Comments (3)
hongzimao
commented on July 27, 2024
No specific reason. The project was initially developed before Tensorflow 2 was available.
A side story: you might have noticed we had to deal with the static graph in tensorflow even though our problem inherently has dynamic input graph size (since the number of jobs is not pre-determined). Indeed, our best choice was pytorch, but it did not support certain sparse matrix operations that we needed. It was an unfortunate decision that we had to live with Tensorflow 1.x at that time. Later on, we actually implemented a pytorch version of the core graph neural network: https://github.com/hongzimao/gcn_pytorch
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Sijing0608
commented on July 27, 2024
Hi,
It is a very interesing project to me and I'd like to know a runable enviroment setup. Can anyone share it? Thanks!
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hongzimao
commented on July 27, 2024
Hi,
It is a very interesing project to me and I'd like to know a runable enviroment setup. Can anyone share it? Thanks!
This might be helpful: #31 (comment)
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Related Issues (20)
- some questions of your code HOT 9
- The model training issue with reward function optimizing makespan HOT 14
- About nodes information HOT 1
- A question about the result HOT 5
- some question about the main idea
- Some questions about Decima's GNN HOT 2
- A question about actor_network HOT 2
- Questions about the input vector HOT 2
- What are the versions of the project pkg requirement? HOT 6
- Question about multi resource training. HOT 1
- when calculating the reward whether the locality of the data to the core is taken into account? in other word,the data transfer between different nodes may significant affect the reward calculation.
- How to integrate Decima in Spark
- About the model of generation HOT 1
- Question about .npy files
- Some questions about executor
- Code for Learning State Representation
- Could u plz tell me the piplist of this code? HOT 2
- Bug in determining `done` in `env.step`?
- PyTorch Implementation of Decima Available!
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