[Reinforcement Learning] Get started to learn Actor Critic for reinforcement learning

Actor-Critic is basically combined with Policy Gradient (Actor)  and Function Approximation (Critic) based algorithm together. Actor is based on the probability given by policy to act and Critic judges the performance of Actor and gives the score. So, Actor will improve its probability given by policy based on Critic's judge and score. The following diagram is the concept:

[Reinforcement Learning] Get started to learn Sarsa(lambda λ) for reinforcement learning

Once you know what the Sarsa algorithm is, you can continue to learn Sarsa(lambda λ) algorithm.
I basically refer to these tutorial documents (written in Chinese) :
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-A-sarsa-lambda/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/3-3-tabular-sarsa-lambda/
https://zhuanlan.zhihu.com/p/28108498

The Sarsa(lambda λ) algorithm looks like this:

[Reinforcement Learning] Get started to learn Sarsa for reinforcement learning

If taking a look at Sarsa algorithm, you will find that it is so similar with Q-Learning.
For my previous post about Q-Learning, please refer to this link:
https://danny270degree.blogspot.com/2018/11/reinforcement-learning-get-started-to_21.html

Here is the Sarsa algorithm:

[Reinforcement Learning] Using dynamic programming to solve a simple GridWorld with 4X4

I borrow the example and its source code from here which is a dynamic programming to solve a simple GridWorld with 4X4 and put my explanation for the calculation of value function. Hope that will help to understand dynamic programming and Markov Reward Process(MRP) more quickly.

[Reinforcement Learning] Get started to learn DQN for reinforcement learning

The previous post about Q-Learning is here:
[Reinforcement Learning] Get started to learn Q-Learning for reinforcement learning

Basically, Deep Q-Learning ( DQN ) is upgraded the Q-Learning algorithm and the Q-table is replaced by the neural network. For the DQN tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-A-DQN/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-1-DQN1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-2-DQN2/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/4-3-DQN3/

[Reinforcement Learning] Get started to learn Q-Learning for reinforcement learning

The previous post about reinforcement learning:
[Reinforcement Learning] Get started to learn gradient method for reinforcement learning

For the Q-Learning tutorial, I refer to these as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-A-q-learning/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-2-tabular-q1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/2-3-tabular-q2/

[Reinforcement Learning] Get started to learn policy gradient method for reinforcement learning

This post is about my first time to learn policy gradient method for reinforcement learning. Basically, there are already a lot of materials on the internet, but in this time, I only want to focus on a tutorial as follows: ( sorry, they are written in Chinese )
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-1-policy-gradient-softmax1/
https://morvanzhou.github.io/tutorials/machine-learning/reinforcement-learning/5-2-policy-gradient-softmax2/

[RNN] What are the difference of input and output's tensor shape in dynamic_rnn and static_rnn using TensorFlow

When studying RNN, my first issue encountered in my program is about the shape of input and output tensors. Shape is a very important information to connect between layers. Here I just directly point out what are differences in input/output shape of static RNN and dynamic RNN.
P.S: If you use Keras to write your RNN model, you won't need to deal with these details.

[TensorFlow] The explanation of average gradients by example in data parallelism

When studying some examples of training model using Multi-GPUs ( in data parallelism ), the average gradients function always exists in some kind of ways, and here is a simple version as follows:

[Dynamic Control Flow] Whitepaper: Implementation of Control Flow in TensorFlow

In the following whitepaper, we can understand more dynamic control flow in details.
Whitepaper: Implementation of Control Flow in TensorFlow
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf

[TensorFlow] Train in Tensorflow and do inference with the trained model

If you want to train your model in Tensorflow and do inference with the trained model, you can refer to this post.

1. Train your model

I will use the simple CNN model in my previous post:
[ONNX] Train in Tensorflow and export to ONNX (Part II)
https://danny270degree.blogspot.com/2018/08/onnx-train-in-tensorflow-and-export-to_20.html

So, after training, you will get these files:

my_mnist/

├── checkpoint

├── graph.pbtxt

├── my_mnist_model.data-00000-of-00001

├── my_mnist_model.index

└── my_mnist_model.meta

[LLVM] LLVM studying list for newbie

If you are an LLVM newbie and are interested in LLVM like me, you may take a look at my LLVM studying list. It takes time for me to search the related resources and documents. So, I think it will help somehow. By the way, most of my list items are written in Chinese so that those who are native Engish speakers may not suit for this.

[TensorFlow] Does it help the processing time and transmission time if increasing CUDA Steam number in TensorFlow?

Before starting to increase the CUDA Steam number in TensorFlow, I want to recap some ideas about the Executor module. When TensorFlow session runs, it will build Executor. Meanwhile, if you enable CUDA in TensorFlow build configuration, the Executor will add visible GPU devices and create TF device object (GPUDevice object) mapping to physical GPU device. There are 4 kinds of streams inside GPUDevice:

• CUDA stream 
• Host_to_Device stream
• Device_to_Host stream
• Device_to_Device stream

[TensorFlow Grappler] How to do the topological sorting in TensorFlow Grappler?

If you try to implement some optimizers in TensorFlow Grappler, you must have to know how to deal with the directed computation graph. One of the most important tools/knowledges is topological sorting.
The definition from Wiki: Topological sorting
https://en.wikipedia.org/wiki/Topological_sorting
"In the field of computer science, a topological sort or topological ordering of a directed graph is a linear ordering of its vertices such that for every directed edge uv from vertex u to vertex v, u comes before v in the ordering."

[Tool] To draw a sequence diagram using online tool sequencediagram

This website provides an online free tool for users to draw the sequence diagram as follows:
https://sequencediagram.org/

Basically, you can follow the instructions at the left top corner button. Check it out.
Here is my example of the sequence diagram about tracing some source codes of XLA AOT in TensorFlow.

[TensorFlow Grappler] The ways to traverse all nodes' input and output in the graph using C++ in TensorFlow Grappler

Here I want to introduce 2 ways to traverse all nodes' input and output in the graph using C++ in Grappler.
P.S: you have to be able to get GrapplerItem and GraphDef objects in your code.

First, check my example node name in Tensorboard as follows:
conv1/Conv2D

[NUMACTL] How to use numactl in practice?

I recently attended the Intel AI workshop and they gave an advice of using NUMACTL to improve the performance of training and inferencing in Deep Learning with Intel Caffe. Here I post some related information as follows:

[XLA 研究] How to use XLA AOT compilation in TensorFlow ( Part II )

My previous post: [XLA 研究] How to use XLA AOT compilation in TensorFlow is about a simple example to use XLA AOT. But, if you want to see a more complicated example, please take a look at this: https://gist.github.com/carlthome/6ae8a570e21069c60708017e3f96c9fd

[TFLMS] Large Model Support in TensorFlow by Graph Rewriting

This post just introduces this paper "Large Model Support in TensorFlow by Graph Rewriting" and it is published as a pull request in the TensorFlow repository for contributing to the TensorFlow community. With TFLMS, we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size, respectively. Quite amazing...

[TensorFlow] Why does the feed's shape matter in TensorFlow Grappler?

Before explaining this, you should understand what Shapes and dynamic dimensions are in TensorFlow. This article below explains the concept very well.
https://blog.metaflow.fr/shapes-and-dynamic-dimensions-in-tensorflow-7b1fe79be363
The key idea is:

[XLA related] Sort out my thought and notes about XLA related

This post could be a little bit unstructured because it's for my reference in notes.
I recently found that there are several slides in SlideShare which contain very good information and source code analysis/study about XLA related as follows:

[TensorFlow] My simple way to profile TensorFlow and dump variables and GPU memory

As we know that if we want to profile Tensorflow graph and know what operations take more time and what less. This can be done with Tensorflow timeline module like this:
( I ignore the part of the model to simplify my example code )

...
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
...
with tf.Session(config=config) as sess:
    init.run()
    for epoch in range(n_epochs):
        for iteration in range(10):
            sess.run(training_op, feed_dict={X: picture, y:picture_label}, 
                     options=run_options, run_metadata=run_metadata)
            fetched_timeline = timeline.Timeline(run_metadata.step_stats)
            chrome_trace = fetched_timeline.generate_chrome_trace_format()
            with open('timeline_step_%d.json' % iteration, 'w') as f:
                f.write(chrome_trace)

[ONNX] Train in Tensorflow and export to ONNX (Part II)

If you read the previous post as the link below, you probably may ask a question: If the input TF graph for freezing is not a binary format, what do we do?
http://danny270degree.blogspot.com/2018/08/onnx-train-in-tensorflow-and-export-to.html

Let us recall the previous example below. The file "graph.proto" is the binary format of the protobuf file for TensorFlow graph generated from the following function:

  with open("graph.proto", "wb") as file:
    graph = tf.get_default_graph().as_graph_def(add_shapes=True)
    file.write(graph.SerializeToString())

[TensorFlow] Rewriter_Config and Memory Optimization Passes

In the previous post as the below link, I mentioned that the default value of rewrite_config seems to change a little bit.
https://danny270degree.blogspot.com/2018/06/tensorflow-compare-memory-options-in.html

To clarify my doubt, I check the TensorFlow's memory_optimizer.cc and arrange the mapping table:

[TensorFlow] How to print the timestamp of a node/operation of computation graph in run-time?

When some people first time tries to debug or print out information of some result from a node/operation in the computation graph in TensorFlow, they maybe confuse about how to do it. Fortunately, someone in Google gave a great explanation of the print function:
https://towardsdatascience.com/using-tf-print-in-tensorflow-aa26e1cff11e
After reading it, you should understand how tf.Print() function works and to use it.

[ONNX] Use ONNX_TF and nGraph_ONNX to do inference/prediction with ONNX model

Here I try to use the pre-trained model from ONNX model zoo, which the models are already converted from some deep learning framework. So I download the Resnet50 model from the following URL and untar it:

wget https://s3.amazonaws.com/download.onnx/models/opset_8/resnet50.tar.gz
tar -xzvf resnet50.tar.gz 

P.S: pre-trained ONNX models: https://github.com/onnx/models

Then, I can do the inference/prediction using this ONNX model in two ways:

[ONNX] Train in Tensorflow and export to ONNX (Part I)

From my point of view, ONNX is a model description spec and ONNX model needs Deep Learning framework or backend tool/compiler which supports it to run.
The advantage of ONNX as I know is about portable and exchangeable between DL frameworks.
Here I will use this tutorial to convert TensorFlow's model to ONNX model by myself.

https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb

[Fun] compress and composite dataset to one image file

If you want to compress and composite the images in a dataset, like MNIST or CIFAR-10, you probably can refer to my example.

People need to see it to believe it. Here you go:

[Confusion Matrix] How to calculate confusion matrix, precision and recall list from scratch

I directly give an example which is with 10 categories, such as CIFAR-10 and MNIST. It explains how to calculate the confusion matrix, precision and recall list from scratch in Python. My data is generated at random. You should replace by yours. Here it goes:

[Qt5] How to develop Qt5 GUI with TensorFlow C++ library?

Here I give a simple and complete example of how to develop Qt5 GUI with TensorFlow C++ library on Linux platform. Please check out my GitHub's repository as follow:
https://github.com/teyenliu/tf_inference_gui

[TensorFlow] How to implement LMDBDataset in tf.data API?

I have finished implementing the LMDBDataset in tf.data API.  It could be not the bug-free component, but at least it's my first time to try to implement C++ and Python function in TensorFlow. The API architecture looks like this:

[TensorFlow] How to build your C++ program or application with TensorFlow library using CMake

When you want to build your  C++ program or application using TensorFlow library or functions, you probably will encounter some header file missed issues or linking problems. Here is the step list that I have verified and it works well.

1. Prepare TensorFlow ( v1.10) and its third party's library

$ git clone --recursive https://github.com/tensorflow/tensorflow
$ cd tensorflow/contrib/makefile
$ ./build_all_linux.sh

2. Modify .tf_.tf_configure.bazelrc

$ cd tensorflow/
$ vim .tf_configure.bazelrc
  append this line in the bottom of the file
  ==>
  build --define=grpc_no_ares=true

[XLA JIT] How to turn on XLA JIT compilation at multiple GPUs training

Before I discuss this question, let's recall how to turn on  XLA JIT compilation and use it in TensorFlow python API.

1. Session
Turning on JIT compilation at the session level will result in all possible operators being greedily compiled into XLA computations. Each XLA computation will be compiled into one or more kernels for the underlying device.

[PCIe] How to read/write PCIe Switch Configuration Space?

Here is a question, how to read/write PCIe Switch Configuration Space? We can see this picture first.

[TensorFlow] How to get CPU configuration flags (such as SSE4.1, SSE4.2, and AVX...) in a bash script for building TensorFlow from source

The AVX and SSE4.2 and others are offered by Intel CPU. (AVX and SSE4.2 are CPU infrastructures for faster matrix computations) Did you wonder what CPU configuration flags (such as SSE4.1, SSE4.2, and AVX...) you should use on your machine when building Tensorflow from source? If so, here is a quick solution for you.

[TensorFlow 記憶體優化實驗] Compare the memory options in Grappler Memory Optimizer

As we know that in Tensorflow, there is an optimization module called "Grappler". It provides many kinds of optimization functionalities, such as: Layout, Memory, ModelPruner, and so on... In this experiment, we can see the effect of some memory options enabled in a simple CNN model using MNIST dataset.

[XLA 研究] How to use XLA AOT compilation in TensorFlow

This document is going to explain how to use AOT compilation in TensorFlow. We will use the tool: tfcompile, which is a standalone tool that ahead-of-time (AOT) compiles TensorFlow graphs into executable code. It can reduce the total binary size, and also avoid some runtime overheads. A typical use-case of tfcompile is to compile an inference graph into executable code for mobile devices. The following steps are as follows:

1. Build tool: tfcompile

> bazel build --config=opt --config=cuda //tensorflow/compiler/aot:tfcompile

[XLA 研究] Take a glance to see the graph changes in XLA JIT compilation

In the preamble of this article, to understand XLA JIT is pretty hard because you probably need to understand TensorFlow Graph, Executor,  LLVM, and math... I have been through this painful study work somehow so that I hope my experience can help for those who are interested in XLA but have not get understood yet.

[TX2 研究] My first try on Jetson TX2

I got a Jetson TX2 several days ago from my friend and it looks like following pictures. I setup it using Nivida's installing tool: JetPack-L4T-3.2 version (JetPack-L4T-3.2-linux-x64_b196.run). During the installation, I indeed encounter some issues with not abling to setup IP address on TX2, and I resolved it. If anyone still has this issue, let me know and I will post another article to explain the resolving steps. 

[Caffe] Try out Caffe with Python code

This document is just a testing record to try out on Caffe with Python code. I refer to this blog. For using Python, we can easily to access every data flow blob in layers, including diff blob, weight blob and bias blob. It is so convenient for us to understand the change of training phase's weights and what have done in each step.

[Caffe] How to use Caffe to solve the regression problem?

There is a question coming up to my mind recently. How to use Caffe to solve the regression problem? We used to see a bunch of examples related to image recognition with labels and they are classification problem. In my experience, I have done this problem using TensorFlow, not Caffe. But, I think in theory they are both the same. The key point is using EuclideanLossLayer as the final Loss Layer and it's the detail from the official web site:

[Raspberry Pi] Use Wireless and Ethernet together

The following content is my Raspberry Pi 3's setting in /etc/network/interface as follows. In my case, I both use wireless and ethernet device at the same time.

# Include files from /etc/network/interfaces.d:
source-directory /etc/network/interfaces.d

auto lo
iface lo inet loopback

auto wlan0
allow-hotplug wlan0
iface wlan0 inet manual
    Wpa-conf /etc/wpa_supplicant/wpa_supplicant.conf

allow-hotplug eth0
iface eth0 inet static
    address 140.96.29.224
    netmask 255.255.255.0
    up ip route add 100.85.0.0/24 via 140.96.29.254 dev eth0
    up ip route add 140.96.29.0/24 via 140.96.29.254 dev eth0
    up ip route add 140.96.98.0/24 via 140.96.29.254 dev eth0

[Debug] Debugging Python and C++ exposed by boost together

During the studying of Caffe, I was curious about how Caffe provides Python interface and what kind of tool uses for wrapping. Then, the answer is Boost.Python. I think for C++ developer, it is worth time to learn and I will study it sooner. In this post, I want to introduce the debugging skill which I found in this post and I believe these are very useful such as debugging Caffe with Python Layer. Here is the link:
https://stackoverflow.com/questions/38898459/debugging-python-and-c-exposed-by-boost-together

[PCIe] lspci command and the PCIe devices in my server

The following content is about my PCIe devices/drivers and the lspci command results.

$ cd /sys/bus/pci_express/drivers

$ ls -al

drwxr-xr-x 2 root root 0  7月  6 15:33 aer/

drwxr-xr-x 2 root root 0  7月  6 15:33 pciehp/

drwxr-xr-x 2 root root 0  7月  6 15:33 pcie_pme/

[Caffe] Install Caffe and the depended packages

This article is just for me to quickly record the all the steps to install the depended packages for Caffe. So, be careful that it maybe is not good for you to walk through them in your environment. ^_^

# Install CCMAKE

$ sudo apt-get install cmake-curses-gui

[NCCL] Build and run the test of NCCL

NCCL requires at least CUDA 7.0 and Kepler or newer GPUs. Best performance is achieved when all GPUs are located on a common PCIe root complex, but multi-socket configurations are also supported.

Note: NCCL may also work with CUDA 6.5, but this is an untested configuration.

Build & run

To build the library and tests.

$ cd nccl
$ make CUDA_HOME=<cuda install path> test
Test binaries are located in the subdirectories nccl/build/test/{single,mpi}.

$ ~/git/nccl$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:./build/lib
$ ~/git/nccl$ ./build/test/single/all_reduce_test 100000000
# Using devices
#   Rank  0 uses device  0 [0x04] GeForce GTX 1080 Ti
#   Rank  1 uses device  1 [0x05] GeForce GTX 1080 Ti
#   Rank  2 uses device  2 [0x08] GeForce GTX 1080 Ti
#   Rank  3 uses device  3 [0x09] GeForce GTX 1080 Ti
#   Rank  4 uses device  4 [0x83] GeForce GTX 1080 Ti
#   Rank  5 uses device  5 [0x84] GeForce GTX 1080 Ti
#   Rank  6 uses device  6 [0x87] GeForce GTX 1080 Ti
#   Rank  7 uses device  7 [0x88] GeForce GTX 1080 Ti

#                                                 out-of-place                    in-place
#      bytes             N    type      op     time  algbw  busbw      res     time  algbw  busbw      res
   100000000     100000000    char     sum   30.244   3.31   5.79    0e+00   29.892   3.35   5.85    0e+00
   100000000     100000000    char    prod   30.493   3.28   5.74    0e+00   30.524   3.28   5.73    0e+00
   100000000     100000000    char     max   29.745   3.36   5.88    0e+00   29.877   3.35   5.86    0e+00
   100000000     100000000    char     min   29.744   3.36   5.88    0e+00   29.868   3.35   5.86    0e+00
   100000000      25000000     int     sum   29.692   3.37   5.89    0e+00   29.754   3.36   5.88    0e+00
   100000000      25000000     int    prod   30.733   3.25   5.69    0e+00   30.697   3.26   5.70    0e+00
   100000000      25000000     int     max   29.871   3.35   5.86    0e+00   29.700   3.37   5.89    0e+00
   100000000      25000000     int     min   29.809   3.35   5.87    0e+00   29.852   3.35   5.86    0e+00
   100000000      50000000    half     sum   28.590   3.50   6.12    1e-02   27.545   3.63   6.35    1e-02
   100000000      50000000    half    prod   27.416   3.65   6.38    1e-03   27.375   3.65   6.39    1e-03
   100000000      50000000    half     max   30.811   3.25   5.68    0e+00   30.670   3.26   5.71    0e+00
   100000000      50000000    half     min   30.818   3.24   5.68    0e+00   30.931   3.23   5.66    0e+00
   100000000      25000000   float     sum   29.719   3.36   5.89    1e-06   29.750   3.36   5.88    1e-06
   100000000      25000000   float    prod   29.741   3.36   5.88    1e-07   30.029   3.33   5.83    1e-07
   100000000      25000000   float     max   28.400   3.52   6.16    0e+00   28.400   3.52   6.16    0e+00
   100000000      25000000   float     min   28.364   3.53   6.17    0e+00   28.434   3.52   6.15    0e+00
   100000000      12500000  double     sum   33.989   2.94   5.15    0e+00   34.104   2.93   5.13    0e+00
   100000000      12500000  double    prod   33.895   2.95   5.16    2e-16   33.833   2.96   5.17    2e-16
   100000000      12500000  double     max   30.228   3.31   5.79    0e+00   30.273   3.30   5.78    0e+00
   100000000      12500000  double     min   30.324   3.30   5.77    0e+00   30.341   3.30   5.77    0e+00
   100000000      12500000   int64     sum   29.914   3.34   5.85    0e+00   30.036   3.33   5.83    0e+00
   100000000      12500000   int64    prod   30.975   3.23   5.65    0e+00   31.083   3.22   5.63    0e+00
   100000000      12500000   int64     max   29.954   3.34   5.84    0e+00   29.949   3.34   5.84    0e+00
   100000000      12500000   int64     min   29.946   3.34   5.84    0e+00   29.952   3.34   5.84    0e+00
   100000000      12500000  uint64     sum   29.981   3.34   5.84    0e+00   30.100   3.32   5.81    0e+00
   100000000      12500000  uint64    prod   30.911   3.24   5.66    0e+00   30.800   3.25   5.68    0e+00
   100000000      12500000  uint64     max   29.890   3.35   5.85    0e+00   29.947   3.34   5.84    0e+00
   100000000      12500000  uint64     min   29.929   3.34   5.85    0e+00   29.964   3.34   5.84    0e+00

 Out of bounds values : 0 OK
 Avg bus bandwidth    : 5.81761

[Mpld3] Render Matplotlib chart to web using Mpld3

The following example is about rendering a matplotlib chart on web, which is based on Django framework to build up. I encountered some problems before, such as, not able to see chart on the web page or having a run-time error after reloading the page. But, all the problems are solved.

<< demo/views.py>>

import matplotlib.pyplot as plt
import numpy as np
import mpld3

def plot_test1(request):
context = {}
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
N = 100

"""
Demo about using matplotlib and mpld3 to rendor charts
"""
scatter = ax.scatter(np.random.normal(size=N),
np.random.normal(size=N),
c=np.random.random(size=N),
s=1000 * np.random.random(size=N),
alpha=0.3,
cmap=plt.cm.jet)
ax.grid(color='white', linestyle='solid')

ax.set_title("Scatter Plot (with tooltips!)", size=20)

labels = ['point {0}'.format(i + 1) for i in range(N)]
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)
#figure = mpld3.fig_to_html(fig)
figure = json.dumps(mpld3.fig_to_dict(fig))
context.update({ 'figure' : figure })

"""
Demo about using tensorflow to predict the result
"""
num = np.random.randint(100)
prediction = predict_service.predict(num)
context.update({ 'num' : num })
context.update({ 'prediction' : prediction })

return render(request, 'demo/demo.html', context)

<<demo/demo.html>>

<script type="text/javascript" src="http://d3js.org/d3.v3.min.js"></script>
<script type="text/javascript" src="http://mpld3.github.io/js/mpld3.v0.2.js"></script>
<style>
/* Move down content because we have a fixed navbar that is 50px tall */
body {
padding-top: 50px;
padding-bottom: 20px;
}
</style>
<html>
<div id="fig01"></div>
<script type="text/javascript">

figure = {{ figure|safe }};
mpld3.draw_figure("fig01", figure);

</script>
</html>

So, we can see the result as follows:

[Hadoop] To build a Hadoop environment (a single node cluster)

For the purpose of studying Hadoop, I have to build a testing environment to do. I found some resource links are good enough to build a single node cluster of Hadoop MapReduce as follows. And there are additional changes from my environment that I want to add some comments for my reference.

http://www.thebigdata.cn/Hadoop/15184.html
http://www.powerxing.com/install-hadoop/

Login the user "hadoop"

$ sudo su - hadoop

Go to the location of Hadoop

$ /usr/local/hadoop

Add the variables in ~/.bashrc

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64 export HADOOP_HOME=/usr/local/hadoop export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop export HADOOP_INSTALL=/usr/local/hadoop export PATH=$PATH:$HADOOP_INSTALL/bin export PATH=$PATH:$HADOOP_INSTALL/sbin export HADOOP_MAPRED_HOME=$HADOOP_INSTALL export HADOOP_COMMON_HOME=$HADOOP_INSTALL export HADOOP_HDFS_HOME=$HADOOP_INSTALL export YARN_HOME=$HADOOP_INSTALL

Modify $JAVA_HOME in etc/hadoop/hadoop-env.sh

export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64

Start dfs and yarn

$ sbin/start-dfs.sh
$ sbin/start-yarn.sh

Finally, we can try the Hadoop MapReduce example as follows:
$ bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar grep input output 'dfs[a-z.]+'

[Spark] To install Spark environment based on Hadoop

This document is to record how to install Spark environment based on Hadoop as the previous one. For running Spark in Ubuntu machine, it should install Java first. Using the following command is easily to install Java in Ubuntu machine.

$ sudo apt-get install openjdk-7-jre openjdk-7-jdk
$ dpkg -L openjdk-7-jdk | grep '/bin/javac'
$ /usr/lib/jvm/java-7-openjdk-amd64/bin/javac

So, we can setup the JAVA_HOME environment variable as follows:
$ vim /etc/profile
  append this ==> export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64

$ sudo tar -zxf ~/Downloads/spark-1.6.0-bin-without-hadoop.tgz -C /usr/local/
$ cd /usr/local
$ sudo mv ./spark-1.6.0-bin-without-hadoop/ ./spark
$ sudo chown -R hadoop:hadoop ./spark

$ sudo apt-get update
$ sudo apt-get install scala
$ wget http://apache.stu.edu.tw/spark/spark-1.6.0/spark-1.6.0-bin-hadoop2.6.tgz
$ tar xvf spark-1.6.0-bin-hadoop2.6.tgz
$ cd /spark-1.6.0-bin-hadoop2.6/bin
$ ./spark-shell

$ cd /usr/local/spark
$ cp ./conf/spark-env.sh.template ./conf/spark-env.sh
$ vim ./conf/spark-env.sh
  append this ==> export SPARK_DIST_CLASSPATH=$(/usr/local/hadoop/bin/hadoop classpath)

[picamera] Solving the problem of video display using Raspberry Pi Camera

When I tried to use Raspberry Pi Camera to display video or image, I encountered a problem that there is no image frame and the GUI showed a black frame on the screen. It took me a while to figure out this issue.
    After searching the similar error on the Internet, I found it is related with using picamera library v1.11 and Python 2.7. So I try downgrading to picamera v1.10 and this should resolve the blank/black frame issue:

The linux command is as follows:
$ sudo pip uninstall picamera
$ sudo pip install 'picamera[array]'==1.10

So, it seems there are some issues with the most recent version of picamera that are causing a bunch of problems for Python 2.7 and Python 3 users.