| 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-startCLI 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 hi@elbo.ai Thanks for reading till here and for your time! EDIT: Updated formatting. |
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.