[Multimodal] Update version bounds #2818
Conversation
sxjscience
changed the title
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For version bounds, we can adopt the guideline to always use: # Packages that have low--up bounds.
PACKAGE_NAME>=LOWER_BOUND,<UPPER_BOUND
# Packages that must be upgraded if it's lower version
PACKAGE_NAME>=LOWER_BOUND
# Most common packages
PACKAGE_NAME |
sxjscience
requested review from
zhiqiangdon,
Innixma,
gradientsky and
bryanyzhu
| "sentencepiece>=0.1.95,<0.2.0", | ||
| f"autogluon.core[raytune]=={version}", | ||
| f"autogluon.features=={version}", | ||
| f"autogluon.common=={version}", | ||
| "pytorch-metric-learning>=1.3.0,<1.4.0", | ||
| "pytorch-metric-learning>=1.3.0,<2.0", |
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@zhiqiangdon I noticed that pytorch-metric-learning released version 2.0. Do we need to consider to update the version?
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I checked and <2 would be enough to get things working. It's not ideal and should be lifted to allow v2, but it's less urgent than lifting the cap from <1.4.
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pytorch-metric-learning seems okay to upgrade.
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@sxjscience Regarding this: how about the following: "numpy", # version range defined in `core/_setup_utils.py`
"pandas", # version range defined in `core/_setup_utils.py`
"torchvision>=foo,<bar",
...I think not everyone on the team is familiar with the fact that we actually are version range bounding those packages like |
| "jinja2>=3.0.3,<3.1", | ||
| "openmim>0.1.5,<0.4.0", | ||
| "defusedxml>=0.7.1,<0.7.2", | ||
| "jinja2>=3.0.3,<3.2", |
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jinja2 needs to be <3.1 or it will error, at least it did in my PR before I dropped to <3.1.
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The error may have been from the requirements_docs dependencies though, so maybe <3.2 is ok for the package dependencies, lets keep it <3.2 and see if it passes
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Okay, let's see if the CI + document build can pass.
Innixma
added
module: multimodal
dependency
| "pytorch_lightning>=1.8.0,<1.10.0", | ||
| "text-unidecode<=1.3", | ||
| "text-unidecode<1.4", | ||
| "torchmetrics>=0.8.0,<0.9.0", |
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Latest version for this is 0.11.1, could the cap be lifted?
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@zhiqiangdon Can we loose the cap of torchmetrics?
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We can't loose the cap of torchmetrics for now. We need to make some changes in our data preprocessing to loos the cap.
| "jsonschema<4.18", | ||
| "seqeval<1.2.3", | ||
| "evaluate<0.4.0", | ||
| "accelerate>=0.9,<0.17", | ||
| "timm<0.7.0", | ||
| "torch>=1.9,<1.14", | ||
| "torchvision<0.15.0", |
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Side note: what happened to the torchtext dependency? 🤔
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We managed to remove it by upgrading lightning... See #2739
| @@ -235,7 +235,7 @@ def collate_fn(self, text_column_names: Optional[List] = None) -> Dict: | |||
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| def build_one_token_sequence( | |||
| self, | |||
| text_tokens: Dict[str, NDArray[(Any,), np.int32]], | |||
| text_tokens: Dict[str, NDArray], | |||
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@zhiqiangdon @cheungdaven I changed the typehints to be just NDArray due to the API change of the latest nptyping.
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NDArray should be fine, but the shape and dtype info are no longer available.
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Job PR-2818-e939343 is done. |
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Job PR-2818-6a538d9 is done. |
Issue #, if available:
#612
Description of changes:
Update the version bounds in multimodal dependencies.
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