Answers.Body: <p>To the day, I have found 3 places with free GPU: Colab, Kaggle and gradient.run (haven't tested the last one yet). There are several issues with all of them:</p>
  <ol>
  <li>Usually very weak CPU (even my 10-y.o. laptop has faster than what Colab offers).</li>
  <li>GPU (or TPU, together referred to as &quot;accelerators&quot;) will not be given you for unlimited use (30 hours a week on Kaggle, unpredictable with Colab) (you can try paying for Colab Pro for &quot;increased chances of getting access to an accelerator&quot;). Colab resets after 12 hours of continuous run, erasing everything you haven't saved.</li>
  <li>Not comfortable to study and run small experiments: Colab times out in 15 minutes of not doing anything, and you have to restart everything.</li>
  <li>It's Jupyter notebooks with all its limitations.</li>
  </ol>
  <p>I personally bought not a very expensive one, because I won't be running industry-level experiments, and will probably use Colab for something requiring time, but nothing is ever guaranteed with these free-GPU providers.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-10T20:29:44.517Z
Answers.LastActivityDate: 2022-07-10T20:29:44.517Z
Answers.UserDisplayName: asdfasdfsdf
Body: <p>Assuming I have internet access, should I spend money on a PC or just use Google Colab?
  I'll be doing deep-learning training.</p>
  <p>Google CoLab: <a href="https://colab.research.google.com/" rel="nofollow noreferrer">https://colab.research.google.com/</a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-10T17:46:35.573Z
Comments.Text: I suggest you explain what kinds of AI work you hope to complete, and which options you want to compare. If you **can** identify the work you want to do in enough detailf for someone to understand what resources you need, and if it is mostly machine learning topics. then you may get a better response on Data Science Stack Exchange than here, since AI stack exchange tends to focus on theoretical apects, and Data Science has more experts on practical work.
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-10T19:15:05.887Z
Comments.Text: Thanks for the update. Sorry we will need much more detail than "deeplearning training". What kind of deep models? For example, are you hoping to learn the basics and try out some small-to-medium-size architectures on well-known and toy problems? Do you have any specific interest in trying any of the current cutting-edge research in image generation (like DALL.E), large language models (like GPT-3), or game-playing (like Alpha Go)? Requirements for learning the basics will be much lower than requirements to even try toy versions of cutting edge research.
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-10T19:20:16.907Z
Comments.Text: Separately to any question/answer here, if you really are at the stage of "I would like to try some deep learning, not sure what I will do with it yet", then I expect the free tier of Colab will be fine for you. When you hit some limitation (e.g. you want to train models for longer than a few hours), then the Python/TensorFlow (or PyTorch) knowledge is the same and you can look for other resources - including possible local device - at that stage.
Comments.UserDisplayName: Neil Slater
Tags: <deep-learning><training><gpu>
Title: Should I spend money on a machine-learning capable PC or just use Google CoLab?
UserDisplayName: Daniel Melo Avila
ThreadText: # Should I spend money on a machine-learning capable PC or just use Google CoLab?
  
  (Asked by: username_0 on 2022-07-10)
  
  Assuming I have internet access, should I spend money on a PC or just use Google Colab?
  I'll be doing deep-learning training.
  
  Google CoLab: <https://colab.research.google.com/>
  
  **Tags:** deep-learning, training, gpu
  
  > **Comments:**
  > **username_1**: I suggest you explain what kinds of AI work you hope to complete, and which options you want to compare. If you **can** identify the work you want to do in enough detailf for someone to understand what resources you need, and if it is mostly machine learning topics. then you may get a better response on Data Science Stack Exchange than here, since AI stack exchange tends to focus on theoretical apects, and Data Science has more experts on practical work.  
  > **username_1**: Thanks for the update. Sorry we will need much more detail than "deeplearning training". What kind of deep models? For example, are you hoping to learn the basics and try out some small-to-medium-size architectures on well-known and toy problems? Do you have any specific interest in trying any of the current cutting-edge research in image generation (like DALL.E), large language models (like GPT-3), or game-playing (like Alpha Go)? Requirements for learning the basics will be much lower than requirements to even try toy versions of cutting edge research.  
  > **username_1**: Separately to any question/answer here, if you really are at the stage of "I would like to try some deep learning, not sure what I will do with it yet", then I expect the free tier of Colab will be fine for you. When you hit some limitation (e.g. you want to train models for longer than a few hours), then the Python/TensorFlow (or PyTorch) knowledge is the same and you can look for other resources - including possible local device - at that stage.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-07-10. Score: 3
  
  To the day, I have found 3 places with free GPU: Colab, Kaggle and gradient.run (haven't tested the last one yet). There are several issues with all of them:
  
  1. Usually very weak CPU (even my 10-y.o. laptop has faster than what Colab offers).
  2. GPU (or TPU, together referred to as "accelerators") will not be given you for unlimited use (30 hours a week on Kaggle, unpredictable with Colab) (you can try paying for Colab Pro for "increased chances of getting access to an accelerator"). Colab resets after 12 hours of continuous run, erasing everything you haven't saved.
  3. Not comfortable to study and run small experiments: Colab times out in 15 minutes of not doing anything, and you have to restart everything.
  4. It's Jupyter notebooks with all its limitations.
  
  I personally bought not a very expensive one, because I won't be running industry-level experiments, and will probably use Colab for something requiring time, but nothing is ever guaranteed with these free-GPU providers.
  
  
  ---
  
  