7awk – Page 2

March 18, 2017March 19, 2017 7awk Julia

problems installing HDF5

To recap, got this error on running the Julia convert script:

UndefVarError: h5open not defined

in macro expansion; at ./REPL[9]:2 [inlined]

So the HDF5 module is not correctly installed, although I’d assumed it was when I ran convert.jl.

The top line of output from the compiler confirms that:

ERROR: LoadError: LoadError: HDF5 not properly installed. Please run Pkg.build(“HDF5”)

So reinstalled HDF5 as recommended:

julia> Pkg.build(“HDF5”)

Getting build errors…. and the compiler suggests:

— build the package(s) and all dependencies with `Pkg.build(“HDF5”)`

– build a single package by running its `deps/build.jl` script

But I’ve already tried the package manager build twice without success, so next I’ll try cloning the HDF5 code from Github.

March 17, 2017March 17, 2017 7awk Julia

Converting the CIFAR-10 dataset

The binary files of the CIFAR-10 dataset need to be converted to HDF5 as that is the format recognised by Mocha. To do this, I will use the Julia script convert.jl provided by pluskid. First, in Julia used the package manager to install HDF5: Pkg.add(“HDF5”). Then ran the conversion script. I get the following error:

UndefVarError: h5open not defined
in macro expansion; at ./REPL[9]:2 [inlined]
in anonymous at ./<missing>:?

March 11, 2017March 17, 2017 7awk Caffe|Docker

Installing Caffee on Docker

Downloaded the Docker image from BVLC’s repository. First time I tried, the pull failed with a Docker i/o error: “failed to register layer” Then reset Docker to factory defaults. Then the pull worked: dowboaded newer image for bvlc/caffee: cpu.

February 25, 2017March 22, 2017 7awk Caffe|Deep learning|GPU

“Cold-brewed” Caffee

I’m going to install Caffee on a system that doesn’t have a GPU. This MacBook Air 2014 only has Intel integrated graphics… no dedicated graphics chip. Is it possible to install and run Caffee without a GPU? Yes, according to their docs. Caffee can be configured to use only the CPU. Uncomment CPU_ONLY := 1 in MakeFile.config to get “cold-brewed” Caffee.

Of course, the deep learning algorithms required for image classification would run much faster with GPU support, as the parallel architecture of a GPU is ideally suited to those algorithms. But they can still run on the CPU alone. So I’m going to try the deep learning tutorial without a GPU. Something may be learned by starting from the minimum and working up.

February 18, 2017January 27, 2018 7awk Caffe|Julia|Mocha

Installing Caffee on OS X

I found out yesterday that I’d have to install Caffee first before I could start the demo in Mocha. Caffee is a CNN (Convolutional Neural Network) developed by Berkely Vison and Learning Center. The tutorial I’m following (at mochajl.readthedocs.io) requires using Caffee’s API to export the Caffee model parameters to HDF5 format. After that, the exported file can be input to Mocha. To sum up, I have to install Caffee first. That’s next…

February 8, 2017 7awk Deep learning|Julia

Setting up Julia locally

Setting up Julia locally first, before trying the cloud environment. Running OS X, El Capitan. Python and iPython weee already installed. So I first installed Julia, then installed IJulia.jl from the Julia REPL (Read Eval Print Loop). IJulia is an interface between Julia and Jupyter notebooks. Then installed Images.jl, also from the Julia REPL. Then Gadfly, a visualization tool written in Julia. Will continue tomorrow night.

January 11, 2017March 22, 2017 7awk GPU

Disenchanted with the MacBook

For the first time since getting a MacBook around 6 years ago, I’m seriously considering moving to a different platform, for both hardware and software reasons.

October 23, 2016October 23, 2016 7awk Python

String searching algorithms

Objective: compare two algorithms by inputting the same data to both. The two algorithms are the naive exact matching algorithm and the Boyer-Moore algorithm. They are implemented in Python code. The data consists of a pattern (P) and text (T).

November 5, 2014October 10, 2020 7awk Astronomy

The ghosts of galaxies past …

The primary objective of the Milky Way at home project is to develop three-dimensional maps of stellar streams orbiting the Milky Way Galaxy. Stellar streams are collections of stars that orbit the galaxy. They have been aptly described as, “the ghosts of galaxies past.”
Have any planetary systems been found in the stellar streams?

September 1, 2014October 10, 2020 7awk Astronomy

Mapping the Milky Way

… Is a gigantic task when you consider that the Milky Way galaxy is about 100,000 light years across, and our sun is 27,000 light years from the Galactic Center. We’re in a lonely part of the Milky Way, on the inner edge of a spiral called the Orion Arm. Yet the Milky Way galaxy is just one galaxy in a huge group of galaxies called a supercluster. A team of astronomers at the university of Hawaii have been mapping this supercluster. We now know that it’s 500 million light years in diameter and contains a hundred thousand galaxies. The Milky Way galaxy is located in the outskirts of this supercluster. Laniakea, our hone supercluster of galaxies.