colab or rapidminer.
both of them work great for scratch projects.
Ask HN: What ML platform are you using?
31–40 of 84 posts
Re: Ask HN: What ML platform are you using?
#32Get a decent NVIDIA GPU. Then install PyTorch and off you go. I advise to make all your own tooling, as you likely have a specific use-case, and so your tooling can be tailored to that. Most ML tools are very generic, or so simply you might as well do them yourself. The advantage if having your own box, is that (1) you'll learn some systems skills building it and (2) since you invested in it, you should feel obligate…
I would say the exact same thing if the circumstances weren’t so dire, but an NVIDIA GPU is so expensive nowadays that it might be a bit better to use rented services (like the paid version of Google Colab) for now, if you have any monetary constraints. Maybe GPU prices will stabilize after Ethereum switches to POS and manufacturing pipelines get back to normal, but then I’m not that sure after seeing US trying to go…
I have a 3090 for serious (hobby) work and a 1070 from 4ish years back next to my bed. I think getting anything 1070 or better is good enough at the beginner level (training baby datasets/models from scratch such as MNIST/CIFAR, transfer learning the big models). I just don't understand the cost argument .. you can get this stuff used. Main thing is you need CUDA.
The 3090 machine gets about the same use as the 1070 in my case. While it is nice to have more GPU memory to have huge batches and train things faster, this is a quality of life improvement/bragging to be honest. Serious work in some sub-areas needs multi-GPUs or enterprise grade hardware (e.g. A100s).
Software-wise, I just use Pytorch/Pytorch lignting/keras, and anaconda.
Edit: I used to build my own machines in my younger days. The two machines I spoke of above are alienware. Got them on black friday sales. Cost-wise, they were ridiculously cheap for the power they give/impact on my career.
Re: Ask HN: What ML platform are you using?
#33I haven't experienced any real issues with GPU availability on Colab, I suggest that you just go ahead and use it and wait with the premature optimization until you actually hit a wall and need it.
For general advice focused on beginners and ESPECIALLY practical, cheap and efficient methods and hacks to do DL, I recommend searching in https://www.fast.ai/ and their forums https://forums.fast.ai/
I'll try to search inside fast.ai if there is a more specific link to give. I know that one of their chief pieces of advice has been to use Colab and take advantage of the 300$ free credit you get (per credit card) when signing up to Google Cloud, which you can use for DL.
Disclaimer - I'm one of the creators of DagsHub, we created the platform especially to help people like you with the difficulties of managing things like data and model versioning, experiment tracking, labeling, etc. we'd love to have you onboard, and thanks for reading until the end :)
Re: Ask HN: What ML platform are you using?
#34Try it out on a local Linux machine first, if you have one. There are plenty of ML techniques outside of neural networks which train perfectly well on a CPU, so I'd start there.
Look at some kind of AutoML framework like AutoGluon, then dive deeper on the components it uses once you've got through the initial setup process. AutoGluon will let you train some basic models with all the data cleaning and normalisation steps handled for you.
Re: Ask HN: What ML platform are you using?
#35BTW this question probably suits a poll. https://news.ycombinator.com/newpoll
You need over 200 karma to create a poll. OP doesn't have that much, yet.
Re: Ask HN: What ML platform are you using?
#36> I am very much a beginner in the space of machine learning
While the (precious and useful) advice around seem to cover mostly the bigger infrastructures, please note that
you can effectively do an important slice of machine learning work (study, personal research) with just a battery-efficiency-level CPU (not GPU), in the order of minutes, on a battery. That comes before going to "Big Data".
And there are lightweight tools: I am current enamoured with Genann («minimal, well-tested open-source library implementing feedfordward artificial neural networks (ANN) in C», by Lewis Van Winkle), a single C file of 400 lines compiling to a 40kb object, yet well sufficient to solve a number of the problems you may meet.
https://codeplea.com/genann // https://github.com/codeplea/genann
After all, is it a good idea to use tools that automate process optimization while you are learning the deal? Only partially. You should build - in general and even metaphorically - the legitimacy of your Python ops on a good C ground.
And: note that you can also build ANNs in R (and other math or stats environments). If needed or comfortable...
Also note - reminder - that the MIT lessons of Prof. Patrick Winston for the Artificial Intelligence course (classical AI with a few lessons on ANNs) are freely available. That covers the grounds before a climb into the newer techniques.
Re: Ask HN: What ML platform are you using?
#37Honestly, if you're a beginner in the machine learning space, you're not going to need GPUs for a LONG time, and would benefit from learning what's going on under the hood. Install Python on your machine, learn to structure your projects well, environments and requirements, etc. if and when you need more, figure it out then.
Re: Ask HN: What ML platform are you using?
#38Personally I stick with the classic SMLNJ
This made me smile
Re: Ask HN: What ML platform are you using?
#39> I am very much a beginner in the space of machine learning While the (precious and useful) advice around seem to cover mostly the bigger infrastructures, please note that you can effectively do an important slice of machine learning work (study, personal research) with just a battery-efficiency-level CPU (not GPU), in the order of minutes, on a battery. That comes before going to "Big Data". And there are lightweig…
Note that this won't work with reasonably performant CNNs. Passing an image batch through a large-ish ResNet takes half a second on our GPUs, several minutes at full load on CPU. This makes training infeasible, and most models small enough to work on CPU are so far from state-of-the-art that you can't do any worthwhile computer vision research with them.
Re: Ask HN: What ML platform are you using?
#40> I am very much a beginner in the space of machine learning While the (precious and useful) advice around seem to cover mostly the bigger infrastructures, please note that you can effectively do an important slice of machine learning work (study, personal research) with just a battery-efficiency-level CPU (not GPU), in the order of minutes, on a battery. That comes before going to "Big Data". And there are lightweig…
Note that this won't work with reasonably performant CNNs. Passing an image batch through a large-ish ResNet takes half a second on our GPUs, several minutes at full load on CPU. This makes training infeasible, and most models small enough to work on CPU are so far from state-of-the-art that you can't do any worthwhile computer vision research with them.
Yes, but note on the other hand that simpler infrastructures such as one-digit-wide-GB GPUs you could buy and install on your workstation could be similarly frustrating, because you may easily encounter their limits (as in, "I got this semi-specialized equipment and I cannot get an output above 1024x768?!").
So, while one is learning, the case could be for being conservative and work directly on available tools, which will be revealing on some scalability requirements, also optimistically: you do not need a full lab to do (reasonable) linear regression, nor to train networks for OCR, largely not to get acquainted with the various techniques in the discipline.
When the needs push, it sometimes will not be just high-end consumer equipment to solve your problem, so on the side of hardware already some practical notion of actual constraints of scale will help orientation. Because you do not need a GPU for most pathfinding (nor for getting a decent grasp of the techniques I am aware of), and when you will want to produce new masterpieces from a Rembrandt "ROM construct"¹ (and much humbler projects) a GPU will not suffice.
(¹reprising the Dixie Flatline module in William Gibson's Neuromancer)