Cheapest (0$) Deep Learning Hardware Options | 2021
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

In this video, I'll give you the cheapest options to get started with deep learning! I'll also mention what paid options you have at your disposal.

You'll get:
✔️ A high-level overview of the deep learning HW landscape

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 
--- Free options

✅ Google Colab: https://colab.research.google.com/
✅ (Google) Kaggle: https://www.kaggle.com/
✅ Kaggle tips: https://www.kaggle.com/docs/efficient-gpu-usage

If you want to dig deeper:
✅ Colab vs Kaggle: https://towardsdatascience.com/kaggle-vs-colab-faceoff-which-free-gpu-provider-is-tops-d4f0cd625029

Sign-up for 1-month free credits:
✅ Azure ML: https://azure.microsoft.com/en-us/free/machine-learning/search/?&ef_id=Cj0KCQjw5eX7BRDQARIsAMhYLP--LX_y8V5tXNp2AIQH79TiJDc0S9_P694O-Bh75-TahDH_BjMhbxwaAuqBEALw_wcB:G:s&OCID=AID2100645_SEM_Cj0KCQjw5eX7BRDQARIsAMhYLP--LX_y8V5tXNp2AIQH79TiJDc0S9_P694O-Bh75-TahDH_BjMhbxwaAuqBEALw_wcB:G:s&dclid=CjgKEAjw5eX7BRDukp61rqLX0z8SJACF1AMBoJvajYg6QC5LOUWfroyE4zdyncMk1K_MEPbu2CqyafD_BwE
✅ GCP: https://cloud.google.com/free
✅ IBM: https://www.ibm.com/cloud/free

Check out this link for an awesome compilation of your options:
✅ https://github.com/zszazi/Deep-learning-in-cloud

Check out these links for comparisons between cloud options:
✅ https://determined.ai/blog/cloud-v-onprem/
✅ https://medium.com/@akhil.vasvani/what-to-use-for-deep-learning-cloud-services-vs-gpu-385ebaa037ee
✅ https://towardsdatascience.com/maximize-your-gpu-dollars-a9133f4e546a

--- Paid options

Build a budget deep learning PC:
✅ https://www.youtube.com/watch?v=xsnVlMWQj8o&ab_channel=MachineLearningwithPhil
✅ https://www.youtube.com/watch?v=th20fbZfZUc&ab_channel=Pysource

Build your deep learning rig:
✅ https://www.mrdbourke.com/notes-on-building-a-deep-learning-pc/

Why is deep learning rig more cost-effective than the cloud?
✅ https://medium.com/the-mission/why-building-your-own-deep-learning-computer-is-10x-cheaper-than-aws-b1c91b55ce8c

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 

⌚️ Timetable: 
0:00 Intro: Deep Learning is expensive
0:37 First option: Google Colab and Kaggle
1:12 Tradeoffs you make by picking this option
2:00 Second option: Cloud For Free
3:30 ML-specific cloud providers (vs traditional)
4:45 Paid options (budget deep learning PC and beyond)
5:48 Recap - What's the best option for you?
6:27 Grants for researchers and final thoughts

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 
💰 BECOME A PATREON OF THE AI EPIPHANY ❤️ 

If these videos, GitHub projects, and blogs help you,
consider helping me out by supporting me on Patreon! 

The AI Epiphany ► https://www.patreon.com/theaiepiphany
One-time donation: 
https://www.paypal.com/paypalme/theaiepiphany 

Much love! ❤️

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 

💡 The AI Epiphany is a channel dedicated to simplifying the field of AI using creative visualizations and in general, a stronger focus on geometrical and visual intuition, rather than the algebraic and numerical "intuition". 

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 
👋 CONNECT WITH ME ON SOCIAL 
LinkedIn ► https://www.linkedin.com/in/aleksagordic/ 
Twitter ► https://twitter.com/gordic_aleksa 

Instagram ► https://www.instagram.com/aiepiphany/ 
Facebook ► https://www.facebook.com/aiepiphany/ 

👨‍👩‍👧‍👦 JOIN OUR DISCORD COMMUNITY:
Discord ► https://discord.gg/peBrCpheKE

📢 SUBSCRIBE TO MY MONTHLY AI NEWSLETTER:
Substack ► https://aiepiphany.substack.com/

💻 FOLLOW ME ON GITHUB FOR COOL PROJECTS: 
GitHub ► https://github.com/gordicaleksa 

📚 FOLLOW ME ON MEDIUM:
Medium ► https://gordicaleksa.medium.com/ 
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

#deeplearning #gpus #hardware
 Okay, so you started playing with deep learning and you figured out that neural networks take up a lot of VRAM, they are really compute intensive and you'll need some expensive hardware, specifically GPUs in order to do anything meaningful. So in this video I'm going to break it down for you and start with the cheapest options like zero dollars, like free options first, and then in the second part of the video I'll give you some recommendations about the paid options also. So without further ado let's start with the first option. Okay, so the first two options you should consider are both free and they are Google CoLab and Kaggle. So you probably heard of both of them, actually they both belong to Google. Google CoLab is awesome because it gives you GPUs and also TPUs for free and the same goes for Kaggle which gives you P100 GPUs which are actually a bit better than CoLab's because CoLab gives you K80s. There are a lot of tutorials how to get started and I won't get into that part, I'll just give you all the options you have. There are obviously some trade-offs when using Google CoLab and using Kaggle and the main ones is that they disconnect after a certain period. So for CoLab you can expect around 12 hours before it kind of just disconnects your runtime and Kaggle gives you around nine hours of free usage. Now if your GPU goes idle it will basically stop your runs. And there's one more thing you should know and that's that there is no guarantee that CoLab will give you GPUs or TPUs, they have a certain amount of GPUs per region so if people in your region are using CoLab a lot, either for deep learning or maybe mining, then you probably won't get a GPU and that sucks. Okay for the second part of this video I'm going to tell you what your next three options are and that's cloud. And I hear you say wait, cloud? But the thing is most of the big traditional cloud providers such as Azure, such as AWS, Google Cloud Platform or GCP for short, they all give you like 200 to 300 dollars for a month for free and you can kind of use that. So Azure gives you 200 bucks for a month, also IBM gives you 200 dollars for a month, GCP gives you 300 dollars for a month, and finally Alibaba gives you 300 dollars. You also have AWS has some free program but I'm not sure if they have the same thing as these ones I mentioned. I think they only offer CPUs. So doing this you can basically have like you could have cloud GPUs like K80s for four or five months if you just switch between these providers. And it's kind of annoying to switch all the time but you basically need one day to set it up and get started and then you have it free for a month and then you can just switch to the other cloud provider. Doing this you'll acquire important skill and that's using different cloud providers. Later if you want to maybe continue using some of them you can just pay and keep using the provider. So aside from these traditional cloud providers you also have machine learning specific cloud providers such as Spell which I've used and which is really super easy to use, much easier than some of these more traditional cloud providers. There is also Paperspace, there is Floyd Hub, there are a bunch of options there. So Spell gives you 10 bucks for free and you can check for the others. I'll link a bunch of useful resources down in the description. So the thing with these ML specific cloud providers is that they are all based off of like traditional providers. So they use as a backbone they use either AWS or Azure or GCP and so forth. And that was basically it for the free options. So you either use Colab or Kaggle for free forever but obviously it's slower, it sometimes disconnects your runtime so there are some cons there. Or you simply use you switch from cloud provider to cloud provider, you get a lot of skill by doing that and you get some awesome GPU clusters for free. So those were the free options. Now let's jump into some of the paid options which you may consider if you're doing deep learning really seriously. So there are basically two things you could do here. You could either just buy some hardware off the shelf or you could build your own custom PC. You can basically build a deep learning PC for less than thousand bucks and it could be really good. I'll link a couple of those videos for building a budget deep learning PC down in the description. If you have more money you can build obviously better deep learning PC. They also call those deep learning rigs. So basically if you can afford two or three or four thousand dollars you can build a really a beast of a PC. And the reason why you might want to do this is if you're really doing some serious deep learning then you'll either have to pay some cloud based solution or you'll build your custom PC. And there are a bunch of resources out there that show that it's much more cost effective to actually build your own deep learning rig. And I'll also link some of the resources down in the description helping you build the rig for yourself. So at the end it all depends on your preference. If you don't have any budget whatsoever and you want to start with machine learning then go with Kaggle or go with Google CoLab. If you do have some money but you don't want to lose any time you just want something quick just buy some off-the-shelf PC or a deep learning rig. If you do have some money and you also do have some time to spend you can the best thing to do is to build your own deep learning rig. And I think that the worst option if you're an individual playing with deep learning is to be paying the cloud unless you are a startup and you create some kind of a multi-year contract. So that's it for this video. Hope you found it useful. So you have all of those options out there. I'll link a bunch of useful resources down in the description if you want to maybe investigate a bit deeper. But that's like the high level overview of the current hardware, deep learning hardware landscape. So I'd like to know which options out of these did you personally use and also which one works the best for you in your current situation. If you think I've missed something feel free to comment down in the comment section and I'll get back to you. If you found this video useful consider subscribing and hit that like button to get notified when I upload a new video. Until next time keep learning deep.
[{"start": 0.0, "end": 6.72, "text": " Okay, so you started playing with deep learning and you figured out that neural networks take"}, {"start": 6.72, "end": 14.0, "text": " up a lot of VRAM, they are really compute intensive and you'll need some expensive hardware,"}, {"start": 14.0, "end": 19.92, "text": " specifically GPUs in order to do anything meaningful. So in this video I'm going to"}, {"start": 19.92, "end": 27.92, "text": " break it down for you and start with the cheapest options like zero dollars, like free options first,"}, {"start": 27.92, "end": 32.32, "text": " and then in the second part of the video I'll give you some recommendations about the paid"}, {"start": 32.32, "end": 36.56, "text": " options also. So without further ado let's start with the first option."}, {"start": 38.72, "end": 45.52, "text": " Okay, so the first two options you should consider are both free and they are Google CoLab"}, {"start": 45.52, "end": 51.760000000000005, "text": " and Kaggle. So you probably heard of both of them, actually they both belong to Google. Google CoLab"}, {"start": 51.76, "end": 59.92, "text": " is awesome because it gives you GPUs and also TPUs for free and the same goes for Kaggle which gives"}, {"start": 59.92, "end": 66.64, "text": " you P100 GPUs which are actually a bit better than CoLab's because CoLab gives you K80s. There are a"}, {"start": 66.64, "end": 71.68, "text": " lot of tutorials how to get started and I won't get into that part, I'll just give you all the"}, {"start": 71.68, "end": 76.96, "text": " options you have. There are obviously some trade-offs when using Google CoLab and using Kaggle"}, {"start": 76.96, "end": 82.96, "text": " and the main ones is that they disconnect after a certain period. So for CoLab you can expect"}, {"start": 82.96, "end": 90.47999999999999, "text": " around 12 hours before it kind of just disconnects your runtime and Kaggle gives you around nine hours"}, {"start": 90.47999999999999, "end": 98.96, "text": " of free usage. Now if your GPU goes idle it will basically stop your runs. And there's one more"}, {"start": 98.96, "end": 104.88, "text": " thing you should know and that's that there is no guarantee that CoLab will give you GPUs or TPUs,"}, {"start": 104.88, "end": 111.03999999999999, "text": " they have a certain amount of GPUs per region so if people in your region are using CoLab a lot,"}, {"start": 111.03999999999999, "end": 118.32, "text": " either for deep learning or maybe mining, then you probably won't get a GPU and that sucks."}, {"start": 120.8, "end": 127.52, "text": " Okay for the second part of this video I'm going to tell you what your next three options are"}, {"start": 127.52, "end": 134.16, "text": " and that's cloud. And I hear you say wait, cloud? But the thing is most of the big traditional cloud"}, {"start": 134.16, "end": 142.72, "text": " providers such as Azure, such as AWS, Google Cloud Platform or GCP for short, they all give you like"}, {"start": 142.72, "end": 150.48, "text": " 200 to 300 dollars for a month for free and you can kind of use that. So Azure gives you 200 bucks"}, {"start": 150.48, "end": 157.92, "text": " for a month, also IBM gives you 200 dollars for a month, GCP gives you 300 dollars for a month,"}, {"start": 157.92, "end": 164.72, "text": " and finally Alibaba gives you 300 dollars. You also have AWS has some free program but I'm not"}, {"start": 164.72, "end": 171.35999999999999, "text": " sure if they have the same thing as these ones I mentioned. I think they only offer CPUs. So doing"}, {"start": 171.35999999999999, "end": 180.95999999999998, "text": " this you can basically have like you could have cloud GPUs like K80s for four or five months if"}, {"start": 180.96, "end": 188.0, "text": " you just switch between these providers. And it's kind of annoying to switch all the time but you"}, {"start": 188.0, "end": 193.76000000000002, "text": " basically need one day to set it up and get started and then you have it free for a month and then you"}, {"start": 193.76000000000002, "end": 200.4, "text": " can just switch to the other cloud provider. Doing this you'll acquire important skill and that's"}, {"start": 200.4, "end": 206.88, "text": " using different cloud providers. Later if you want to maybe continue using some of them you can just"}, {"start": 206.88, "end": 212.96, "text": " pay and keep using the provider. So aside from these traditional cloud providers you also have"}, {"start": 212.96, "end": 218.4, "text": " machine learning specific cloud providers such as Spell which I've used and which is really super"}, {"start": 218.4, "end": 224.64, "text": " easy to use, much easier than some of these more traditional cloud providers. There is also"}, {"start": 225.6, "end": 231.84, "text": " Paperspace, there is Floyd Hub, there are a bunch of options there. So Spell gives you 10 bucks for"}, {"start": 231.84, "end": 237.44, "text": " free and you can check for the others. I'll link a bunch of useful resources down in the description."}, {"start": 237.44, "end": 241.84, "text": " So the thing with these ML specific cloud providers is that they are all based off of"}, {"start": 242.56, "end": 249.04, "text": " like traditional providers. So they use as a backbone they use either AWS or Azure or GCP"}, {"start": 249.68, "end": 255.44, "text": " and so forth. And that was basically it for the free options. So you either use Colab or Kaggle"}, {"start": 255.44, "end": 262.0, "text": " for free forever but obviously it's slower, it sometimes disconnects your runtime so there are"}, {"start": 262.0, "end": 269.68, "text": " some cons there. Or you simply use you switch from cloud provider to cloud provider, you get a lot"}, {"start": 269.68, "end": 276.32, "text": " of skill by doing that and you get some awesome GPU clusters for free. So those were the free options."}, {"start": 276.32, "end": 282.4, "text": " Now let's jump into some of the paid options which you may consider if you're doing deep learning"}, {"start": 282.4, "end": 291.67999999999995, "text": " really seriously. So there are basically two things you could do here. You could either just buy some"}, {"start": 291.67999999999995, "end": 297.59999999999997, "text": " hardware off the shelf or you could build your own custom PC. You can basically build a deep learning"}, {"start": 297.59999999999997, "end": 303.59999999999997, "text": " PC for less than thousand bucks and it could be really good. I'll link a couple of those videos"}, {"start": 303.59999999999997, "end": 308.64, "text": " for building a budget deep learning PC down in the description. If you have more money you can build"}, {"start": 308.64, "end": 314.8, "text": " obviously better deep learning PC. They also call those deep learning rigs. So basically if you can"}, {"start": 314.8, "end": 322.0, "text": " afford two or three or four thousand dollars you can build a really a beast of a PC. And the reason"}, {"start": 322.0, "end": 328.47999999999996, "text": " why you might want to do this is if you're really doing some serious deep learning then you'll either"}, {"start": 328.47999999999996, "end": 334.96, "text": " have to pay some cloud based solution or you'll build your custom PC. And there are a bunch of"}, {"start": 334.96, "end": 339.76, "text": " resources out there that show that it's much more cost effective to actually build your own deep"}, {"start": 339.76, "end": 345.59999999999997, "text": " learning rig. And I'll also link some of the resources down in the description helping you"}, {"start": 345.59999999999997, "end": 350.88, "text": " build the rig for yourself. So at the end it all depends on your preference. If you don't have any"}, {"start": 350.88, "end": 355.35999999999996, "text": " budget whatsoever and you want to start with machine learning then go with Kaggle or go with Google"}, {"start": 355.35999999999996, "end": 361.35999999999996, "text": " CoLab. If you do have some money but you don't want to lose any time you just want something quick"}, {"start": 361.36, "end": 368.72, "text": " just buy some off-the-shelf PC or a deep learning rig. If you do have some money and you also do"}, {"start": 368.72, "end": 374.24, "text": " have some time to spend you can the best thing to do is to build your own deep learning rig."}, {"start": 374.24, "end": 380.16, "text": " And I think that the worst option if you're an individual playing with deep learning is to be"}, {"start": 380.16, "end": 386.72, "text": " paying the cloud unless you are a startup and you create some kind of a multi-year contract."}, {"start": 386.72, "end": 392.48, "text": " So that's it for this video. Hope you found it useful. So you have all of those options out"}, {"start": 392.48, "end": 397.28000000000003, "text": " there. I'll link a bunch of useful resources down in the description if you want to maybe"}, {"start": 397.28000000000003, "end": 402.72, "text": " investigate a bit deeper. But that's like the high level overview of the current hardware,"}, {"start": 403.76000000000005, "end": 409.12, "text": " deep learning hardware landscape. So I'd like to know which options out of these did you"}, {"start": 409.12, "end": 415.36, "text": " personally use and also which one works the best for you in your current situation."}, {"start": 415.36, "end": 419.92, "text": " If you think I've missed something feel free to comment down in the comment section"}, {"start": 420.32, "end": 425.28000000000003, "text": " and I'll get back to you. If you found this video useful consider subscribing and hit that"}, {"start": 425.28, "end": 446.32, "text": " like button to get notified when I upload a new video. Until next time keep learning deep."}]