ChatGPT uses GPT-3.5, which is a pre-trained model. Google uses pertained models. Facebook created a pre-trained model recently.

If these models satisfy their needs it will definitely satisfy yours. Unless if you are going beyond a kind of problem that hasn't been tackled before, a pre-trained model will save you so much time training and require a lot less data to get it to converge and actually be useful.

Dude this is like saying you are trying to build a ship with a rubber duckey. Use the right tools.

Try looking into if they have an API. shutdown is rare, but it happens so I only ran into it once. Having the cloud on your mobile device is great, it allows you to check anywhere and do some simple things quickly.

• https://aws.amazon.com/about-aws/whats-new/2013/01/08/use-amazon-cloudwatch-to-detect-and-shut-down-unused-amazon-ec2-instances/

>Can you recommend a tutorial or something that explains the steps to move from (e.g. pytorch) training on your own machine to training that model in the Cloud (e.g. AWS)?

Same as running on your own machine.

>What type of instances to chose, how/where to store data, making sure Nvidia/CUDA stuff is working properly, etc.?

Just look up a EC2 or VM that has the gpu you want and there you go. nvidia-smi is the command that should tell you the gpu you have. It's working if it outputs the GPU you have. I would suggest checking in the code if CUDA is running.

I prefer to use a EC2 or VM because it's normally cheaper, but you have to do your own research on pricing. Cloud is a competitive market, so there is always someone ready to offer a A100 at a cheaper price. Lambda Cloud I heard was super cheap for on demand.

You can get free credits online if you ask for it up to the thousands for research

https://aws.amazon.com/government-education/research-and-technical-computing/cloud-credit-for-research/

https://www.microsoft.com/en-us/azure-academic-research/

https://edu.google.com/intl/ALL_us/programs/credits/research/?modal_active=none

The cloud vs local debate depends on your needs though.

Get a consultant and they can show you how. It depends on your processes.

I been running the A100 the entire weekend and so far it’s only costing me under 20 bucks. If you need it around an hour and it would probably cost you between 1-3 dollars

I would recommend you plan a budget before you get started and it will almost always be cheaper on a year basis. Try Colab first and see if you will need it longer than 12 hours.

Aws isn't the only one doing spot instances

I agree. It also helps with deploying an API for you model. Also systemMD is useful to keep things running is the server gets reset for whatever reason.

This is something most cloud services use to lock you in to their services and discourage migrations to another vendor.

The fact I have to use a script proves my point. I shouldn't be needing a script.

Availability in GPU is terrible. Colab is better because you don't have to wait for a GPU that is usually snagged in seconds.

Availability in GPU is terrible in paper space. I would rather get colab for that and a VM for heavy loads. I got a refund when it took me a day to find a GPU. I don't have time to watch 24/7 for a GPU that is snagged in seconds. This was in the payed option.

I disagree about that. Imagine you invest millions of dollars then someone makes millions of it and you lose millions of dollars.

It’s terrible at the moment.

If you are doing PyTorch you are signing up for a nightmare with the mps backend.

They benefit from releasing the paper because it gives other researchers inspiration and allows Google to get free R&D. The researcher then releases another paper and Google gets to benefit from that.

It doesn't make any sense to run neural network on the client side at all. Youtube takes a moment to process your video before it gets uploaded, which is probably when their deep learning algorithms get to work. After that you just save the frames and don't run the neural networks again.

This is a valid guess because it takes a lot longer to upload a video on Youtube in comparison to other platforms that do no checks at all.

He just said why. It's because there isn't a diverse and large amount data you are training on. Imaginet was trained on many different kind of objects (over a million images) and while your toy dataset may probably only have 50-100k.

Ok thank you. I kind of hate the mob mentality of this site though. It discourages learning and experimenting.

I was just asking more of a question tbh than pretending to know it. It’s why I said correct me if I am wrong.

IDK, I guess I ran into the Reeeeeditors. Mob mentality is what drives the website and blind dislike. I just don’t worry about it and enjoy my life outside this site.

Correct me if I am wrong AI: Niche part of ML ML: AI + Data Science

Edit: An “intelligent” computer uses AI to think like a human and perform tasks on its own. Machine learning is how a computer system develops its intelligence

https://azure.microsoft.com/en-us/solutions/ai/artificial-intelligence-vs-machine-learning/

It’s both. Both spit out answers. It’s more about the search engine though.

But what about something more niche like MPS or Rocm?

I learned this today. The moment you leave the Google search engine, is the moment it turns to total useless garbage.