Adding to this video: I paid for collab pro and I didn't use my credits during the month then I canceled the pro plan to stop paying for something was not using. The compute credits can be used up to 90 days. I used the copute credits with the T4 GPU for feature extraction then after 1 h runtime the execution was terminated by collab. and my credits were used for nothing. It is frustrating that I am experiencing my paid compute credits in the same way that a free plan experiences.

Just discovered your channel. I’m going to try out Paperspace as well. Could you also do a video on how you’re managing your experiment results, model training runs or dataset versions?

Thanks for this review, man. Really helpful for me

Can anyone tell me what exactly does compute unit actually mean?

With gpu prices a bit more manageable now, it seems like building a system makes a lot more sense with deep learning now that colab is no longer worth it.

Thank you very much colab pro how much ram available

does anyone know how much disk colabs pro plus provide to us?

Hi Jeff. How did you get the Googl Colab Runtime type to offer "Python 3.9 (tensorflow)"? I'm trying to get Tensforflow 2.9 with Python 3.8 and am facing challenges. It just defaults to Python3.10 (latest)

Isn't sagemaker studio free?

thank you , please can you explain, what computer units means? its said you have 1300 unites, and for example if I purchase 100 unites, how long can I use that .. lets say I spend 5hr a day to work with Stable Diff.

Runpod good?

can you redo the test now?

Paperspace subscription is quite good, cost 8$ a month and you get to choose the GPU, they also have a free tier

Jeff - This is exactly my frustration with Colab right now. Do you happen to know of a source that compares consumer-grade GPUs to purchase for machine learning? I'm thinking of just buying an Nvidia or AMD GPU to run locally, but all benchmarking sites focus on gaming performance, which may or may not be similar to machine learning.

I would like to build my own PC with dual RTX 4090 for machine learning, may I ask which motherboard and case you would recommend? I can hardly find a board that can fit two RTX 4090.

I'm subscribed to Colab Pro, not Colab Pro+. I get A100-40GB all the time too. Does anyone get any higher? I actually need more VRAM for some heavy deep-learning training.

I am experimenting Colab Pro+ for CV and object identification training and inferencing. I have found loading images from my Google Drive really slows down the training. Is there a way to speed up the image loading process?

How much intense we can run in Google colab pro+ in one time

Thank you for this instructive video

Thanks for your insight. Please do a follow up video on better alternatives.

when you are subscribed as pro+ can you use a non-GPU runtime and leave it in the background (for example for a long gridsearch for a randomforest) and will it not consume your compute units?

Im trying to export 5100 frames 2/3 times a week, do you think Colab pro will work?

I have one question, which one is better collab pro + Services or buy NVIDA RTX 3060 for Research purspose

How do you handle big data uploads to CoLab ? Also with Google Drive?

2024 i get to choose between A100 gpu and V100 gpu, I believe the A100 gpu is more efficient right? Tbh I only bought colab pro+ because of the background running since some of my notebooks tend to have extensive execution times.

Can we connect snowflake database to collab?

Wow, this can get hopefully expensive in a hurry

I had 3 Colab Pro+, but dropped all three after their pricing change. Just wasn’t worth it any more. Paperspace gave more bang.

How about paperspace they have a free start to pay as well?

im a student and google collab pro is not for student, ill recommend google remove the "student" tag from their packages list plan. Too expensive.

Can you run multiple colab sessions at once with Colab Pro?

Lambda labs charges $1.1 per hour for an A100 instance. Colab pro is more expensive at $1.3 per hour. Either way, if someone needs a GPU for full time research they're still better off building a PC with a dedicated GPU.

I find ram is much better in Colab Plus

summarized by Claude+ "Here is a summary of the key points from the video: - Google Colab started as a free service but now charges for GPU access and additional features. The free tier now provides limited, intermittent GPU access. - Colab Pro and Pro Plus are paid subscription plans that provide faster, dedicated GPUs and additional features like a terminal window. However, the specific GPUs and resources provided are not clearly specified. - The video creator finds the lack of transparency around the GPUs and resources provided to be frustrating. The paid plans can end up being expensive if a lot of compute is used, but it's hard to determine if the cost is worthwhile without more details. - Colab Pro Plus provides continuous execution, so notebooks can be stopped and restarted without losing progress. This makes it easy to accidentally run up high compute costs if unchecked. - As of early 2023, Colab Pro Plus appears to primarily provide access to A100 GPUs, which can be quite expensive. The creator would prefer to see lower-cost options like V100 GPUs available as well. - Overall, the creator feels Colab started as a very useful free service but has become less valuable and transparent over time. The paid plans may be worth it for some limited use cases, like accessing the terminal window, but are hard to evaluate fully without more details on the resources provided. - Alternative options mentioned include Kaggle, SageMaker, and managing your own EC2 instances. The key takeaways are: 1) Google Colab's free GPU access and resources have become more limited over time. The paid plans provide faster, dedicated resources but lack transparency into what is actually provided. 2) Colab Pro and Pro Plus can end up being expensive, especially if high-end GPUs like the A100 are used. More cost-effective options would be preferable for many use cases. 3) While still useful for some limited purposes, Colab is not as compelling of an option as it used to be due to the lack of details provided about the paid resources. Alternative services may be better options depending on your needs. 4) If you do use Colab, be very aware of how much you are spending on compute to avoid surprise charges from continuously running or resource-intensive notebooks. Does this summary accurately reflect the key highlights and takeaways from the video? Let me know if you have any feedback or additions. I'm happy to clarify or expand on any part of the summary."

Runpod is about 6 times cheaper.

49,9$ for one month or for three months! Because auto subscription to new 500 computation unit

In my stupid country called India, the gpus are like 3* higher priced than compared to USA. So I'll rather stick with colab. I still use a frickin GT 610 as a programmer and a 3D artist. SMH