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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/n

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-for-gpus-and-preemptible-local-ssds 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?

wyattk 8y ago
A great and free option are Colab notebooks from Google: https://colab.research.google.com/ https://colab.research.google.com/ You can attach a GPU for free, and, if I recall, even a TPU. See https://colab.research.google.com/notebooks/gpu.ipynb https://colab.research.google.com/notebooks/gpu.ipynb
jachiam 8y ago
The guide doesn't assume access to specialized hardware. Experiments and iterations with all of those algorithms can be done on a normal CPU that anyone has. :) Quote: "Iterate fast in simple environments. To debug your implementations, try them with simple environments where learning should happen quickly, like CartPole-v0, InvertedPendulum-v0, FrozenLake-v0, and HalfCheetah-v2 (with a short time horizon—only 100 or 250 steps instead of the full 1000) from the OpenAI Gym. Don’t try to run an algorithm in Atari or a complex Humanoid environment if you haven’t first verified that it works on the simplest possible toy task. Your ideal experiment turnaround-time at the debug stage is <5 minutes (on your local machine) or slightly longer but not much. These small-scale experiments don’t require any special hardware, and can be run without too much trouble on CPUs."

wyattk 8y ago

A great and free option are Colab notebooks from Google: https://colab.research.google.com/ https://colab.research.google.com/ You can attach a GPU for free, and, if I recall, even a TPU. See https://colab.research.google.com/notebooks/gpu.ipynb https://colab.research.google.com/notebooks/gpu.ipynb

jachiam 8y ago

The guide doesn't assume access to specialized hardware. Experiments and iterations with all of those algorithms can be done on a normal CPU that anyone has. :) Quote: "Iterate fast in simple environments. To debug your implementations, try them with simple environments where learning should happen quickly, like CartPole-v0, InvertedPendulum-v0, FrozenLake-v0, and HalfCheetah-v2 (with a short time horizon—only 100 or 250 steps instead of the full 1000) from the OpenAI Gym. Don’t try to run an algorithm in Atari or a complex Humanoid environment if you haven’t first verified that it works on the simplest possible toy task. Your ideal experiment turnaround-time at the debug stage is <5 minutes (on your local machine) or slightly longer but not much. These small-scale experiments don’t require any special hardware, and can be run without too much trouble on CPUs."