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 [email protected]
Thanks for reading till here and for your time!
EDIT: Updated formatting.
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.
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.
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.