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katib NAS trial created for feedforward architecture but no SUGGESTION was created #1561
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Hi @Jaydeemourg and thank you for testing ENAS Algorithm. ENAS Suggestion creates candidates with Model Architecture and NN config based on your Operation Search Space. You can read more about it here: https://github.com/kubeflow/katib/tree/master/pkg/suggestion/v1beta1/nas/enas Then, the input of your Trial container must be In you Trial Training container you should have Model Constructor, like this one: https://github.com/kubeflow/katib/blob/master/examples/v1beta1/nas/enas-cnn-cifar10/ModelConstructor.py, which creates model based on Architecture and NN Config. After that, you can train you model and metrics collector will collect the reward for the Suggestion to train ENAS controller. |
Hello @andreyvelich, thanks for your quick response. i assigned the --architecture and --nn_config as input to my container without Model Constructor. my model building and trained are in the .py script used as Entrypoint of the container. Now i can view the suggestions in the Katib NAS UI. Do i actually need the Model Constructor for a feedforward network like mine since there are no special operation to be done? the example you posted utilized it for CNN. Here is the best architecture; Output of
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If you have feedforward network with different Hypeprameters, like num-layers, batch-size, etc.., why do you want to use ENAS ?
By default for each CNN network we add these 2 layers at the end according to the paper. These layers are just added in the UI: https://github.com/kubeflow/katib/blob/master/pkg/ui/v1beta1/util.go#L282-L287. |
@andreyvelich thanks for your response. i will close this issue. |
/kind bug
What steps did you take and what happened:
i created a training script to train my dnn model, then package the script in an image. A YAML file for the Neural Architecture Search (NAS) experiment was then created. upon running the experiment, the trials created as shown below but no suggestions were made; am i missing something
output of
kubectl logs ai-nalyze-tuning-nas-0-enas-6f5578d676-7cdnk -n ki-user -c suggestion
YAML content;
What did you expect to happen:
the experiment should run successfully
Anything else you would like to add:
Environment:
Kubeflow version: 1.3
Kubernetes version: (use kubectl version --short): v1.20.7
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