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Grid2op / grid2op

Grid2Op a testbed platform to model sequential decision making in power systems.
https://grid2op.readthedocs.io/
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Installed packages for the ICAPS 2021 competition #233

Closed BDonnot closed 2 years ago

BDonnot commented 3 years ago

The main installed packages are listed below.

In your submission, you can only use the package listed there.

During the warmup phase, if you need other packages we can see if it's possible to install them and they will be available for the validation and test phases.

The script that scores the submission run on the bdonnot/l2rpn:icaps.2021.4 docker images, you can retrieve it with docker pull bdonnot/l2rpn:icaps.2021.4 and use it locally to be as close as possible to the evaluation script.

Otherwise, you can install the specific version of these packages in a virtual env as explained in the last part of this post.

NB you can use any package you want during training of your agent (if they need training), to find hyper parameters etc. These packages are only used for evaluating how well your submission performs. For example, you could use any RL framework you want to train an agent. And once the policy is trained, you load it using tensorflow and use this tensorflow model in codalab. This causes absolutely no trouble.

Standard packages

Power system / grid2op ecosystem

Deep learning and reinforcement learning libraries

Optimization libraries

Complete list

Here is the list, in no particular order (it can be a pain to install all these packages at these versions due to some dependencies: for example cvxpy works if ecos is installed. But you need to install a specific version of ecos to be compatible with numpy 1.19.5 otherwise you cannot import it)

absl-py==0.13.0

Replicating locally the setup without docker

This is an example on how to set up your machine to be as close as possible to the environment used to score your submission. We show it an example on an Ubuntu machine. You will need to adapt it if you use another operating system.

The most simple way to replicate the scoring environment is through docker by running your script in the bdonnot/l2rpn:icaps.2021.1 image. These explanations are given as an example and WITHOUT ANY GUARANTEE.

Other you can: 

- but wait, there is more
```bash
pip install torch-scatter==2.0.7 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html torch-sparse==0.6.9 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html torch-cluster==1.5.9 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html torch-spline-conv==1.2.1 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html

And now your are good to go.

kamil-kaczmarek commented 3 years ago

hi @BDonnot ,

I just checked the list of packages for the ICAPS 2021 competition. I just wanted to ask if it's ok to add this lib: https://github.com/dmlc/dgl

(install instructions: https://www.dgl.ai/pages/start.html)

BDonnot commented 3 years ago

It makes sense indeed. I'll try to include it for the validation phase (need to check for possible incompatibility issues)

kamil-kaczmarek commented 3 years ago

Great, thank you.

On Sun, Jul 25, 2021, 18:10 Benjamin DONNOT @.***> wrote:

It makes sense indeed. I'll try to include it for the validation phase (need to check for possible incompatibility issues)

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/rte-france/Grid2Op/issues/233#issuecomment-886223790, or unsubscribe https://github.com/notifications/unsubscribe-auth/AGAI3G66I3FUC2PAU3X6B7TTZQZPBANCNFSM47DERR4Q .

BDonnot commented 3 years ago

As a reminder: the original post, BEFORE i updated the packages

DO NOT USE

The main installed packages are listed below.

In your submission, you can only use the package listed there.

During the warmup phase, if you need other packages we can see if it's possible to install them and they will be available for the validation and test phases.

The script that scores the submission run on the bdonnot/l2rpn:icaps.2021.1 docker images, you can retrieve it with docker pull bdonnot/l2rpn:icaps.2021.1 and use it locally to be as close as possible to the evaluation script.

Otherwise, you can install the specific version of these packages in a virtual env as explained in the last part of this post.

NB you can use any package you want during training of your agent (if they need training), to find hyper parameters etc. These packages are only used for evaluating how well your submission performs. For example, you could use any RL framework you want to train an agent. And once the policy is trained, you load it using tensorflow and use this tensorflow model in codalab. This causes absolutely no trouble.

Standard packages

Power system / grid2op ecosystem

Deep learning and reinforcement learning libraries

Optimization libraries

Complete list

Here is the list, in no particular order (it can be a pain to install all these packages at these versions due to some dependencies: for example cvxpy works if ecos is installed. But you need to install a specific version of ecos to be compatible with numpy 1.19.5 otherwise you cannot import it)

Replicating locally the setup without docker

This is an example on how to set up your machine to be as close as possible to the environment used to score your submission. We show it an example on an Ubuntu machine. You will need to adapt it if you use another operating system.

The most simple way to replicate the scoring environment is through docker by running your script in the bdonnot/l2rpn:icaps.2021.1 image. These explanations are given as an example and WITHOUT ANY GUARANTEE.

Other you can: 

- but wait, there is more
```bash
pip install torch-scatter==2.0.7 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html torch-sparse==0.6.9 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html torch-cluster==1.5.9 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html torch-spline-conv==1.2.1 -f https://pytorch-geometric.com/whl/torch-1.8.0+cpu.html

And now your are good to go.

End Reminder

END DO NOT USE