Comments (4)
This should be a standard implementation of transformer inference decoding. I'm not sure about further speedup
You may use multiple GPUs to do inference on a batch in parallel.
from texar.
can you guide me how to inference on multiple gpus..?
can we do it on multiple cores on cpu as well..?
from texar.
Just noticed that horovod
(the library used in Texar for distributed training) does not directly support distributed inference at deployment time.
The simplest way then is to launch the program on different GPUs (or CPUs) separately, using different data shads.
from texar.
Same question, thank you.
from texar.
Related Issues (20)
- BERT as encoder and a transformer as a decoder. HOT 2
- Is there anyway to empty cache/memory after loading get-2 into our model?
- "infer_greedy" and "infer_sample" for GPT2 Decoder cannot work correctly HOT 2
- How to get the source data set and target set in PairedTextData?
- Is there anyway to train "big data" using transformer? HOT 1
- In Text-Style-Transfer Example, how do you use the model on a chosen sentence? HOT 5
- TypeError: unsupported operand type(s) for +=: 'float' and 'list' HOT 3
- ModuleNotFoundError: No module named 'texar' HOT 1
- ImportError: No module named 'texar.tf' HOT 1
- ERROR: Directory '.' is not installable. Neither 'setup.py' nor 'pyproject.toml' found. HOT 8
- Text Style transfer - Train on my own dataset HOT 4
- save model and predict
- Importing ABC directly from collections will be removed in Python 3.10
- How do I print the output value of the function tx.modules.MLPTransformConnector()
- ASYML project suggestions
- Output is not as expected(style does not getting transferred)
- Question about the installation about the version 0.2.1 HOT 1
- Performance issues in the program
- pip install -e error
- Dependencies in `requirements.txt` have module conflicts.
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