The spinning up guide is neat, but it seems to assume access to fairly expensive GPU resources for running models.
https://cloud.google.com/blog/products/gcp/new-lower-prices-...
> In US regions, each K80 GPU attached to a VM is priced at $0.45 per hour while each P100 costs $1.46 per hour.
The $300 free tier gets you ~600 hours of K80. The spinning up guide suggests iterating models in <5 min, so that's 7200 iterations.
> start with vanilla policy gradient (also called REINFORCE), DQN, A2C (the synchronous version of A3C), PPO (the variant with the clipped objective), and DDPG, ... VPG...
that's 6 algorithms, combined with a half a dozen tasks to try, whittles it down to a few hundred iterations per task/algo combo.
that, combined with a lot of paper-reading, and perhaps clever blogging is probably enough to get started.
Still, it seems beneficial to democratize DL by making these 5 minute iterations free, doesn't it?
https://cloud.google.com/blog/products/gcp/new-lower-prices-...
> In US regions, each K80 GPU attached to a VM is priced at $0.45 per hour while each P100 costs $1.46 per hour.
The $300 free tier gets you ~600 hours of K80. The spinning up guide suggests iterating models in <5 min, so that's 7200 iterations.
> start with vanilla policy gradient (also called REINFORCE), DQN, A2C (the synchronous version of A3C), PPO (the variant with the clipped objective), and DDPG, ... VPG...
that's 6 algorithms, combined with a half a dozen tasks to try, whittles it down to a few hundred iterations per task/algo combo.
that, combined with a lot of paper-reading, and perhaps clever blogging is probably enough to get started.
Still, it seems beneficial to democratize DL by making these 5 minute iterations free, doesn't it?