srelbo

2 năm trướcdiscuss

We have been a Tensordock customer for more than two years now. We run our celery nodes on Tensordock — along side other nodes on AWS (startup credits). The tensordock nodes always spin up faster and load our models faster than AWS (some strange IOPS limits).

Also their customer service is insane. We get usually get replies within a few mins even at 1AM at night!

Question — Do you guys plan to have some sort of managed K8 service in the future ? Something like GKE ?

srelbo

4 năm trướcdiscuss

The best part of Clerky is their support team, makes our life as first time founders so much easier! Super happy customers, thank you for building something so awesome.

srelbo

4 năm trướcdiscuss

oh and shameless plug (I know I will get downvoted) art.elbo.ai, but see our public gallery of images people create with free tools.

srelbo

4 năm trướcdiscuss

You can also play around for free on a slightly less sophisticated model here https://art.elbo.ai

srelbo

4 năm trướcdiscuss

thanks for the feedback, we will fix it.

srelbo

4 năm trướcdiscuss

Also it’s multilingual and completely free to use.

srelbo

4 năm trướcdiscuss

AI art generation, completely free, no limits — https://art.elbo.ai

srelbo

4 năm trướcdiscuss

Hi HN,

If you have long-running Python tasks, like model training or evaluation runs, you may find our App useful.

Using a simple Python Api [1] you can log messages, numbers, and images. These will show up on the App [2].

Here is an example, you can also try it on Replit [4].

  from elbo.tracker.tracker import TaskTracker
  tracker = TaskTracker("Hello World")

  # Log a message
  tracker.log_message("Hi there!  Stay strong ")

  # Log a metric
  tracker.log_key_metric("Accuracy", 100.0)

  # Log an image
  tracker.log_image(image_title="We can do it!",
                    image_file_path="WeCanDoIt.jpeg")

  # Upload logs
  tracker.upload_logs()

Would appreciate your candid feedback, thanks!

[1] API - https://docs.elbo.ai/reference/api-reference/the-elbo-tracke...

[2] App - https://apps.apple.com/us/app/elbo-tracker/id1625604999

[3] Website - https://www.elbo.ai/tracker

[4] Replit demo - https://replit.com/@SaravanaRathina/ELBO-Tracker-Demo

(edited to fix formatting and add links)

srelbo

4 năm trướcdiscuss

> I am very motivated to delve into this space (it's been on my mind a while) and I want to do it right, which is why I am asking for personal experiences on this forum given that there is a very healthy mix of technology hobbyists as well as professionals on HN, of which the opinion of both is equally valuable to me for different reasons.

Regarding personal experiences, I moved to ML engineering after almost 15 years in Software Development. I found it challenging at first, to cope up with the terminology and Math. Although I was able to create data processing pipelines and simple models it was still a mystery how it all worked. After a good year and a half of trying to teach myself ML, I decided that I needed formal education. After researching possible options that work would for my work schedule and skill level, the Stanford SCPD AI Certificate program seemed to be the best. Here are some useful pointers (in no particular order).

- This blog by Pavel helped me a lot, to understand what the course was about and how to approach it -- http://coldattic.info/post/122/

- Most of Stanford Lectures notes and slides are publicly available. CS229 is a good beginner class to take (http://cs229.stanford.edu/syllabus.html)

- The best and the most interesting IMO, is CS236 on Generative Modeling. It is taught by Prof. Ermon and his team. Some of the topics covered in class (especially Score based models) were mind blowing. Here is a talk by Prof. Ermon if you are interested in generative modeling (https://www.youtube.com/watch?v=8TcNXi3A5DI).

- If your math skills are a bit rusty, then you will have to practice and work a lot more. I found the TA sessions and office hours extremely helpful.

Some additional personal experiences:

- "Deep Learning with Python" by Francois Chollet (Creator of Keras) is a good book to get started. The code samples are in TF Keras and easy to understand and implement.

- Avoid TensorFlow if you can. Its unnecessarily complicated (personal opinion). You will find PyTorch and PyTorch Lightning much more approachable to start learning.

- I also found Kaggle tutorials helpful for practical aspects of ML. For example: Categorial Variables (https://www.kaggle.com/alexisbcook/categorical-variables).

- Yannic Kilcher's ML News series is a great way to keep in touch with the latest events in ML (https://www.youtube.com/c/YannicKilcher). Also very entertaining :)

- Prof. Jeff Heaton has a bunch of good videos on practical ML applications - https://www.youtube.com/c/HeatonResearch

ML is very exciting and rewarding, Good luck on your new adventure! Feel to reach out and I would be happy to help in any way.

srelbo

4 năm trướcdiscuss

Please give us a try:

https://elbo.ai - Train more. Pay less

We want to make ML tasks as cheap and as easy as possible. We can provision GPU nodes from multiple cloud providers (today we have 4 - TensorDock, AWS, Linode and FluidStack). You don't have to sign up with them, manage keys or passwords, AMI Images, VPCs, Subnets, Firewall rules, EBS volumes or worry about Colab closing your session, network transfer bills, GPU usage approvals, opening ports, billing surprises. We take care of all that and let you focus on learning ML.

I faced the same problem when I started learning ML and tried different cloud providers, Colab, Paperspace, custom PC with RTX30 series GPU. Most of the solutions were either very expensive or very complicated. I started building a tool for myself to deploy GPU nodes with a single command and thought it would be a nice product to have for other ML learners like me.

  1. Sign up at https://elbo.ai for the free tier.
  2. `pip3 install elbo`
  3. `elbo login` with your token (from signup)
  4. Jupyter Notebook in a single command in under 4 minutes (typically)- `elbo notebook`
  5. Setup a GPU node to work remotely over SSH using `elbo create`
  6. Submit ML tasks defined in a YAML file using `elbo run --config <config_file>`

Quick start guide - https://docs.elbo.ai/quick-start

CLI Reference - https://docs.elbo.ai/reference/cli-reference

Looking at our inventory today, you can get a decent Quadro 4000 GPU with 16 CPU and 32 GB memory for about $0.61 an hour.

    PRICE                 GPU CPU   MEM  GPU-MEM 
    $ 0.2700/h      Tesla K80   4   61Gb 12Gb AWS (spot)
    $ 0.6100/h    Quadro 4000  16   32Gb  8Gb TensorDock
    $ 0.9000/h      Tesla K80   4   61Gb 12Gb AWS
    $ 0.9180/h           V100   8   61Gb 16Gb AWS (spot)
    $ 0.9200/h    Quadro 5000   2    4Gb 16Gb FluidStack
    $ 0.9600/h          A5000   2   16Gb 24Gb TensorDock
    $ 1.4900/h          A4000  12   64Gb 16Gb FluidStack
    $ 1.4940/h            A40   2   12Gb 48Gb TensorDock
    $ 1.5000/h    Quadro 6000   8   32Gb  0Gb Linode 
    $ 1.5140/h          A6000   2   16Gb 48Gb TensorDock
    $ 2.1600/h   8x Tesla K80  32  488Gb 12Gb AWS (spot)
    $ 3.0000/h 2x Quadro 6000  16   64Gb  0Gb Linode 
    $ 3.0600/h           V100   8   61Gb 16Gb AWS
    $ 3.6720/h        4x V100  32  244Gb 16Gb AWS (spot)
    $ 3.7460/h        7x V100   6    8Gb 16Gb TensorDock
    $ 4.3200/h  16x Tesla K80  64  732Gb 12Gb AWS (spot)
    $ 4.5000/h 3x Quadro 6000  20   96Gb  0Gb Linode 
    $ 6.0000/h 4x Quadro 6000  24  128Gb  0Gb Linode 
    $ 7.3440/h        8x V100  64  488Gb 16Gb AWS (spot)
    $ 7.9200/h   8x Tesla K80  32  488Gb 12Gb AWS
    $ 9.8318/h        8x A100  96 1152Gb 80Gb AWS (spot)
    $13.0360/h        4x V100  32  244Gb 16Gb AWS
    $14.4000/h  16x Tesla K80  64  732Gb 12Gb AWS
    $24.4800/h        8x V100  64  488Gb 16Gb AWS
    $32.7726/h        8x A100  96 1152Gb 80Gb AWS

If you just need a dedicated machine on the cloud, then I would highly recommend our provider - Tensordock (https://tensordock.com/). They have a good range of ML capable GPUs and are cheaper than many other cloud providers.

We are just getting started, so if you hit any glitches or bugs, please email us at [email protected]

Thanks for reading till here and for your time!

EDIT: Updated formatting.

Submissions

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