Building Machine Learning Systems with Python - Richert, Coelho

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Chapter 12 We need to create a new ssh key with the following: starcluster createkey mykey -o .ssh/mykey.rsa Now that we have configured a 16 node cluster and set up the keys, let's try it out. starcluster start mycluster This may take a few minutes as it allocates 17 new machines. Why 17 when our cluster is only 16 nodes? We always have a master node. All of these nodes have the same filesystem, so anything we create on the master will also be seen by the slaves. This also means that we can use jug on these clusters. These clusters can be used as you wish, but they come pre-equipped with a job queue engine, which makes them ideal for batch processing. The process of using them is simple: 1. You log in to the master node. 2. You prepare your scripts on the master (or better yet, have them prepared before hand). 3. You submit jobs to the queue. A job can be any Unix command. The scheduler will find free nodes and run your job. 4. You wait for the jobs to finish. 5. You read the results on the master node. You can also now kill all the slave nodes to save money. In any case, do not forget that your system is running over the long term. Otherwise, this will cost you (in the dollars and cents meaning of the word). We can log in to the master node with a single command: starcluster sshmaster mycluster We could also have looked up the address of the machine that was generated and used an ssh command as we did earlier, but when using the preceding command, it does not matter what the address was, as starcluster takes care of it behind the scenes for us. As we said earlier, starcluster provides a batch queuing system for its clusters; you write a script to perform your actions, put it on the queue, and it will run in any available node. At this point, you will need to repeat the actions to install the needed packages on the cluster. If this was a real project, we would set up a script to perform all the initialization for us, but since it is a tutorial, you should just run the installation steps again. [ 257 ]
Big(ger) Data We can use the same jugfile.py system as before, except that now, instead of running it directly on the master, we schedule it on the cluster. First, write a very simple wrapper script: #!/usr/bin/env bash jug execute jugfile.py Call it using run-jugfile.sh and use chmod +x run-jugfile.sh to give it an executable permission: For c in 'seq 16'; do qsub run-jugfile.sh; done This will create 16 jobs, each of which will run the run-jugfile.sh script, which will simply call jug. You can still use the master as you wish. In particular, you can at any moment run jug status and see the status of the computation. In fact, jug was developed in exactly such an environment, so it works very well in it. Eventually, the computation will be finished and we can kill off all the nodes. Be sure to save the desired results somewhere and run the following: starcluster terminate mycluster Note that terminating will really destroy the filesystem and all your results. Of course, it is possible to change this default. You can have the cluster write to a filesystem which is not allocated and destroyed by starcluster but is available to you on a regular instance; in fact the flexibility of these tools is immense. However, these advanced manipulations could not all fit in this chapter. Starcluster has excellent documentation online at http://star.mit.edu/cluster/, which you should read for more information about all the possibilities of this tool. We have seen only a small fraction of the functionality and used only the default settings here. [ 258 ]
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Building Machine Learning Systems w
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Credits Authors Willi Richert Luis
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Luis Pedro Coelho is a Computationa
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Maurice HT Ling completed his PhD.
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Table of Contents Preface 1 Chapter
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Table of Contents Tuning the instan
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Table of Contents Improving classif
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Preface You could argue that it is
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Preface What you need for this book
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Downloading the example code You ca
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Getting Started with Python Machine
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Learning How to Classify with Real-
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Chapter 2 We are using Matplotlib;
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Chapter 2 The last few lines select
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Chapter 2 error = 0.0 for ei in ran
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Chapter 2 We can play around with t
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Chapter 2 Features and feature engi
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Chapter 2 In the preceding screensh
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Chapter 2 Binary and multiclass cla
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Clustering - Finding Related Posts
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Chapter 3 How to do it More robust
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Chapter 3 This means that the first
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Chapter 3 ... post = posts[i] ... i
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Chapter 3 If you have a clear pictu
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Chapter 3 Extending the vectorizer
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Chapter 3 0.0 >>> print(tfidf("b",
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Chapter 3 Flat clustering divides t
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Because the cluster centers are mov
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Chapter 3 'D:\\data\\379\\raw\\comp
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As we have learned previously, we w
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Chapter 3 Position Similarity Excer
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Chapter 3 But before you go there,
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Topic Modeling For those who are in
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Topic Modeling Sparsity means that
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Topic Modeling Although daunting at
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Topic Modeling … for tj,v in t:
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Topic Modeling Finally, we build th
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Topic Modeling Alternatively, we ca
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Topic Modeling Topic modeling was f
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Classification - Detecting Poor Ans
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Classification II - Sentiment Analy
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Chapter 6 Getting to know the Bayes
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Using Naive Bayes to classify Given
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Chapter 6 This denotation "" leads
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Chapter 6 Similarly, we do this for
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Chapter 6 A quick look at the previ
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Chapter 6 To keep our experimentati
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Chapter 6 Y = np.zeros(Y.shape[0])
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Chapter 6 ° ° Experiment with whe
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Chapter 6 We have to be patient whe
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Chapter 6 First, we define a range
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Chapter 6 Determining the word type
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Chapter 6 Successfully cheating usi
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Chapter 6 Our first estimator Now w
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Chapter 6 for d in documents: allca
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Regression - Recommendations You ha
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Chapter 7 The preceding graph shows
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Chapter 7 Root mean squared error a
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Penalized regression The important
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Chapter 7 P greater than N scenario
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Chapter 7 So, we can see that the d
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Chapter 7 Fortunately, scikit-learn
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[ 161 ] Chapter 7 The loading of th
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Chapter 7 Summary In this chapter,
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Regression - Recommendations Improv
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Weights Regression - Recommendation
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Classification III - Music Genre Cl
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Chapter 9 Matplotlib provides the c
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[ 185 ] Chapter 9
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Chapter 9 def create_fft(fn): sampl
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Chapter 9 ax.set_yticks(range(len(g
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Chapter 9 On the left-hand side gra
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[ 193 ] Chapter 9 Improving classif
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We get the following promising resu
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Chapter 9 Summary In this chapter,
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Computer Vision - Pattern Recogniti
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Building Machine Learning Systems with Python - Richert, Coelho

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