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Will this project expand faster than Tensorflow Python to make it worth investing in? We will see in the coming months :)
Didn’t he left the nodejs ecosystem because he said vocally that Go is so much better? Just curious what changed his mind...
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So now you'll have to setup a nodeJS env? I don't really see the difference.
Not sure what there is to set up for a new python project, but on Node, all you have to do is `npm init` and then `npm install propel` to get started.
conda create -n fooenv numpy scipy etcGoing 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 :)
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.
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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.
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 ...
This could a fun way to learn ML using just your browser. I'm still skeptic about the performance but for prototyping it could be viable. One thing bothering me about reading the API is the inconsistent way of adding semicolons. Not an important thing I'm sure and as my personal opinion I'd much rather have the examples without them. To me they are just waste of time. Also it makes the code look more like Python. Any…
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.
Let's say you already have a NodeJS service. This reducers the friction to add some ML into the mix to basically zero. Install the optional propel_linux_gpu package, throw it on a GPU instance on AWS and call it a day.
ML right now is still mostly the domain of the tech giants and researchers, efforts like these will serve to make it an everyday component of software stacks everywhere.
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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.
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 ...
I always forget to set that up. Also I never learned python packaging, with npm it was trivial.
Is there a practical use case for this system? It looks great. I want to parse out textual data and this system seems to only work on numbers sets.
I guess it's still work in progress, but they are probably aiming to achieve something in the direction of the scientific Python stack (i.e. Jupyter, TensorFlow and the like). Maybe starting with a subset of methods useful in ML. It will be interesting to see how this scientific JS toolchain evolves. Would be fun to get a more feature-complete Propel ML working with Mike Bostock's Observable project: https://beta.obs…
Seems to be quite far away. IIRC, there are a lot of immature, scattered math libs for JS.
Most of the ML libs I've seen are proof-of-concept, like brain.js or synapse. Those libs seem to implement basic perceptrons and other textbook examples, but not much else.
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.