Python in Xcode 7 - Stack Overflow

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Python in Xcode 7 - Stack Overflow


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I figured it out! The steps make it look like it will take more effort than it actually does.

These instructions are for creating a project from scratch. If you have existing Python scripts that you wish to include in this project, you will obviously need to slightly deviate from these instructions.

If you find that these instructions no longer work or are unclear due to changes in Xcode updates, please let me know. I will make the necessary corrections.

Open Xcode. The instructions for either are the same.
In the menu bar, click “File” → “New” → “New Project…”.
Select “Other” in the left pane, then "External Build System" in the right page, and next click "Next".
Enter the product name, organization name, or organization identifier.
For the “Build Tool” field, type in /usr/local/bin/python3 for Python 3 or /usr/bin/python for Python 2 and then click “Next”. Note that this assumes you have the symbolic link (that is setup by default) that resolves to the Python executable. If you are unsure as to where your Python executables are, enter either of these commands into Terminal: which python3 and which python.
Click “Next”.
Choose where to save it and click “Create”.
In the menu bar, click “File” → “New” → “New File…”.
Select “Other” under “OS X”.
Select “Empty” and click “Next”.
Navigate to the project folder (it will not work, otherwise), enter the name of the Python file (including the “.py” extension), and click “Create”.
In the menu bar, click “Product” → “Scheme” → “Edit Scheme…”.
Click “Run” in the left pane.
In the “Info” tab, click the “Executable” field and then click “Other…”.
Navigate to the executable from Step 6. You may need to use ⇧⌘G to type in the directory if it is hidden.
Select the executable and click "Choose".
Uncheck “Debug executable”. If you skip this step, Xcode will try to debug the Python executable itself. I am unaware of a way to integrate an external debugging tool into Xcode.
Click the “+” icon under “Arguments Passed On Launch”. You might have to expand that section by clicking on the triangle pointing to the right.
Type in $(SRCROOT)/ (or $(SOURCE_ROOT)/) and then the name of the Python file you want to test. Remember, the Python program must be in the project folder. Otherwise, you will have to type out the full path (or relative path if it's in a subfolder of the project folder) here. If there are spaces anywhere in the full path, you must include quotation marks at the beginning and end of this.
Click “Close”.
Note that if you open the "Utilities" panel, with the "Show the File inspector" tab active, the file type is automatically set to "Default - Python script". Feel free to look through all the file type options it has, to gain an idea as to what all it is capable of doing. The method above can be applied to any interpreted language. As of right now, I have yet to figure out exactly how to get it to work with Java; then again, I haven't done too much research. Surely there is some documentation floating around on the web about all of this.

Running without administrative privileges:

If you do not have administrative privileges or are not in the Developer group, you can still use Xcode for Python programming (but you still won't be able to develop in languages that require compiling). Instead of using the play button, in the menu bar, click "Product" → "Perform Action" → "Run Without Building" or simply use the keyboard shortcut ^⌘R.

Other Notes:

To change the text encoding, line endings, and/or indentation settings, open the "Utilities" panel and click "Show the File inspector" tab active. There, you will find these settings.

For more information about Xcode's build settings, there is no better source than this. I'd be interested in hearing from somebody who got this to work with unsupported compiled languages. This process should work for any other interpreted language. Just be sure to change Step 6 and Step 16 accordingly.

A Celery-like Python Task Queue in 55 Lines of Code: A Celery-like Python Task Queue in 55 Lines of Code

A Celery-like Python Task Queue in 55 Lines of Code

Update: you can check this out on GitHub here.

Celery is probably the best known task queuing Python package around. It makes asynchronous execution of Python code both possible and reasonably straightforward. It does, however, come with a good deal of complexity, and it's not as simple to use as I would like (i.e. for many use cases it's overkill). So I wrote a distributed Python task queue. In 55 lines of code (caveat: using two awesome libraries).

Background

What does a distributed task queue do? It takes code like the following (taken from the documentation forRQ, another Celery alternative):

import requests

def count_words_in_page(url):
    resp = requests.get(url)
    return len(resp.text.split())

and allows it to be sent to a worker process (possibly on another machine) for execution. The worker process then sends back the results after the calculation is complete. In the meantime, the sender doesn't have to block waiting for the (possibly expensive) calculation to complete. They can just periodically check if the results are ready.

So what's the absolute simplest way we could do this? I submit to you, brokest.py:

"""Broker-less distributed task queue."""
import pickle

import zmq
import cloud

HOST = '127.0.0.1'
PORT = 9090
TASK_SOCKET = zmq.Context().socket(zmq.REQ)
TASK_SOCKET.connect('tcp://{}:{}'.format(HOST, PORT))

class Worker(object):
    """A remote task executor."""

    def __init__(self, host=HOST, port=PORT):
        """Initialize worker."""
        self.host = host
        self.port = port
        self._context = zmq.Context()
        self._socket = self._context.socket(zmq.REP)

    def start(self):
        """Start listening for tasks."""
        self._socket.bind('tcp://{}:{}'.format(self.host, self.port))
        while True:
            runnable_string = self._socket.recv_pyobj()
            runnable = pickle.loads(runnable_string)
            self._socket.send_pyobj('')
            args = self._socket.recv_pyobj()
            self._socket.send_pyobj('')
            kwargs = self._socket.recv_pyobj()
            response = self._do_work(runnable, args, kwargs)
            self._socket.send_pyobj(response)

    def _do_work(self, task, args, kwargs):
        """Return the result of executing the given task."""
        print('Running [{}] with args [{}] and kwargs [{}]'.format(
            task, args, kwargs))
        return task(*args, **kwargs)

def queue(runnable, *args, **kwargs):
    """Return the result of running the task *runnable* with the given 
    arguments."""
    runnable_string = cloud.serialization.cloudpickle.dumps(runnable)
    TASK_SOCKET.send_pyobj(runnable_string)
    TASK_SOCKET.recv()
    TASK_SOCKET.send_pyobj(args)
    TASK_SOCKET.recv()
    TASK_SOCKET.send_pyobj(kwargs)
    results = TASK_SOCKET.recv_pyobj()
    return results

if __name__ == '__main__':
    w = Worker()
    w.start()

And to use it? Here's the complete contents of app.py:

import requests
from brokest import queue

def count_words_in_page(url):
    resp = requests.get(url)
    return len(resp.text.split())

result = queue(count_words_in_page, 'http://www.jeffknupp.com')
print result

Rather than calling the function directly, you simply call brokest.queue with the function and it arguments as arguments. While the current implementation is blocking, it would be trivially easy to make it non-blocking. Adding multiple workers is just a matter of adding code to make use of a config file with their locations.

Clearly, the stars here are zmq and cloud. ZeroMQ makes creating distributed systems a lot easier, and the documentation is chock full of ZeroMQ design patterns (ours is probably the simplest one, Request-Reply).

cloud is the LGPL'd PiCloud library. PiCloud is a company whose value proposition is that they let you seamlessly run computationally intensive Python code using Amazon EC2 for computing resources. Part of making that a reality, though, required a way to pickle functions and their dependencies (functions are not normally directly pickle-able). In our example, the Worker is able to make use of code using requestslibrary despite not having imported it. It's the secret sauce that makes this all possible.

Is This For Real?

The code works, but my intention was not to create a production quality distributed task queue. Rather, it was to show how new libraries are making it easier than ever to create distributed systems. Having a way to pickle code objects and their dependencies is a huge win, and I'm angry I hadn't heard of PiCloud earlier.

One of the best things about being a programmer is the ability to tinker not just with things, but with ideas. I can take existing ideas and tweak them, or combine existing ideas in new ways. I think brokest is an interesting example of how easy it has become to create distributed systems.

Posted on Feb 11, 2014 by Jeff Knupp

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