Comments (7)
It looks like there is a runtime error related to the JAX library. The error message indicates that the version of JAX you have is not compatible with the version required by your code. Quickly Check Google's cloud library version & JAX version requirements.
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I was above to temporarily get past this issue by downgrading the following packages:
!pip install jax==0.4.16
!pip install jaxlib==0.4.16
!pip install flax==0.7.3
from graphcast.
I had this issue too. I tried updgrading jax to a version >= 0.4.14 with !pip install jaxlib==0.4.14
, but this line
from graphcast import casting
still causes the following error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-3-08ed8f8071bd> in <cell line: 13>()
11 from google.cloud import storage
12 from graphcast import autoregressive
---> 13 from graphcast import casting
14 from graphcast import checkpoint
15 from graphcast import data_utils
4 frames
/usr/local/lib/python3.10/dist-packages/graphcast/casting.py in <module>
17 from typing import Any, Mapping, Tuple
18
---> 19 import chex
20 from graphcast import predictor_base
21 import haiku as hk
/usr/local/lib/python3.10/dist-packages/chex/__init__.py in <module>
15 """Chex: Testing made fun, in JAX!"""
16
---> 17 from chex._src.asserts import assert_axis_dimension
18 from chex._src.asserts import assert_axis_dimension_comparator
19 from chex._src.asserts import assert_axis_dimension_gt
/usr/local/lib/python3.10/dist-packages/chex/_src/asserts.py in <module>
24 from unittest import mock
25
---> 26 from chex._src import asserts_internal as _ai
27 from chex._src import pytypes
28 import jax
/usr/local/lib/python3.10/dist-packages/chex/_src/asserts_internal.py in <module>
32
33 from absl import logging
---> 34 from chex._src import pytypes
35 import jax
36 from jax.experimental import checkify
/usr/local/lib/python3.10/dist-packages/chex/_src/pytypes.py in <module>
23 ArrayBatched = jax.interpreters.batching.BatchTracer
24 ArrayNumpy = np.ndarray
---> 25 ArraySharded = jax.interpreters.pxla.ShardedDeviceArray
26 # For instance checking, use `isinstance(x, jax.Array)`.
27 if hasattr(jax, 'Array'):
AttributeError: module 'jax.interpreters.pxla' has no attribute 'ShardedDeviceArray'
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!pip install jax==0.4.16
!pip install jaxlib==0.4.16
!pip install flax==0.7.3
@Shadowwomen see my reply above. I was able to make it work as intended by downgrading the packages to these versions after the original versions were installed.
from graphcast.
I'm still getting the error:
AttributeError: module 'jax.interpreters.pxla' has no attribute 'ShardedDeviceArray'
after trying the fix from @scploeger. Any help would be appreciated.
from graphcast.
I was above to temporarily get past this issue by downgrading the following packages:
!pip install jax==0.4.16 !pip install jaxlib==0.4.16 !pip install flax==0.7.3
This worked for me also. I only needed to downgrade jax and jaxlib
from graphcast.
The version of chex being pointed to is deprecated.
Running
!pip install --upgrade jaxlib
!pip install --upgrade chex
should fix these issues.
Hope this helps!
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Related Issues (20)
- when is the prediction result of this demo? HOT 2
- Forecasting beyond 10 days HOT 8
- Cyclone tracking
- There are some questions about forecasting. HOT 1
- Fine-Tuning Strategy for the GraphCast Operational Model HOT 2
- About the atmospheric variable “Vertical velocity”
- about the autoregressive finetuning
- How to train a model by myself HOT 4
- How get the value from model dataset, such as norm_prediction?
- graphcast intro description needs ERA5 link fixing
- How was the Graphcast Small 1.0 degree Reanalysis made? HOT 1
- RESOURCE_EXHAUSTED HOT 1
- prediciton output time HOT 7
- Performance of GraphCast_small vs regular GraphCast vs GraphCast_operational HOT 1
- May I ask for a multiple batch example nc file?
- Training duration of pre-training model
- Issues with GraphCast Training – Request for Assistance
- Floating Point Error in 1-month forecast HOT 5
- Training: Learning Rate schedule based on iterations rather than epochs HOT 2
- Craphcast HOT 1
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