Showing posts with label software. Show all posts
Showing posts with label software. Show all posts

Launching Jupyter notebook server on startup

Posted in Labels: , , , | at 11:00

In this post, I show how to automatically run the Jupyter notebook server on an OSX machine on startup, without using extra terminal windows.

Background

Jupyter notebooks provide a useful interactive environment (think better alternative for console) for scripting languages like Python, Julia, Lua/torch, which has become a favourite tool of many data scientists for handling datasets that fit into the laptop’s memory. If you have not used it, try it next time you do data analysis or prototype an algorithm.

The Pain

There is a slight technical difficulty, though. The way Jupyter works is you run a server process on your machine (or somewhere in the network), and then access it through a web client. I have noticed a popular pattern of usage in the local-server case: a user runs the command to start the server process in the terminal then keeps the terminal window open while she is using the notebook. This method has several flaws:

  • you need to run the process manually each time you need a notebook (after reboot);
  • you have to remember the command line arguments;
  • the process takes up a terminal window/tab, or, if you run it in the background, you can accidentally close the tab and your RAM state will be lost.

The Solution

If your perfectionist’s feelings are hurt by those nuisances above, my setup may remedy it. It uses launchd framework that starts, stops, and manages background processes. 

First, create a property list file in your ~/Library/LaunchAgents directory: 

~/Library/LaunchAgents/com.blippar.jupyter-ipython.plist

and put the following text there. This is a custom format, which is not quite XML (e.g. order of children nodes apparently matters). I highlighted red the paths and other variables you will have to change:

<?xml version="1.0" encoding="UTF-8"?>
 <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
  "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
      <plist version="1.0">
      <dict>
          <key>KeepAlive</key>
          <true/>
          <key>Label</key>
          <string>com.blippar.jupyter-ipython</string>
          <key>ProgramArguments</key>
          <array>
              <string>zsh</string>
              <string>-c</string>
              <string>source ~/.zshrc;workon py3;jupyter notebook --notebook-dir /Users/roman/jupyter-nb --no-browser</string>
          </array>
          <key>RunAtLoad</key>
          <true/>
          <key>StandardErrorPath</key>
          <string>/Users/roman/Library/LaunchAgents/jupyter-notebook.stderr</string>
          <key>StandardOutPath</key>
          <string>/Users/roman/Library/LaunchAgents/jupyter-notebook.stdout</string>
      </dict>
      </plist>
     
The most interesting part is the ProgramArguments entry. As it is impossible to specify several commands as an argument, I had to run a separate shell instance and pass the commands separated by semicolons. If you do not need to set the virtual environment (workon py3), you might get away without calling the shell, although I recommend always using virtualenv for python on a local machine. Also, I use zsh; replace it with your shell of choice.

To check if everything works, run
launchctl load com.blippar.jupyter-ipython
launchctl start com.blippar.jupyter-ipython

and open http://localhost:8888/tree in your browser. Hopefully, you see the list of available notebooks.

Linux users, you should be able to replicate this with systemd (although, chances are you already know that =).

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Comic strip: Nerd's confession

Posted in Labels: , , , , , | at 22:21

I continue sharing my Prague comic strips, since I have not been posting for a while. In fact, I was busy writing a paper, so this one may seem somehow connected. Disclaimer: There's nothing personal in the strip. Kids under 18 are not allowed. :)

The next post is going to be about multimedia technologies for art. Stay tuned!

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LidarK 2.0 released

Posted in Labels: , , , , , , | at 16:39

The second major release of GML LidarK is now available. It reflects our 3-year experience on 3D data processing. The description from the project page:

The LidarK library provides an open-source framework for processing multidimensional point data such as 3D LIDAR scans. It allows building a spatial index for performing fast search queries of different kinds. Although it is intended to be used for LIDAR scans, it can be helpful for a wide range of problems that require spatial data processing.

The API has been enriched with various features in this release. Indeed, it became more consistent and logical. New ways to access data (i.e. various iterators) are implemented. One can now find k nearest neighbours for any point, not just for one that belongs to the index. Since the data structure is a container, we've decided to parametrize it with template parameter. This decision is controversive: one does not need to cast tuples any more, but the code became clumsier and less portable, unfortunately.

The C++/MATLAB code is licensed free of charge for academic use. You can download it here.

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Comic strip: 10%

Posted in Labels: , , , , , , | at 22:49

Last summer, during my internship at CMP in Prague I drew some xkcd-style comic strips, and I want to share some of them here. I considered starting a different blog for that, but there are too few of them, and they do suck (at least in comparison to Randall's work). However, most of them are programming/research related, so they are appropriate here.

I planned to use them when I would have nothing to post about. This is not the case now (I am planning a couple more posts in a week or so), but this one is topical. It was originally drawn in September 1, 2009 when GMail was not operating for a few hours. From the recent news: Google shuts Google Wave down, admitting their failure predicted by some sceptics (see the image title text).

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Mendeley news

Posted in Labels: , , , , , | at 11:53

Few months ago I posted about Mendeley, the reference management system. I've been using it all the time and I am really satisfied. It is much more convenient to have your papers on a server than on a flash drive. Also keeping meta-information about the papers makes dealing with them (exploring, reading, citing etc.) more pleasant.

In this month a major update was released and a monetizing scheme was introduced. There are now 3 tariff plans in addition to the free one that allows one to use up to 1Gb of server disk space. There are also restrictions on the max number of shared collections and the number of users per collection. It does not look critical after all: there are no features you cannot use in the free version, so the company seems not evil. However, on the sister project last.fm the radio feature became paid after years of free service, which caused many users drift to grooveshark.

The update has affected the design of the paper info right-hand bar, and the references bar was eliminated. They promise to rebirth the bar in the future releases. It was really unusable before. When you read a paper, it is handy to have the list of indexed references on the bar, especially if the reference-style citing is used (it is the major problem with reading papers from the screen: where the hell [42] is referencing to?) I hope the people in Mendeley realize it.

It would be great to have a possibility to find papers without leaving the application. In theory, you could drag a citation from the references bar to a collection, find its (more) precise details via Internet search, and you are likely to have the link, and if it is direct, you can add the pdf with the Add File dialog using the found URL. In practice, the found URLs usually are not direct, they refer to IEEE/ACM/Springer pages, where the paper could be downloaded (usually not freely). In the same time, the paper is likely to be available for free through the direct link on the author's homepage. Moreover, Google Scholar often finds them too, but Mendeley chooses indirect links.

Mendeley needs to be more "semantic" while working with authors and conferences. It stores the author name as it appears in the paper. When you want to filter your papers by author, you can see "Shapovalov, R", "Shapovalov, R.", "Shapovalov, Roman", "Shapovalov, Roman V." etc. in the list. There are bases like DBLP, Mendeley can build their own index, so the author should be identified. If we know the author (but not only her name appearance in the paper), we are able to filter Google Scholar results and retrieve the direct link to the paper hosted on the author/university site. Finally, it is feasible to create the system, where human don't need to find papers. If I see the reference, I can automatically retrieve the pdf in a few seconds. I want Mendeley to develop in this direction.

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Max-product optimization methods and software

Posted in Labels: , , , , , , , , , | at 22:38

I hoped I would never post a sorry-for-not-posting message, but I do (and now I feel like I owe you a long post =). In fact, I have been being really busy, particularly with putting my masters thesis down to paper. However, it has a positive outcome: I systematized my knowledge about inference techniques in MRFs, and I'd like to share them here.

Please don't consider it a survey. While it claims to be quite comprehensive, it is not enough deep and verbose. You are not gonna see a lot of formulas, but the links to papers and implementations along with the recommendations on applicability of the methods and the software. It could be useful for anybody who wants to quickly try energy minimization in her task, but don't wanna spend a lot of time digging into the literature and implementing algorithms. So let's call this form of posting unsurvey. You can always find more details on the topic in Stan Li's book (not to be confused with Stan Lee recently appeared in The Big Bang Theory).

Max-product energy minimization problem arises in a number of fields such as statistical physics, social network analysis, and, of course, computer vision, where the problems like image denoising, dense stereo reconstruction, semantic segmentation lead to the single discrete optimization problem: maximize the product of some functions defined over nodes and edges of the graph G=(N,E), typically a grid over image pixels in low-level vision. Due to using logarithms, it is equivalent to maximizing the sum


where each Yi corresponds to a node of the graph and takes on a value from a fixed discrete range. It could be a class label in semantic segmentation, or a disparity value in the pixel in dense stereo. So-called potential functions φ (unary in the first term and pairwise in the second one) define possibility of the assignment given class labels to the nodes and edges correspondingly. They could be conditioned by the data. For example, unary potentials could reflect colour information in the pixel, and pairwise potentials could enforce assignment of the similar labels to the ends of an edge to provide smoothness in the resulting labelling. If the graph contains cliques of the order 3 and greater, one should consider more terms (as I explained earlier), but in practice higher order cliques are often ignored. Also, an important particular case, 4-connected grid, contains only second order cliques, that's why the research community focused on this "pairwise" problem.

In general, this maximization problem is NP-hard. That's why in the 1980s they solved it using genetic algorithms or simulated annealing. Fortunately, specific methods have been developed.

First of all, there are two cases when the exact global optimum could be found in polynomial time. The first is when the graph has a tree structure. Actually, I don't know application where this case is useful. Probably, it is good for analysis of parsing trees in natural language processing. In this case the maximum is found using the dynamic programming based belief propagation algorithm. One node is selected as the root, and messages are passed from the root and then back to the root. The optimal assignment is thus found.

Another polynomial case allows an arbitrary graph topology, but restricts the number of labels and the form of pairwise potentials. Only one of the two labels could be assigned to each node. Pairwise potentials should be submodular, i.e. φ(0,0) + φ(1,1) >= φ(0,1) + φ(1,0). Thus, the binary assignment implies that all the nodes should be divided into the two groups according to their labels. This division is found using the algorithm for finding minimum cut on the supplementary graph. Two fake nodes are added to the graph and declared as the source and the sink. Both fake nodes are adjacent to all the nodes of the "old" graph. Using your favourite min-cut/max-flow algorithm you can separate the nodes into two sets, which gives you the resulting labelling.

Unfortunately, the binary labelling problem is not so common too. In practice people want to choose one of the multiple labels for the nodes. This problem is NP-hard, but the iterative procedures of α-expansion and αβ-swap give good approximations with certain theoretical guarantees. [Boykov et al., 2001] During each iteration the min-cut algorithm is run, so there is an important constraint. Each projection to any two variables of pairwise potentials should be submodular. If it is hold for your energy, I recommend you to use this method since it is really fast and accurate. The implementation by Olga Veksler is available.

What should we do if the energy is not submodular and the graph contains cycles? There are some methods too, but they are not so fast and typically not so effective. First of all, the stated integer programming problem could be relaxed to linear programming. You can use any LP solver, but it will eventually take days to find the optimum. Moreover, it won't be exact, because the relaxed problem solution could be rounded back in multiple ways. No surprise here, because the possibility of getting the global optimum would prove that P = NP.

One of the methods for approaching the general problem is Loopy Belief Propagation [Kschischang, 2001]. No doubt, it is really loopy. Dynamic programming is used on the graph with loops, and it can eventually fall into the endless loop passing some messages along the loop. It is now considered outdated, but you can still try it, it is implemented in libDAI. The input is a factor graph, which is inconvenient in most cases, but really flexible (you could create cliques of arbitrary order).

State-of-the-art approach in general case, tree-reweighted message passing (TRW), has been developed by Wainwright and Kolmogorov. [Kolmogorov, 2006] It is based on the decomposition of the original graph to some graphs where exact message-passing inference is possible, i.e. trees. Message passing is done iteratively on the trees, and trees are enforced to assign the same values in particular nodes. The procedure is guaranteed to converge, but unfortunately not always to the global maximum. However, it is better than Loopy BP. Kolmogorov's implementation contains LBP and TRW-S under a common interface, so you can compare their performance. To understand the decomposition technique better, you could read Komodakis's paper [2007]. It describes a general decomposition framework.

Once a company of the world-leading MRF researchers gathered and implemented LBP, graph-cuts and TRW-S under common interface, investigated performance and even shared their code on a Middlebury college page. It would be great to use those implementations interchangeably, but unfortunately the interface is tailored for the stereo problem, i.e. it is impossible to use the general form of potentials. My latest research is about usage MRFs for laser scan classification, where the topology of the graph is far from a rectangular grid, and the potentials are more complex than the ones in the Potts model. So I ended up in writing my own wrappers.

Another interesting method was re-discovered by Kolmogorov and Rother [2007]. It is known as quadratic pseudo-boolean optimization (QPBO). This graph-cut based method can either assign a label to a node, or reject to classify it. The consistency property is guaranteed: if the label has been assigned, this exact label is assigned in the global optimum. The equivalent solution could be obtained using a specific LP relaxation with 1/2 probabilities for class labels allowed. I have not used the technique, so I cannot recommend an implementation.

To summarize, the algorithm for choosing an optimization method is the following. If your graph is actually a tree, use belief propagation. Otherwise, if your energy is submodular, use graph-cut based inference; it is both effective and efficient (even exact in the binary case). If not, use TRW-S, it often yields a good approximation.

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OpenCV bindings

Posted in Labels: , , , , , , , , , , , , , | at 23:27

If you are a computer vision researcher or an engineer, you cannot miss OpenCV library. Even if you are not, it could be useful to you. For example, here is a funny application: camera shots a programmer's face when a merge fails. If you are not familiar with the library, I recommend you to look through the list of its features on Wikipedia.


OpenCV is written in C to be extremely portable (for example, to DSP). The fact is C is not very popular nowadays. The recent release 2.0 contains (besides the other decent stuff) also C++ and Python wrappers. What about the other languages?

There are a number of C# wrappers. The most known is EmguCV, which is reported to be the only C# wrapper that supports OpenCV 2.0 (actually, I don't now what it means, but I suppose the API should correspond the C++ interface). It is distributed under GPL or the "Commercial License with a small fee".

As for Java, JavaCV seems to be the only viable wrapper. It also contains wrappers for other popular libraries like FFmpeg. It is also distributed under GPL, but the author promised to discuss weakening it if needed.

OpenCV was being supported by Intel, but it became a FOSS project recently. They are also going to participate in Google Summer of Code. If they will succeed, you might try to apply. I think it is a nice experience to develop such a popular library and be paid for it. :)

One of the fields they want to develop is augmented reality support for Android operating system. When I get known that there is an AR API in Android, I decided to try it. So, this is going to be a good opportunity!

UPD (Aug 6, 2011). There appeared a Haskell (!) wrapper for OpenCV by Noam Lewis.
Also, OpenCV folks are developing the official Java wrapper. Looking forward to use it!

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Mendeley: the reference management software

Posted in Labels: , , | at 14:55

Yesterday, a friend of mine Dmitry Konstantinov told me about Mendeley. His problem was he had got a lot of papers within his computer and needed to handle them efficiently. After digging the Internet, he found Mendeley reference management system. The system allows you to sort papers, extract the title, abstract, authors, and references from textual PDFs, automatically retrieve meta-information about them from services like CiteSeerx, annotate papers and even their paragraphs, tag papers, flag them as read/favourite etc. Full text search across all your library is very convenient too.


The project is based in London, and share some management with Last.fm. Unsurprisingly, they have a social network upon all that. You can share you paper collections, upload your articles, synchronize your library with Mendeley Web. A user has up to 500 Mb space on servers. Now I see, where the idea of SciPeople, Russian scientific network, comes from. I don't know if such networks are the future of science, but they are, however, worth paying attention.

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