Comments (2)
Hi, it looks like this is an error with nasbench301 because the repository was recently updated. There are a couple options to fix it.
(1) If you are only using nasbench101 or nasbench201, then you can comment out lines 8 and 12 of naszilla/nas_benchmarks.py
for now.
(2) You can make the following changes to the new version of the nasbench301 repository (this version):
-
In
nasbench301/surrogate_models/gnn/models/gcn_lib/sparse/torch_nn.py
, line 3, change the line tofrom nasbench301.surrogate_models
... -
In
nasbench301/surrogate_models/surrogate_model.py
, line 32, change the line to
self.config_loader=utils.ConfigLoader(os.path.expanduser('~/nasbench301/configspace.json'))
- In
nasbench301/surrogate_models/utils.py
, comment out NuSVR/SVR on lines 24-25 and 50-51
I think nasbench301 may be fixed soon, so I will update the naszilla repo when it is fixed.
from naszilla.
Can confirm the suggested changes have fixed the nasbench301 issue, thank you! There're some other issues observed during the test installation. I'll submit them separately. Please feel free to close the issue.
from naszilla.
Related Issues (17)
- nasbench sampling procedure HOT 1
- Path encoding in DARTS search space HOT 2
- Val Accuracy prediction with D-VAE HOT 2
- Questions about porting algorithms to architecture search framework HOT 3
- RuntimeError with GCN predictor in installation test HOT 2
- Dependency installation error on Ubuntu HOT 1
- Cell301 get_paths function HOT 1
- Does Python version matter?
- About the error in the environment configuration
- Encodings experiments didn't work by the given command
- BANANAS genotype HOT 1
- About the Meta Neural Network training method HOT 4
- Why load the nasbench data file each time running? HOT 1
- The bananas method will consume all of the system memory HOT 4
- The search speed of bananas algorithm compare with random is quite solow? HOT 4
- The performance gap between encode path or not encode path HOT 2
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from naszilla.