parallel(n_jobs=2)(delayed(sqrt_func)(i) for i in range(10))
print(results)
```

## Serialization

Joblib's `dump` and `load` functions are a replacement for `pickle` that work efficiently on arbitrary Python objects containing large data, particularly NumPy arrays.

```python
import joblib
import numpy as np

data = np.random.random((1000, 1000))

# Save to file
joblib.dump(data, 'data.joblib')

# Load from file
loaded_data = joblib.load('data.joblib')
```

[[programming/python/python]]