Comments (1)
- If you wanted to see the values predicted by the trained model, you would need to train and save it first. Then load it back during inference. If you are only interested in system performance and therefore using random values is fine, you can call the --inference-only option directly.
- The code will simply pick part of the dataset indicated in option
--raw-data-file
or--processed-data-file
, if you pass your custom dataset there, it will use it.
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Related Issues (20)
- Getting Nan loss when training dlrm with Kaggle Criteo dataset HOT 7
- Question regarding the pooling in QR trick HOT 1
- Issue with Activating UVM Function in torchrec_dlrm
- Size of embedding tables in MLPerf checkpoint
- Segmentation Fault on M1 Mac. HOT 1
- Help with installation. No module named caffe2
- Docker Build failing HOT 1
- Unable to preprocess Criteo Kaggle Display Advertising Challenge Dataset
- What's the purpose of torchrec_dlrm/?
- Correctly changing precision in DLRM HOT 1
- Multi-GPU training does not converge HOT 2
- Bind to cores for inference test
- mini-batch-size can impact inference-only test
- Why does embedding vectors could have various dimensions?
- Significant Variability in DLRM Benchmark Performance Metrics
- Accuracy of Quotient Remainder Method
- 'batched' function looks buggy HOT 1
- test.txt problem HOT 1
- torchrec run failed HOT 1
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