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Ask HN: What ML platform are you using?

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Re: Ask HN: What ML platform are you using?

#61

Post by 37ef_ced3 »

mdp2021 wrote: post #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 lightweig…

Fast CPU transformers: https://bellard.org/libnc

Fast CPU convolutions: https://NN-512.com

Both are completely stand-alone (no external dependencies).

Re: Ask HN: What ML platform are you using?

#62

Post by apohn »

For personal use I use my laptop for anything ML and Colab Pro for anything Deep Learning. The free edition of Colab is also great for learning Deep Learning.

I personally find most cloud providers annoying to use for personal use. You have to ask for permission to get access to a GPU that's not any better than what you get in the free with Colab. Then, there's all sorts of configuration you have to do. Colab is much easier and basically zero wait time to go from logging in to starting to run code.

At work we use Databricks, which is too expensive for personal use.

Re: Ask HN: What ML platform are you using?

#63

Post by apohn »

cweill wrote: post #42

I worked in Google Research for over 5 years doing Machine Learning, and recently quit to build my own ML start-up. These days, I solve a mix of NLP, computer vision, and tabular problems, all with state of the art neural network techniques. I've tried many setups. My advice is go with Colab Pro ($50/mo) and TensorFlow/Keras. You can go with Pytorch too if you prefer. I made the mistake of buying a 2080Ti for my desk…

Note that Colab Pro is $10/month and Pro+ is $50/month.

The $10 is more than enough for learning Deep Learning.

Re: Ask HN: What ML platform are you using?

#65

Post by xoserr »

I think you have to figure out what kind of problems you want to solve. scikit-learn will run perfectly fine on your CPU and you might not need all this complexity beyond that.

I think you are getting side tracked by a bunch of people at a car show with their hood popped checking out the custom chrome engines each other have. It is a bit pointless to worry about if you don't even know how to drive yet.

Re: Ask HN: What ML platform are you using?

#66

Post by rg111 »

hackhack168 wrote: post #17
rg111 wrote: post #16

Thr best option for you is- Gradient Paperspace and Colab. Both are free and managed. Learn Machine Learning first. Do not spend time on managing infra for ML while you are learning ML. Focus on learning ML first. You can make decent cutting edge models and SOTA classic models just with free options. I am saying this because I have done this. I suggest that you get Colab Pro after that. AWS burns a hole on your pocke…

can you please be specific on "necessary Math"? trying to apply pareto principle and cut down amount of time needed to brush up what seemingly all of lower division math courses.

My suggestion is to learn just high school amount of Differential Calculus, Linear Algebra. That much Statistics is not needed.

And do these the right way- forget about being able to prove stuff for test, or remembering the heuristics for solving problems in tests, or being able to pick the correct option from many in test.

Just forget how you studied for test. Learn limited things- very very deeply.

Learn why and how exactly each thing works. Each and every part.

The resources for these are-

1. Mathematics for Machine Learning: Linear Algebra (Coursera, Imperial)

2. - do - : Calculus

3. Essence of Linear Algebra Playlist: 3blue1brown

4. Essence of Calculus Playlist, Ibid

5. Khan Academy Statistics Playlist for High School

Again, understand each and every part very deeply.

This much Math is enough to get started with Machine Learning.

(You will need much much, much more if you want to be a Research Engineer or an Assistant Professor doing active research.

But you can chart your own path after a while.)

Then you start doing ML.

Then you learn whatever math is needed along the way.

Never, ever load your head with a bunch of math concepts just to "prepare" yourself for studying ML. I, very highly advise against it.

So,

Learn very basic stuff, but make the concepts crystally clear -> start doing ML -> learn more math as you face the need.

Learning math is a noble and worthy goal. But do not confuse it with "learning math so that I can study ML".

Re: Ask HN: What ML platform are you using?

#67

Post by cweill »

m3at wrote: post #59
sillysaurusx wrote: post #47

Earlier quoted context omitted.

(OP, please don’t subject yourself to TensorFlow/Keras. The moment Jax became available on TPUs publicly, the moment I stopped using TF. And boy oh boy, “never looked back” is an understatement. I still cringe remembering all the time I spent trying to get tf.function to just please, please work, like a housewife alarmed that neither her partner nor herself are able to actually work.)

To support your point with data, here is a graph of usage of TF vs PyTorch in papers over time: https://horace.io/pytorch-vs-tensorflow/

I've seen this chart too. But research != Production. I bet that TensorFlow is still more commonly used in serving than Pytorch because of great tooling like TensorFlow Serving. I could be wrong though, as I'm not up to date with the latest in the Pytorch ecosystem.

Re: Ask HN: What ML platform are you using?

#68

Post by cweill »

mark_l_watson wrote: post #52
cweill wrote: post #42

I worked in Google Research for over 5 years doing Machine Learning, and recently quit to build my own ML start-up. These days, I solve a mix of NLP, computer vision, and tabular problems, all with state of the art neural network techniques. I've tried many setups. My advice is go with Colab Pro ($50/mo) and TensorFlow/Keras. You can go with Pytorch too if you prefer. I made the mistake of buying a 2080Ti for my desk…

Great advice! BTW, your startup https://creatorml.com/ is very cool, what a creative idea.

Thank you! Feel free to reach out to me on Twitter or Discord (linked on the homepage) if you want to chat.

Re: Ask HN: What ML platform are you using?

#69

Post by cweill »

apohn wrote: post #63
cweill wrote: post #42

I worked in Google Research for over 5 years doing Machine Learning, and recently quit to build my own ML start-up. These days, I solve a mix of NLP, computer vision, and tabular problems, all with state of the art neural network techniques. I've tried many setups. My advice is go with Colab Pro ($50/mo) and TensorFlow/Keras. You can go with Pytorch too if you prefer. I made the mistake of buying a 2080Ti for my desk…

Note that Colab Pro is $10/month and Pro+ is $50/month. The $10 is more than enough for learning Deep Learning.

100% agreed. Start with the $10 plan. I forgot how much it cost for the middle tier. The one benefit of the higher tier is you get access to better GPUs, and can run multiple colabs in parallel effectively getting multiple accelerators at once if you're doing distributed hyperparameter tuning.

Re: Ask HN: What ML platform are you using?

#70

Post by p1esk »

cweill wrote: post #69
apohn wrote: post #63

Earlier quoted context omitted.

Note that Colab Pro is $10/month and Pro+ is $50/month. The $10 is more than enough for learning Deep Learning.

100% agreed. Start with the $10 plan. I forgot how much it cost for the middle tier. The one benefit of the higher tier is you get access to better GPUs, and can run multiple colabs in parallel effectively getting multiple accelerators at once if you're doing distributed hyperparameter tuning.

The one benefit of the higher tier is you get access to better GPUs

I think it’s the same GPUs as Pro. I’m actually surprised you don’t recommend buying a 2080Ti. The best GPU you get with colab pro+ is P100 which is slower than 2080Ti. If you can afford it, having your own GPU workstation is a much better experience than dealing with colab.

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