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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?

#21

Post by version_five »

For learning (and for development on most projects, until it actually comes time to train the real model) the K80 or whatever the lower tier is on colab is fine as a gpu.

The problem with colab IMO is that if it's your main platform, you'll be pushed to use notebooks for everything which is not really a good practice. Whatever you use, I'd suggest focusing on building a real train.py script (I'm assuming you'll be using python) that takes command line arguments for the hyperparameters. Don't get sloppy and just have things run as a bunch of cells.

If you are learning, my unsolicited advice is don't use built in datasets, make sure you can write datasets / dataloaders yourself so you understand what is going on and can adapt to your own work. All the stock examples using built in mnist or whatever gloss over the most important parts of setting up the data

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

#22

Post by plafl »

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.

Not the parent but I would say you just need a little of statistics and a little of calculus and linear algebra. If you are interested in theory then you need more.

For statistics I would recommend "All of Statistics".

The level of calculus required is to know to differentiate.

Algebra is more important. Any introductory linear algebra book would do. If you are able to multiply matrices and solve equations you can postpone a topic until necessary like for example eigenvectors or matrix factorization.

My advice is to first jump into the pool and learn swimming as needed. But learn swimming, use the concrete problems to motivate yourself.

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

#23

Post by speedylight »

manca wrote: post #13

Are you just interested in the training part and managing the trained models, or you'd actually like to productionize the models and serve them at scale? A lot of end-to-end platforms are available nowadays that try to cover the entire lifecycle of a model from data prep, ETL, to training, serving, monitoring, operating. However, I found none of them really robust enough to cover all these cases perfectly, so I resor…

I'd say for the foreseeable future I simply want to focus training and running trained models, I don't plan to do anything at scale like launch a business, so the creating and training aspect is the one I want and probably should only focus on at first either way.

But I like your approach of stitching together various vendors so they fit your use case, I think it can be really flexible but also probably more expensive and slightly harder to manage... I think it can be worth the tradeoff though.

Thank you for the input!

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

#24

Post by nathaliamdc »

manca wrote: post #13

Are you just interested in the training part and managing the trained models, or you'd actually like to productionize the models and serve them at scale? A lot of end-to-end platforms are available nowadays that try to cover the entire lifecycle of a model from data prep, ETL, to training, serving, monitoring, operating. However, I found none of them really robust enough to cover all these cases perfectly, so I resor…

You make a good point there. Personally I’ve struggled quite a bit moving from one off models to taking them to production. Would you mind elaborating on what you mean by none of the platforms being robust enough?

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

#25

Post by ml_basics »

version_five wrote: post #21

For learning (and for development on most projects, until it actually comes time to train the real model) the K80 or whatever the lower tier is on colab is fine as a gpu. The problem with colab IMO is that if it's your main platform, you'll be pushed to use notebooks for everything which is not really a good practice. Whatever you use, I'd suggest focusing on building a real train.py script (I'm assuming you'll be us…

> All the stock examples using built in mnist or whatever gloss over the most important parts of setting up the data

Could you elaborate on this / provide a link to a tutorial explaining what’s going on?

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

#26

Post by y42 »

timzaman wrote: post #14

Get 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…

You do not necessarily need a high performance GPU to step into ML. I'm running a couple of my hobby projects either on my notebook or an old discarded former Server hardware with onboard GPU.

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

#27

Post by sandruso »

I have been working last 1.5 years on my master thesis and I my setup evolved Colab -> Kaggle -> Azure ML.

Colab you is great for diving into examples that are already premade for colab.

Kaggle is better in my opinion in dataset handling, you can import public or upload your dataset with ease. They give you 30+ gpu hours for a week with ability to train your models in background. This can’t be done in Colab.

ML Azure platform is next level when you can pay for it. I’ve got credits from school. You can start experiments from python sdk with your own configurations, setup python environments, upload datasets, etc.

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

#28

Post by cyber_kinetist »

timzaman wrote: post #14

Get 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 ham with sanctions all over the place.

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

#30

Post by Simon_O_Rourke »

Working with a large well known tech company, with surprisingly basic/non-existent ML until only very recently.

Using Redshift to do a lot of the heavy lifting and initial data preparation, then SageMaker for hosting models and scoring, and Tableau for dashboards.

While you can do training within SageMaker, we have a cluster of EC2 instances using H2O libraries (xgboost) to train, then wrap the resulting model as a docker image and deploy it to ECR and link to a SageMaker endpoint.

Clunky and very much human-in-the-loop for training and deployment, but you can't run before you can crawl in this space.

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