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### Describe the feature
Model versioning indicates the version control of ML models. It describes the practice of storing, tracking, and managing the changes in an ML model. It's recommended suppo…
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By logging the version of the model in execution requests and the hashing of input parameters, duplicate execution requests should be noticed. Whenever a second request with the same input parameters …
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I use nested pydantic models in the structured output. ell only keeps track of the first level pydantic model in versioning and tracing.
Example:
class Sub(BaseModel):
fields...
class Main…
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Any given model will have an embedded assumption about the version(s) of backends that will be able to consume it. There may be differences in one or more of:
- serialization format or intermediate …
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**Is your feature request related to a problem? Please describe.**
I would like to have a way to maintain the version history of the models built in Tortoise, currently I have to create different mod…
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**Problem**
Exported models neither have any version information nor verification checks (on loading) that verify that a particular model can be safely executed with the current code version.
**So…
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Currently, deepnog ships one model per eggnog level and network architecture.
If we ever decide to retrain certain models, users need to individually come up with strategies to tell models apart, or …
VarIr updated
4 years ago
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Versioning feels like a detective's case map
- complex versioning library implementation
- usage limitations (limited target, renaming properties,)
- other usage concerns (Mario), complexity due to p…
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Hi, I've downloaded your code and pip installed your requirements.txt
However, when I try to run `python one_file_ref.py`, I get an error stemming from this line in `tokenizer.py`
```
self._mod…