Comments (9)
@gslin thanks for reporting this, we've fixed the NumPy version and the problem should not occur on the next tagged release.
@lostella
hello, gluonts python package was just updated on July 23, seems this numpy version problem is still here.
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@gslin thanks for reporting this, we've fixed the NumPy version and the problem should not occur on the next tagged release.
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Maybe its best to just set an upper bound to the allowed numpy version to numpy>=1.14.0,<1.15.0
in the requirements/requirements.txt file.
@whyxiang: could you give this a shot on a cloned repo points to the current master
and applies the suggested fix? The install command should be
pip install ".[shell]"
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FYI, am I the only one that thinks this is (mildly) unexpected - pip should be able to handle this case automatically by resolving all dependencies upfront and computing the intersection of allowed version ranges for dependencies defined through multiple paths.
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@aalexandrov
Hi Alex, I install glutonts 0.3.0 manually by pip install the .whl file downloaded from pypi.org
I am installing both catboost and gluonts packages, catboost requires numpy >=1.15.0 but gluonts requires numpy ==1.14.*
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Maybe its best to just set an upper bound to the allowed numpy version to
numpy>=1.14.0,<1.15.0
in the requirements/requirements.txt file.@whyxiang: could you give this a shot on a cloned repo points to the current
master
and applies the suggested fix? The install command should bepip install ".[shell]"
hello Alex, I pip install using the gluonts-0.3.0.tar.gz downloaded from pypi.org
After extracting files from .tar.gz, I manually edited requirements/requirements.txt file with numpy>=1.14.*, then just python setup.py install
However, the installation cannot be completed. The following warning message shows up, and the installation never moves on
WARNING:root:Package 'sphinx' not found. You will not be able to build the docs.
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It seems that the issue is present not just in gluonts==0.3.0
but also in mxnet==1.4.1
.
Both packages require numpy<1.15.0
, whereas catboost==0.16
requires numpy>=1.16.0
, Strictly speaking, this makes catboost==0.16
incompatible with gluonts==0.3.0
or mxnet==1.4.1
.
The only solution I can suggest is to use catboost==0.14
or lower.
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WARNING:root:Package 'sphinx' not found. You will not be able to build the docs.
This is just a warning, it does not mean that the package is not installed.
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@aalexandrov
Hello Alex,
-
Yes, the only solution at this moment is to downgrade catboost with numpy==0.14, and it works. I am just wondering whether it is possible to have gluonts and mxnet compatible with latest version of numpy in the future so there would not be any conflict between the latestest version of mxnet, gluonts and other py packages.
-
the installation cannot be completed, the warning message is the only thing shows up, no error message. This happens after me manually editing requirement.txt to numpy >=1.14.*.
Anyway, this problem is solved, thanks for help :)
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Related Issues (20)
- The simple example runs to completion, but does not output a plot HOT 1
- `TypeError: list indices must be integers or slices, not tuple` when using `DummyValueImputation` HOT 1
- AssertionError when reproduction of Dlinear model HOT 18
- best way for non-periodicity data? HOT 5
- Update dependencies HOT 2
- Dependency clashes (in kaggle) HOT 1
- Convert ListDataset to Multivariate dataset with gluonts.dataset.multivariate_grouper HOT 2
- Only a single validation batch is used if `estimator.train` called with `cache_data=True` HOT 3
- GluonTSDataError: Input for field "target" does not have the requireddimension (field: target, ndim observed: 2, expected ndim: 1) HOT 5
- Which datasets provided in the library are multivariate? HOT 1
- GPVAREstimator - AssertionError: HOT 3
- Numerical overflow in StdScaler HOT 1
- Training is extremely slow on Gluonts [Torch] HOT 1
- Unify QuantileOutput and DistributionOutput
- Unify the design of seq2seq PyTorch models
- Add support for all covariate types to all PyTorch models
- Change of behaviour from lightning logger HOT 2
- BUG No module named 'gluonts.torch.model.i_transformer' HOT 1
- The annotation of the parameter device of PyTorchPredictor is incomplete HOT 2
- Dataframe index is not uniformly spaced. If your dataframe contains data from multiple series in the same column ("long" format), consider constructing the dataset with `PandasDataset.from_long_dataframe` instead. HOT 1
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