Comments (9)
Here are our trained_models. We trained 23000 epochs for 10 days on a server with 56 cores CPU and 4 V100 GPUs. (For training reference. All settings are as default.)
We also perform more evaluations on our trained models: (For testing reference. Each evaluation takes about 30 minutes.)
Scheduler | Avg. JCT | Executor Usage |
---|---|---|
RL 100 | 99540 | 0.6392 |
RL 500 | 89455 | 0.6397 |
RL 1000 | 62610 | 0.6456 |
RL 2000 | 58918 | 0.6493 |
RL 6000 | 56739 | 0.6665 |
RL 10000 | 57241 | 0.6634 |
RL 10900 | 105744 | 0.7236 |
RL 11000 | 66811 | 0.6396 |
RL 12000 | 62434 | 0.6886 |
RL 15000 | 73518 | 0.6029 |
RL 16000 | 61234 | 0.6694 |
RL 18000 | 61145 | 0.6629 |
RL 20000 | 65519 | 0.6518 |
RL 22000 | 61105 | 0.6689 |
It seems not stable for RL training and the performances are not much better than Dynamic Partition. I want to know how to choose the best model checkpoint without testing (which metric in the tensorboard is the most significant one)? Do you have any insight? Thank you.
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This link seems expired, would you please have a check?
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The link is indeed expired. I lost the access to MIT dropbox to retrieve the model since graduation. I searched my local storage but unfortunately I couldn't find the exact trained model. However,I believe there were others downloaded this model before. Can someone upload the model here? Thanks a lot!
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Here are some models I was able to retrieve from our local machine. Although the creation time of the model is a few months prior to this post, It should be in a similar setting. I'm attaching a few model snapshot at after 20,000 iterations. Check if the performance is good: models.zip
Still, if someone has the original model in this post, please do contact us with a copy. We will upload it here. Thanks!
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Thanks for your reply.
I spend some time testing the performance (RL models with 20000 + epochs are given by you):
Scheduler | Avg. JCT | Executor Usage |
---|---|---|
FIFO | 1842803 | 0.8197 |
Dynamic Partition | 62783 | 0.7074 |
RL 20000 | 61190 | 0.6456 |
RL 24000 | 64663 | 0.6469 |
RL 25000 | 64044 | 0.6399 |
RL 26000 | 63494 | 0.6462 |
RL 10000 (Trained by us) | 57241 | 0.6634 |
All settings are as default. Does this performance meet expectations?
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Looks like your trained model performs better :) would you mind sharing the model so that others may use it too? Thank you!
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@Tonyhao96 Would you like to give me some instructions on which command syntax did you used to train your model and how did you compare the performance?
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@jahidhasanlinix I just use the command provided by the authors without modification. I trained Decima several months ago and I totally forget the details.
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@Tonyhao96 Thank you for your response.
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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
- Updating Tensorflow 1.14 to 2 HOT 3
- 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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