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• South migration error: NoMigrations exception for django.contrib.auth

  - by danpalmer

  I have been using South on my project for a while, but I recently did a huge amount of development and changed development machine and I think something messed up in the process. The project works fine, but I can't apply migrations. Whenever I try to apply a migration I get the following traceback: danpalmer:pest Dan$ python manage.py migrate frontend Traceback (most recent call last): File "manage.py", line 11, in <module> execute_manager(settings) File "/Library/Python/2.6/site-packages/django/core/management/__init__.py", line 362, in execute_manager utility.execute() File "/Library/Python/2.6/site-packages/django/core/management/__init__.py", line 303, in execute self.fetch_command(subcommand).run_from_argv(self.argv) File "/Library/Python/2.6/site-packages/django/core/management/base.py", line 195, in run_from_argv self.execute(*args, **options.__dict__) File "/Library/Python/2.6/site-packages/django/core/management/base.py", line 222, in execute output = self.handle(*args, **options) File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/management/commands/migrate.py", line 102, in handle delete_ghosts = delete_ghosts, File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/migration/__init__.py", line 182, in migrate_app applied = check_migration_histories(applied, delete_ghosts) File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/migration/__init__.py", line 85, in check_migration_histories m = h.get_migration() File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/models.py", line 34, in get_migration return self.get_migrations().migration(self.migration) File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/models.py", line 31, in get_migrations return Migrations(self.app_name) File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/migration/base.py", line 60, in __call__ self.instances[app_label] = super(MigrationsMetaclass, self).__call__(app_label_to_app_module(app_label), **kwds) File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/migration/base.py", line 88, in __init__ self.set_application(application, force_creation, verbose_creation) File "/Library/Python/2.6/site-packages/South-0.7-py2.6.egg/south/migration/base.py", line 159, in set_application raise exceptions.NoMigrations(application) south.exceptions.NoMigrations: Application '<module 'django.contrib.auth' from '/Library/Python/2.6/site-packages/django/contrib/auth/__init__.pyc'>' has no migrations. I am not that experienced with South and I haven't met this error before. The only helpful mention I can find online about this error is for pre-0.7 I think and I am on South 0.7. I ran 'easy_install -U South' just to make sure. Thanks for any help that you can provide. I really appreciate it.

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• Java: Read POST data from a socket on an HTTP server

  - by danpalmer

  I have a website (python/django) that needs to use a load of Java resources that may or may not be on the same server. Therefore I am writing a mini webserver in Java that will receive a request and then when processing is finished, POST some data back to a url on the site. I have got the java code receiving connections on sockets and responding with some simple HTML. My problem is that I will POST data to the Java server and that code needs to act on the data. How do I go about reading the data that is posted in the HTML request, if it is even possible. If not, is there any other way you would do this. If you think I am going about this in completely the wrong way then please tell me and I will consider another method, but after conversing with some Java developers, this seemed like the best way for what I was doing. Thanks

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• Keeping files or database records? Java and Python

  - by danpalmer

  My website will use a Neural Network to predict thing based on user data. The user can select the data to be used in training the network and then use their trained network to predict things. I am using a framework to create, train and query the networks. This uses Java. The framework has persistence for saving a network to an XML file. What is the best way to store these files? I can see several potential ideas, but I need help on choosing which is best: Save each network to a separate XML file with a name that is stored in the database. Load this each time. Save all the networks to the same XML file with each network having a different name that is stored in the database. Somehow pass what would normally be written to an XML file to the Django site for writing to the database. This would need to be returned to the Java code when a prediction needs to be made. I am able to do 1 or 2, but I think their performance will be quite limited and I am on shared hosting at the moment, so I don't know how pleased they would be with thousands of files. Also, after adding a few thousand records to one XML file, I was noticing a massive performance hit on saving to it. If I were able to implement version 3 somehow I think it would be best. No issues with separate processes accessing the database and I think performance would be better. Not to mention having no files lying around. However, the stuff in the neural network framework I am using (Encog) for saving to a file needs access to a Java file object, not a string that could be saved to a database. Unless there is some Java magic I can do here (I know very little Java), the only way I can see of doing this would be with a temporary files but I don't know if this is the correct way to do it. I would appreciate any ideas on the best way to implement any of the above 3 ideas or any alternatives. Thanks!

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• Neural Network settings for fast training

  - by danpalmer

  I am creating a tool for predicting the time and cost of software projects based on past data. The tool uses a neural network to do this and so far, the results are promising, but I think I can do a lot more optimisation just by changing the properties of the network. There don't seem to be any rules or even many best-practices when it comes to these settings so if anyone with experience could help me I would greatly appreciate it. The input data is made up of a series of integers that could go up as high as the user wants to go, but most will be under 100,000 I would have thought. Some will be as low as 1. They are details like number of people on a project and the cost of a project, as well as details about database entities and use cases. There are 10 inputs in total and 2 outputs (the time and cost). I am using Resilient Propagation to train the network. Currently it has: 10 input nodes, 1 hidden layer with 5 nodes and 2 output nodes. I am training to get under a 5% error rate. The algorithm must run on a webserver so I have put in a measure to stop training when it looks like it isn't going anywhere. This is set to 10,000 training iterations. Currently, when I try to train it with some data that is a bit varied, but well within the limits of what we expect users to put into it, it takes a long time to train, hitting the 10,000 iteration limit over and over again. This is the first time I have used a neural network and I don't really know what to expect. If you could give me some hints on what sort of settings I should be using for the network and for the iteration limit I would greatly appreciate it. Thank you!

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• S3 Backup Memory Usage in Python

  - by danpalmer

  I currently use WebFaction for my hosting with the basic package that gives us 80MB of RAM. This is more than adequate for our needs at the moment, apart from our backups. We do our own backups to S3 once a day. The backup process is this: dump the database, tar.gz all the files into one backup named with the correct date of the backup, upload to S3 using the python library provided by Amazon. Unfortunately, it appears (although I don't know this for certain) that either my code for reading the file or the S3 code is loading the entire file in to memory. As the file is approximately 320MB (for today's backup) it is using about 320MB just for the backup. This causes WebFaction to quit all our processes meaning the backup doesn't happen and our site goes down. So this is the question: Is there any way to not load the whole file in to memory, or are there any other python S3 libraries that are much better with RAM usage. Ideally it needs to be about 60MB at the most! If this can't be done, how can I split the file and upload separate parts? Thanks for your help. This is the section of code (in my backup script) that caused the processes to be quit: filedata = open(filename, 'rb').read() content_type = mimetypes.guess_type(filename)[0] if not content_type: content_type = 'text/plain' print 'Uploading to S3...' response = connection.put(BUCKET_NAME, 'daily/%s' % filename, S3.S3Object(filedata), {'x-amz-acl': 'public-read', 'Content-Type': content_type})

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• Deploying a Java application. How?

  - by danpalmer

  I am new to Java (and Eclipse) but I have used .NET (and Visual Studio) a fair amount. I also know about compiling C/C++ code and things like that. I know that at the end I get either an EXE or a nice binary file that can be run from the command line. I have been making a Java utility that uses some external libraries. I need to compile this into an executable that I can run from the command line on a unix machine, but I cannot find any way to do this. I can build and run/debug in Eclipse, but that is no use to me as this code will be run on a webserver. I just need all the dependancies compiled in to one file but after hours of searching on Google, the best thing I could find was the Fat-JAR plugin for Eclipse and after using that I just get the following error when I try to run the file: Exception in thread "main" java.lang.NoClassDefFoundError: Network/jar This is really confusing me and as it is such an essential thing to be able to do I am sure I must be missing something blindingly obvious, but as I said, after hours of searching I have gotten nowhere. I really appreciate any help you can give me. Thanks.

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