Parakeet is a runtime compiler for scientific computing in Python which uses type inference, data parallel array operators, and a lot of black magic to make your code run faster.

from parakeet import jit

@jit 
def allpairs_dist(X,Y):
  """
  Compute all pairs distances between rows of X and Y
  For two 1000x1000 float64 inputs (on a 2.67ghz Xeon): 
  - Python runtime: 11.5 seconds
  - Parakeet runtime: .013 seconds 
  """
  def dist(x,y):
    return np.sum( (x-y)**2 )
  return np.array([[dist(x,y) for y in Y] for x in X])

@jit 
def smoothing(x, alpha):
  """
  Exponential smoothing of a time series
  For x = 10**6 floats
  - Python runtime: 9.7 seconds
  - Parakeet runtime: .009 seconds
  """
  s = x.copy()
  for i in xrange(1, len(x)):
    s[i] = alpha * x[i] + (1 - alpha) * s[i-1]
  return s
Installing: You can install Parakeet using pip by running pip install parakeet or you can download the source directly from github. Parakeet is written for Python 2.7 and requires NumPy 1.7+.
Discussion Group: If you have any questions, suggestions, or bug reports, please post them on the Parakeet Google Group.
Limitations: It's important to note that Parakeet is not a general purpose Python compiler (and trying to compile an arbitrary Python function will almost surely fail). The subset of Python supported by Parakeet is only useful for expressing numerical computations, primarily those done on dense NumPy arrays.

A non-exhaustive list of Parakeet's limitations and deviations from Python's semantics:
More Info: More details will about the design, implementation, and usage of Parakeet will soon be posted here. For now, the are a few resources online for learning more about Parakeet: