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Propel – Machine learning for Javascript

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Re: Propel – Machine learning for Javascript

#32
post #17

Earlier quoted context omitted.

Setting up a node env is arguably easier. An installer from the NodeJS downloads page includes both the Node runtime and NPM. One executable to get started. Python, on the other hand, requires a python installer, then a pip or conda installation script. Not to mention python vs python3 decisions.

But some version python comes pretty much with every system, then you only need to install tensorflow with pip.

Best practice is to leave the system python alone. If you have to put sudo in front of pip, you're probably doing it wrong.

Re: Propel – Machine learning for Javascript

#33
post #17

Earlier quoted context omitted.

So now you'll have to setup a nodeJS env? I don't really see the difference.

Setting up a node env is arguably easier. An installer from the NodeJS downloads page includes both the Node runtime and NPM. One executable to get started. Python, on the other hand, requires a python installer, then a pip or conda installation script. Not to mention python vs python3 decisions.

pipenv --three shell pipenv install pkg_1 pkg_ pkg_3 etc.

Seems pretty easy to me.

Plus, no one should be using Python 2 for any new project now, so that's not even a decision to bother with anymore.

Re: Propel – Machine learning for Javascript

#34

Earlier quoted context omitted.

Python has a larger ecosystem for data intensive application and pratictoners. As a language it's very easy to learn for JavaScript devs. The only "killer" feature JS may bring to the table would be client side, browser based inference that leverages GPU. But we're eons away from that.

Arguably the only "killer" feature Python has is the ecosystem. There are efforts to build out a similar ecosystem for JS. For example https://github.com/jacobbogers/libRmath.js is a typescript port of R's core math library https://svn.r-project.org/R/trunk/src/nmath/

And the full force of the entire machine learning research community (except for a few still on Lua/Torch).

Re: Propel – Machine learning for Javascript

#35

Going back to the Tensorflow C API and adding plotting + networking + async capability is a smart engineering choice. Will this project expand faster than Tensorflow Python to make it worth investing in? We will see in the coming months :)

Python has a larger ecosystem for data intensive application and pratictoners. As a language it's very easy to learn for JavaScript devs. The only "killer" feature JS may bring to the table would be client side, browser based inference that leverages GPU. But we're eons away from that.

Agreed. Scientific computing has centered on two languages for well over a decade. I don’t think JavaScript is going to dethrone python as the interpreted language of choice because it can now push computation to the GPU via a well designed API.

Re: Propel – Machine learning for Javascript

#36

It binds directly to the TensorFlow C API, if you run it in NodeJS, which is awesome. I get tired of having to setup a python env anytime I want to play around with ML.

So now you'll have to setup a nodeJS env? I don't really see the difference.

I don't know what is the best practice. `pip` `virtualenv` `pipenv` `conda`?

NodeJS has `npm` or `yarn`, and they basically do and setup the same thing. You can switch between the two without headache. As a new user (or one who has tried on multiple attempts to get into it), python is hard to start.

Re: Propel – Machine learning for Javascript

#37

Earlier quoted context omitted.

But some version python comes pretty much with every system, then you only need to install tensorflow with pip.

Best practice is to leave the system python alone. If you have to put sudo in front of pip, you're probably doing it wrong.

I agree with that, but for a beginner, using `pip -u` is less friction. They can use virtualenvs for their second project.

Re: Propel – Machine learning for Javascript

#38
post #28

Earlier quoted context omitted.

There's no python vs python3 decision to make. There's no (sane) reason to pick Python 2 in 2018 for new projects. Conda installer comes with Python prepackaged. So it's really a single exe and one CMD to get a Tensorflow env setup. And, of course, you have access to numpy, pandas, jupyter, PIL ...

+ virtualenv I always forget to set that up. Also I never learned python packaging, with npm it was trivial.

The basic rule of thumb is npm is 'virtualenv by default' while pip is 'global by default'.

Re: Propel – Machine learning for Javascript

#39

Going back to the Tensorflow C API and adding plotting + networking + async capability is a smart engineering choice. Will this project expand faster than Tensorflow Python to make it worth investing in? We will see in the coming months :)

Does the tensorflow C API use CUDA if you have it installed?
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