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[AWS] RAPIDS 24.10/24.12 and the sagemaker library can't be installed together #443

Closed
jameslamb opened this issue Oct 7, 2024 · 9 comments · Fixed by #504
Closed

[AWS] RAPIDS 24.10/24.12 and the sagemaker library can't be installed together #443

jameslamb opened this issue Oct 7, 2024 · 9 comments · Fixed by #504
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bug Something isn't working cloud/aws Amazon Web Service cloud

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@jameslamb
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Description

AWS distributes a Python client for SageMaker called sagemaker-sdk-python (aws/sagemaker-python-sdk).

That library's latest versions have a pin of cloudpickle==2.2.1

RAPIDS 24.10 depends on distributed==2024.9.0 (code link) which pins to cloudpickle>=3.0.0.

Because of that, it's not possible to install sagemaker together with RAPIDS 24.10+ libraries that depend on Dask.

Reproducible Example

docker run \
    --rm \
    -it rapidsai/base:24.10a-cuda12.5-py3.11 \
    bash

conda install -y -n base \
    'sagemaker>=2.198'

Results in this solver error:

Could not solve for environment specs
The following packages are incompatible
├─ pin-1 is installable and it requires
│  └─ python 3.11.* , which can be installed;
├─ rapids 24.10**  is installable with the potential options
│  ├─ rapids 24.10.00a would require
│  │  └─ dask-cuda 24.10.*  with the potential options
│  │     ├─ dask-cuda 24.10.00a23 would require
│  │     │  └─ rapids-dask-dependency 24.10.*,>=0.0.0a0 , which requires
│  │     │     └─ distributed 2024.9.0 , which requires
│  │     │        └─ cloudpickle >=3.0.0 , which can be installed;
│  │     ├─ dask-cuda 24.10.00a23 would require
│  │     │  └─ python >=3.10,<3.11.0a0 , which conflicts with any installable versions previously reported;
│  │     └─ dask-cuda 24.10.00a23 would require
│  │        └─ python >=3.12,<3.13.0a0 , which conflicts with any installable versions previously reported;
│  ├─ rapids 24.10.00a would require
│  │  └─ python >=3.10,<3.11.0a0 , which conflicts with any installable versions previously reported;
│  └─ rapids 24.10.00a would require
│     └─ python >=3.12,<3.13.0a0 , which conflicts with any installable versions previously reported;
└─ sagemaker >=2.198  is not installable because it requires
   └─ cloudpickle 2.2.1 , which conflicts with any installable versions previously reported.

Notes

Upstream issue about relaxing that cloudpickle pin: aws/sagemaker-python-sdk#4871

@jameslamb jameslamb added bug Something isn't working cloud/aws Amazon Web Service cloud labels Oct 7, 2024
@jacobtomlinson jacobtomlinson changed the title [AWS] RAPIDS 24.10 and the sagemaker library can't be installed together [AWS] RAPIDS 24.10/24.12 and the sagemaker library can't be installed together Dec 10, 2024
@jameslamb
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The cloudpickle pin has been relaxed upstream. Put up a PR to fix that in the newer, more-actively-maintained conda-forge feedstock: conda-forge/sagemaker-python-sdk-feedstock#315

When that gets merged, we should switch {conda,mamba} install sagemaker to {conda,mamba} install sagemaker-python-sdk and hopefully that will resolve this issue.

@claytonparnell
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sagemaker-python-sdk with updated pin is now available at https://anaconda.org/conda-forge/sagemaker-python-sdk/files

@jakirkham
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Thanks Clayton! 🙏

@jameslamb jameslamb self-assigned this Jan 16, 2025
@claytonparnell
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Want to call out, after updating the pins sagemaker-python-sdk was no longer compatible with Python 3.11 (aws/sagemaker-python-sdk#4995). This is fixed by aws/sagemaker-python-sdk#4997 , and updated version to support Py311 should come this week.

@jameslamb
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Thanks very much!

@jameslamb
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It looks to me like we actually can install RAPIDS 25.02 alongside sagemaker-python-sdk on Python 3.11 already.

mamba create -y -n rapids-py311 -c rapidsai-nightly -c conda-forge -c nvidia rapids=25.02 python=3.11 cuda-version=12.5 \
    boto3 \
    ipykernel \
    sagemaker-python-sdk

conda just falls back to an older version (2.109.0, which is 2+ years old).

logs (click me)
Looking for: ['rapids=25.02', 'python=3.11', 'cuda-version=12.5', 'boto3', 'ipykernel', 'sagemaker-python-sdk']

conda-forge/linux-64                                        Using cache
conda-forge/noarch                                          Using cache
nvidia/linux-64                                             Using cache
nvidia/noarch                                               Using cache
pytorch/linux-64                                            Using cache
pytorch/noarch                                              Using cache
rapidsai-nightly/noarch                                       No change
rapidsai-nightly/linux-64                                     No change
Transaction

  Prefix: /home/ec2-user/anaconda3/envs/rapids-py311

  Updating specs:

   - rapids=25.02
   - python=3.11
   - cuda-version=12.5
   - boto3
   - ipykernel
   - sagemaker-python-sdk


  Package                                    Version  Build                                Channel                Size
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
  Install:
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

  + cuda-version                                12.5  hd4f0392_3                           conda-forge          Cached
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  + cuda-cudart-dev_linux-64                 12.5.82  h85509e4_0                           conda-forge          Cached
  + python_abi                                  3.11  5_cp311                              conda-forge          Cached
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  + libnvjpeg                              12.3.2.81  he02047a_0                           conda-forge          Cached
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  + libcufft                               11.2.3.61  he02047a_0                           conda-forge          Cached
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  + cuda-nvrtc                               12.5.82  he02047a_0                           conda-forge          Cached
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  + rapids-xgboost                         25.02.00a  cuda12_py311_250122_g172ef62_7       rapidsai-nightly     Cached
  + cucim                                25.02.00a11  cuda12_py311_250117_g9477daa_11      rapidsai-nightly     Cached
  + raft-dask                            25.02.00a40  cuda12_py311_250117_g097ac45f_40     rapidsai-nightly     Cached
  + cugraph                              25.02.00a72  cuda12_py311_250117_g0b50bf925_72    rapidsai-nightly     Cached
  + cuml                                 25.02.00a48  cuda12_py311_250117_gf29293ffd_48    rapidsai-nightly     Cached
  + jupyter-server-proxy                       4.4.0  pyhd8ed1ab_1                         conda-forge          Cached
  + cuxfilter                             25.02.00a6  cuda12_py311_250123_g6237ba1_6       rapidsai-nightly     Cached
  + rapids                                 25.02.00a  cuda12_py311_250122_g172ef62_7       rapidsai-nightly     Cached

  Summary:

  Install: 447 packages

  Total download: 0 B

Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: / 
To enable CUDA support, UCX requires the CUDA Runtime library (libcudart).
The library can be installed with the appropriate command below:

* For CUDA 11, run:    conda install cudatoolkit cuda-version=11
* For CUDA 12, run:    conda install cuda-cudart cuda-version=12


done

To activate this environment, use

     $ mamba activate rapids-py311

To deactivate an active environment, use

     $ mamba deactivate

But yes for Python 3.12 or to use the latest sagemaker-python-sdk, we'll need the looser omegaconf pin to make it into the sagemaker-python-sdk packages conda-forge packages.

mamba create -y -n rapids -c rapidsai-nightly -c conda-forge -c nvidia rapids=25.02 python=3.11 cuda-version=12.5 \
    boto3 \
    ipykernel \
    'sagemaker-python-sdk>=2.237.3'
├─ python 3.12**  is installable with the potential options
│  ├─ python [3.12.0|3.12.1|...|3.12.8] would require
│  │  └─ python_abi 3.12.* *_cp312, which can be installed;
│  └─ python 3.12.0rc3 would require
│     └─ _python_rc, which does not exist (perhaps a missing channel);
└─ sagemaker-python-sdk >=2.237.3  is not installable because it requires
   └─ omegaconf >=2.2,<2.3  but there are no viable options
      ├─ omegaconf [2.2.2|2.2.3] would require
      │  └─ python_abi 3.10.* *_cp310, which conflicts with any installable versions previously reported;
      ├─ omegaconf [2.2.2|2.2.3] would require
      │  └─ python_abi 3.7.* *_cp37m, which conflicts with any installable versions previously reported;
      ├─ omegaconf [2.2.2|2.2.3] would require
      │  └─ python_abi 3.8 *_pypy38_pp73, which conflicts with any installable versions previously reported;
      ├─ omegaconf [2.2.2|2.2.3] would require
      │  └─ python_abi 3.8.* *_cp38, which conflicts with any installable versions previously reported;
      ├─ omegaconf [2.2.2|2.2.3] would require
      │  └─ python_abi 3.9 *_pypy39_pp73, which conflicts with any installable versions previously reported;
      ├─ omegaconf [2.2.2|2.2.3] would require
      │  └─ python_abi 3.9.* *_cp39, which conflicts with any installable versions previously reported;
      └─ omegaconf 2.2.3 would require
         └─ python >=3.7,<3.11 , which conflicts with any installable versions previously reported.

@jameslamb
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@claytonparnell since aws/sagemaker-python-sdk#4997 just relaxed the pin and didn't seem to require code changes, could that same change just be made in the conda-forge recipe, without waiting for a new release of https://github.com/aws/sagemaker-python-sdk ?

@jameslamb
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could that same change just be made in the conda-forge recipe, without waiting for a new release of https://github.com/aws/sagemaker-python-sdk?

Oh cool, looks like that happened in conda-forge/sagemaker-python-sdk-feedstock#317

And now the latest RAPIDS is installable alongside the latest sagemaker-python-sdk 🎉

mamba create -y -n rapids -c rapidsai-nightly -c conda-forge -c nvidia rapids=25.02 python=3.11 cuda-version=12.8 \
    boto3 \
    ipykernel \
    'sagemaker-python-sdk>=2.239.0'
...
+ cuda-version                                12.8  h5d125a7_3                           conda-forge            21kB
+ cudf                                25.02.00a367  cuda12_py311_250205_g0136e2411b_367  rapidsai-nightly        2MB
...
+ python                                   3.11.11  h9e4cc4f_1_cpython                   conda-forge            31MB
...
+ python_abi                                  3.11  5_cp311                              conda-forge             6kB
...
+ rapids                                 25.02.00a  cuda12_py311_250128_g057e371_9       rapidsai-nightly        7kB
...
+ sagemaker-core                            1.0.21  pyhd8ed1ab_0                         conda-forge           256kB
+ sagemaker-python-sdk                     2.239.0  pyhff2d567_0                         conda-forge           810kB
...

That's the latest cudf 25.02 nightly (https://anaconda.org/rapidsai-nightly/cudf/files), so that's a good sign that no other constraints caused the solver to fall back (which would mean that the non-nightly 25.02.00 release would have issues).

output of 'conda env export --name rapids' (click me)
name: rapids
channels:
  - conda-forge
  - rapidsai-nightly
  - rapidsai
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=2_gnu
  - aiohappyeyeballs=2.4.4=pyhd8ed1ab_1
  - aiohttp=3.11.11=py311h2dc5d0c_0
  - aiosignal=1.3.2=pyhd8ed1ab_0
  - annotated-types=0.7.0=pyhd8ed1ab_1
  - antlr-python-runtime=4.9.3=pyhd8ed1ab_1
  - anyio=4.8.0=pyhd8ed1ab_0
  - aom=3.9.1=hac33072_0
  - argon2-cffi=23.1.0=pyhd8ed1ab_1
  - argon2-cffi-bindings=21.2.0=py311h9ecbd09_5
  - arrow=1.3.0=pyhd8ed1ab_1
  - asttokens=3.0.0=pyhd8ed1ab_1
  - attr=2.5.1=h166bdaf_1
  - attrs=23.2.0=pyh71513ae_0
  - aws-c-auth=0.8.0=hb88c0a9_10
  - aws-c-cal=0.8.0=hecf86a2_2
  - aws-c-common=0.10.3=hb9d3cd8_0
  - aws-c-compression=0.3.0=hf42f96a_2
  - aws-c-event-stream=0.5.0=h1ffe551_7
  - aws-c-http=0.9.1=hab05fe4_2
  - aws-c-io=0.15.2=hdeadb07_2
  - aws-c-mqtt=0.11.0=h7bd072d_8
  - aws-c-s3=0.7.2=h3a84f74_0
  - aws-c-sdkutils=0.2.1=hf42f96a_1
  - aws-checksums=0.2.2=hf42f96a_1
  - aws-crt-cpp=0.29.5=h0e61686_1
  - aws-sdk-cpp=1.11.449=hdaa582e_3
  - azure-core-cpp=1.14.0=h5cfcd09_0
  - azure-identity-cpp=1.10.0=h113e628_0
  - azure-storage-blobs-cpp=12.13.0=h3cf044e_1
  - azure-storage-common-cpp=12.8.0=h736e048_1
  - azure-storage-files-datalake-cpp=12.12.0=ha633028_1
  - bcrypt=4.2.1=py311h9e33e62_0
  - beautifulsoup4=4.13.3=pyha770c72_0
  - bleach=6.2.0=pyh29332c3_4
  - bleach-with-css=6.2.0=h82add2a_4
  - blosc=1.21.6=hef167b5_0
  - bokeh=3.6.2=pyhd8ed1ab_1
  - boto3=1.36.13=pyhd8ed1ab_0
  - botocore=1.36.13=pyge310_1234567_0
  - branca=0.8.1=pyhd8ed1ab_0
  - brotli=1.1.0=hb9d3cd8_2
  - brotli-bin=1.1.0=hb9d3cd8_2
  - brotli-python=1.1.0=py311hfdbb021_2
  - brunsli=0.1=h9c3ff4c_0
  - bzip2=1.0.8=h4bc722e_7
  - c-ares=1.34.4=hb9d3cd8_0
  - c-blosc2=2.15.2=h68e2383_0
  - ca-certificates=2025.1.31=hbcca054_0
  - cached-property=1.5.2=hd8ed1ab_1
  - cached_property=1.5.2=pyha770c72_1
  - cachetools=5.5.1=pyhd8ed1ab_0
  - certifi=2024.12.14=pyhd8ed1ab_0
  - cffi=1.17.1=py311hf29c0ef_0
  - charls=2.4.2=h59595ed_0
  - charset-normalizer=3.4.1=pyhd8ed1ab_0
  - click=8.1.8=pyh707e725_0
  - cloudpickle=3.1.1=pyhd8ed1ab_0
  - colorama=0.4.6=pyhd8ed1ab_1
  - colorcet=3.1.0=pyhd8ed1ab_1
  - comm=0.2.2=pyhd8ed1ab_1
  - contextlib2=21.6.0=pyhd8ed1ab_1
  - contourpy=1.3.1=py311hd18a35c_0
  - cryptography=44.0.0=py311hafd3f86_1
  - cucim=25.02.00a16=cuda12_py311_250205_g0adb828_16
  - cuda-bindings=12.8.0=py311h65d80dc_0
  - cuda-cccl_linux-64=12.8.55=ha770c72_0
  - cuda-crt-dev_linux-64=12.8.61=ha770c72_0
  - cuda-crt-tools=12.8.61=ha770c72_0
  - cuda-cudart=12.8.57=h5888daf_0
  - cuda-cudart-dev=12.8.57=h5888daf_0
  - cuda-cudart-dev_linux-64=12.8.57=h3f2d84a_0
  - cuda-cudart-static=12.8.57=h5888daf_0
  - cuda-cudart-static_linux-64=12.8.57=h3f2d84a_0
  - cuda-cudart_linux-64=12.8.57=h3f2d84a_0
  - cuda-nvcc-dev_linux-64=12.8.61=he91c749_0
  - cuda-nvcc-impl=12.8.61=h85509e4_0
  - cuda-nvcc-tools=12.8.61=he02047a_0
  - cuda-nvrtc=12.8.61=hbd13f7d_0
  - cuda-nvvm-dev_linux-64=12.8.61=ha770c72_0
  - cuda-nvvm-impl=12.8.61=he02047a_0
  - cuda-nvvm-tools=12.8.61=he02047a_0
  - cuda-profiler-api=12.8.55=h7938cbb_0
  - cuda-python=12.8.0=pyh624ad82_1
  - cuda-version=12.8=h5d125a7_3
  - cudf=25.02.00a367=cuda12_py311_250205_g0136e2411b_367
  - cudf-polars=25.02.00a367=cuda12_py311_250205_g0136e2411b_367
  - cudf_kafka=25.02.00a367=cuda12_py311_250205_g0136e2411b_367
  - cugraph=25.02.00a96=cuda12_py311_250205_g20ddb5381_96
  - cuml=25.02.00a70=cuda12_py311_250205_gdef265e8e_70
  - cuproj=25.02.00a28=cuda12_py311_250205_g57a34d7a_28
  - cupy=13.3.0=py311h1c6efab_2
  - cupy-core=13.3.0=py311ha4ffafd_2
  - cuspatial=25.02.00a28=cuda12_py311_250205_g57a34d7a_28
  - custreamz=25.02.00a367=cuda12_py311_250205_g0136e2411b_367
  - cuvs=25.02.00a116=cuda12_py311_250205_gd7c258e_116
  - cuxfilter=25.02.00a8=cuda12_py311_250205_g49cb3c0_8
  - cycler=0.12.1=pyhd8ed1ab_1
  - cyrus-sasl=2.1.27=h54b06d7_7
  - cytoolz=1.0.1=py311h9ecbd09_0
  - dask=2024.12.1=pyhd8ed1ab_0
  - dask-core=2024.12.1=pyhd8ed1ab_0
  - dask-cuda=25.02.00a25=py311_250205_g4b3ad71_25
  - dask-cudf=25.02.00a367=cuda12_py311_250205_g0136e2411b_367
  - dask-expr=1.1.21=pyhd8ed1ab_0
  - datashader=0.17.0=pyhd8ed1ab_0
  - dav1d=1.2.1=hd590300_0
  - debugpy=1.8.12=py311hfdbb021_0
  - decorator=5.1.1=pyhd8ed1ab_1
  - defusedxml=0.7.1=pyhd8ed1ab_0
  - dill=0.3.9=pyhd8ed1ab_1
  - distributed=2024.12.1=pyhd8ed1ab_0
  - distributed-ucxx=0.42.00a=py3.11_250205_g6a8ede0_31
  - dlpack=0.8=h59595ed_3
  - dnspython=2.7.0=pyhff2d567_1
  - docker-py=7.1.0=pyhd8ed1ab_1
  - email-validator=2.2.0=pyhd8ed1ab_1
  - email_validator=2.2.0=hd8ed1ab_1
  - exceptiongroup=1.2.2=pyhd8ed1ab_1
  - executing=2.1.0=pyhd8ed1ab_1
  - fastapi=0.115.8=pyh29332c3_0
  - fastapi-cli=0.0.7=pyhd8ed1ab_0
  - fastrlock=0.8.3=py311h19fd3bc_1
  - fmt=11.0.2=h434a139_0
  - folium=0.19.4=pyhd8ed1ab_0
  - fonttools=4.55.8=py311h2dc5d0c_0
  - fqdn=1.5.1=pyhd8ed1ab_1
  - freetype=2.12.1=h267a509_2
  - freexl=2.0.0=h9dce30a_2
  - frozenlist=1.5.0=py311h2dc5d0c_1
  - fsspec=2025.2.0=pyhd8ed1ab_0
  - geopandas=1.0.1=pyhd8ed1ab_3
  - geopandas-base=1.0.1=pyha770c72_3
  - geos=3.13.0=h5888daf_0
  - geotiff=1.7.3=h77b800c_3
  - gflags=2.2.2=h5888daf_1005
  - giflib=5.2.2=hd590300_0
  - glog=0.7.1=hbabe93e_0
  - google-pasta=0.2.0=pyhd8ed1ab_2
  - h11=0.14.0=pyhd8ed1ab_1
  - h2=4.2.0=pyhd8ed1ab_0
  - holoviews=1.20.0=pyhd8ed1ab_1
  - hpack=4.1.0=pyhd8ed1ab_0
  - httpcore=1.0.7=pyh29332c3_1
  - httptools=0.6.4=py311h9ecbd09_0
  - httpx=0.28.1=pyhd8ed1ab_0
  - hyperframe=6.1.0=pyhd8ed1ab_0
  - icu=75.1=he02047a_0
  - idna=3.10=pyhd8ed1ab_1
  - imagecodecs=2024.9.22=py311h7d28041_0
  - imageio=2.37.0=pyhfb79c49_0
  - importlib-metadata=6.10.0=pyha770c72_0
  - importlib_resources=6.5.2=pyhd8ed1ab_0
  - ipykernel=6.29.5=pyh3099207_0
  - ipython=8.32.0=pyh907856f_0
  - isoduration=20.11.0=pyhd8ed1ab_1
  - jedi=0.19.2=pyhd8ed1ab_1
  - jinja2=3.1.5=pyhd8ed1ab_0
  - jmespath=1.0.1=pyhd8ed1ab_1
  - joblib=1.4.2=pyhd8ed1ab_1
  - json-c=0.18=h6688a6e_0
  - jsonpointer=3.0.0=py311h38be061_1
  - jsonschema=4.23.0=pyhd8ed1ab_1
  - jsonschema-specifications=2024.10.1=pyhd8ed1ab_1
  - jsonschema-with-format-nongpl=4.23.0=hd8ed1ab_1
  - jupyter-server-proxy=4.4.0=pyhd8ed1ab_1
  - jupyter_client=8.6.3=pyhd8ed1ab_1
  - jupyter_core=5.7.2=pyh31011fe_1
  - jupyter_events=0.12.0=pyh29332c3_0
  - jupyter_server=2.15.0=pyhd8ed1ab_0
  - jupyter_server_terminals=0.5.3=pyhd8ed1ab_1
  - jupyterlab_pygments=0.3.0=pyhd8ed1ab_2
  - jxrlib=1.1=hd590300_3
  - keyutils=1.6.1=h166bdaf_0
  - kiwisolver=1.4.7=py311hd18a35c_0
  - krb5=1.21.3=h659f571_0
  - lazy-loader=0.4=pyhd8ed1ab_2
  - lazy_loader=0.4=pyhd8ed1ab_2
  - lcms2=2.16=hb7c19ff_0
  - ld_impl_linux-64=2.43=h712a8e2_2
  - lerc=4.0.0=h27087fc_0
  - libabseil=20240722.0=cxx17_hbbce691_4
  - libaec=1.1.3=h59595ed_0
  - libarchive=3.7.7=hadbb8c3_0
  - libarrow=18.0.0=h4804cb8_9_cuda
  - libarrow-acero=18.0.0=h530483c_9_cuda
  - libarrow-dataset=18.0.0=h530483c_9_cuda
  - libarrow-substrait=18.0.0=hb57c354_9_cuda
  - libavif16=1.1.1=h1909e37_2
  - libblas=3.9.0=28_h59b9bed_openblas
  - libbrotlicommon=1.1.0=hb9d3cd8_2
  - libbrotlidec=1.1.0=hb9d3cd8_2
  - libbrotlienc=1.1.0=hb9d3cd8_2
  - libcap=2.71=h39aace5_0
  - libcblas=3.9.0=28_he106b2a_openblas
  - libcrc32c=1.1.2=h9c3ff4c_0
  - libcublas=12.8.3.14=h9ab20c4_0
  - libcublas-dev=12.8.3.14=h9ab20c4_0
  - libcucim=25.02.00a16=cuda12_250205_g0adb828_16
  - libcudf=25.02.00a367=cuda12_250205_g0136e2411b_367
  - libcudf_kafka=25.02.00a367=cuda12_250205_g0136e2411b_367
  - libcufft=11.3.3.41=hbd13f7d_0
  - libcufile=1.13.0.11=h12f29b5_0
  - libcufile-dev=1.13.0.11=h5888daf_0
  - libcugraph=25.02.00a96=cuda12_250205_g20ddb5381_96
  - libcugraph_etl=25.02.00a96=cuda12_250205_g20ddb5381_96
  - libcuml=25.02.00a70=cuda12_250205_gdef265e8e_70
  - libcumlprims=25.02.00a=cuda12_250205_g14ac078_8
  - libcurand=10.3.9.55=hbd13f7d_0
  - libcurand-dev=10.3.9.55=h5888daf_0
  - libcurl=8.11.1=h332b0f4_0
  - libcusolver=11.7.2.55=h9ab20c4_0
  - libcusolver-dev=11.7.2.55=h9ab20c4_0
  - libcusparse=12.5.7.53=hbd13f7d_0
  - libcusparse-dev=12.5.7.53=h5888daf_0
  - libcuspatial=25.02.00a28=cuda12_250205_g57a34d7a_28
  - libcuvs=25.02.00a116=cuda12_250205_gd7c258e_116
  - libde265=1.0.15=h00ab1b0_0
  - libdeflate=1.22=hb9d3cd8_0
  - libedit=3.1.20250104=pl5321h7949ede_0
  - libev=4.33=hd590300_2
  - libevent=2.1.12=hf998b51_1
  - libexpat=2.6.4=h5888daf_0
  - libffi=3.4.2=h7f98852_5
  - libgcc=14.2.0=h77fa898_1
  - libgcc-ng=14.2.0=h69a702a_1
  - libgcrypt-lib=1.11.0=hb9d3cd8_2
  - libgdal-core=3.10.0=h7250d82_6
  - libgfortran=14.2.0=h69a702a_1
  - libgfortran5=14.2.0=hd5240d6_1
  - libgomp=14.2.0=h77fa898_1
  - libgoogle-cloud=2.31.0=h804f50b_0
  - libgoogle-cloud-storage=2.31.0=h0121fbd_0
  - libgpg-error=1.51=hbd13f7d_1
  - libgrpc=1.67.1=hc2c308b_0
  - libheif=1.18.2=gpl_hffcb242_100
  - libhwy=1.1.0=h00ab1b0_0
  - libiconv=1.17=hd590300_2
  - libjpeg-turbo=3.0.0=hd590300_1
  - libjxl=0.11.1=hdb8da77_0
  - libkml=1.3.0=hf539b9f_1021
  - libkvikio=25.02.00a=cuda12_250205_g6b316e6_33
  - liblapack=3.9.0=28_h7ac8fdf_openblas
  - libllvm14=14.0.6=hcd5def8_4
  - liblzma=5.6.4=hb9d3cd8_0
  - liblzma-devel=5.6.4=hb9d3cd8_0
  - libnghttp2=1.64.0=h161d5f1_0
  - libnl=3.11.0=hb9d3cd8_0
  - libnsl=2.0.1=hd590300_0
  - libntlm=1.8=hb9d3cd8_0
  - libnvjitlink=12.8.61=hbd13f7d_0
  - libnvjpeg=12.3.5.57=h97fd463_0
  - libopenblas=0.3.28=pthreads_h94d23a6_1
  - libparquet=18.0.0=hdbc8f64_9_cuda
  - libpng=1.6.46=h943b412_0
  - libprotobuf=5.28.2=h5b01275_0
  - libraft=25.02.00a52=cuda12_250205_ge15a112d_52
  - libraft-headers=25.02.00a52=cuda12_250205_ge15a112d_52
  - libraft-headers-only=25.02.00a52=cuda12_250205_ge15a112d_52
  - librdkafka=2.5.3=h95ba008_0
  - libre2-11=2024.07.02=hbbce691_2
  - librmm=25.02.00a44=cuda12_250205_g25501569_44
  - librttopo=1.1.0=h97f6797_17
  - libsodium=1.0.20=h4ab18f5_0
  - libspatialite=5.1.0=h1b4f908_12
  - libsqlite=3.48.0=hee588c1_1
  - libssh2=1.11.1=hf672d98_0
  - libstdcxx=14.2.0=hc0a3c3a_1
  - libstdcxx-ng=14.2.0=h4852527_1
  - libsystemd0=256.9=h2774228_0
  - libthrift=0.21.0=h0e7cc3e_0
  - libtiff=4.7.0=hc4654cb_2
  - libucxx=0.42.00a=cuda12_250205_g6a8ede0_31
  - libudev1=257.2=h9a4d06a_0
  - libutf8proc=2.8.0=hf23e847_1
  - libuuid=2.38.1=h0b41bf4_0
  - libuv=1.50.0=hb9d3cd8_0
  - libwebp-base=1.5.0=h851e524_0
  - libxcb=1.17.0=h8a09558_0
  - libxcrypt=4.4.36=hd590300_1
  - libxgboost=2.1.3=rapidsai_h1d61c81_3
  - libxml2=2.13.5=h8d12d68_1
  - libzlib=1.3.1=hb9d3cd8_2
  - libzopfli=1.0.3=h9c3ff4c_0
  - linkify-it-py=2.0.3=pyhd8ed1ab_1
  - llvmlite=0.43.0=py311h9c9ff8c_1
  - locket=1.0.0=pyhd8ed1ab_0
  - lz4=4.3.3=py311h2cbdf9a_1
  - lz4-c=1.9.4=hcb278e6_0
  - lzo=2.10=hd590300_1001
  - mapclassify=2.8.1=pyhd8ed1ab_1
  - markdown=3.6=pyhd8ed1ab_0
  - markdown-it-py=3.0.0=pyhd8ed1ab_1
  - markupsafe=3.0.2=py311h2dc5d0c_1
  - matplotlib-base=3.10.0=py311h2b939e6_0
  - matplotlib-inline=0.1.7=pyhd8ed1ab_1
  - mdit-py-plugins=0.4.2=pyhd8ed1ab_1
  - mdurl=0.1.2=pyhd8ed1ab_1
  - minizip=4.0.7=h05a5f5f_3
  - mistune=3.1.1=pyhd8ed1ab_0
  - mock=4.0.3=pyhd8ed1ab_4
  - msgpack-python=1.1.0=py311hd18a35c_0
  - multidict=6.1.0=py311h2dc5d0c_2
  - multipledispatch=0.6.0=pyhd8ed1ab_1
  - multiprocess=0.70.17=py311h9ecbd09_1
  - munkres=1.1.4=pyh9f0ad1d_0
  - nbclient=0.10.2=pyhd8ed1ab_0
  - nbconvert-core=7.16.6=pyh29332c3_0
  - nbformat=5.10.4=pyhd8ed1ab_1
  - nccl=2.25.1.1=ha44e49d_0
  - ncurses=6.5=h2d0b736_3
  - nest-asyncio=1.6.0=pyhd8ed1ab_1
  - networkx=3.4.2=pyh267e887_2
  - nodejs=22.12.0=hf235a45_0
  - numba=0.60.0=py311h4bc866e_0
  - numba-cuda=0.2.0=pyh267e887_0
  - numpy=1.26.4=py311h64a7726_0
  - nvcomp=4.1.0.6=h66a0f98_0
  - nvidia-ml-py=12.570.86=pyhd8ed1ab_0
  - nvtx=0.2.10=py311h9ecbd09_2
  - nx-cugraph=25.02.00a45=py311_250205_g5d865e9_45
  - omegaconf=2.3.0=pyhd8ed1ab_0
  - openjpeg=2.5.3=h5fbd93e_0
  - openssl=3.4.0=h7b32b05_1
  - orc=2.0.3=he039a57_0
  - overrides=7.7.0=pyhd8ed1ab_1
  - packaging=24.2=pyhd8ed1ab_2
  - pandas=2.2.3=py311h7db5c69_1
  - pandocfilters=1.5.0=pyhd8ed1ab_0
  - panel=1.6.0=pyhd8ed1ab_0
  - param=2.2.0=pyhd8ed1ab_0
  - paramiko=3.5.1=pyhd8ed1ab_0
  - parso=0.8.4=pyhd8ed1ab_1
  - partd=1.4.2=pyhd8ed1ab_0
  - pathos=0.3.3=pyhd8ed1ab_1
  - pcre2=10.44=hba22ea6_2
  - pexpect=4.9.0=pyhd8ed1ab_1
  - pickleshare=0.7.5=pyhd8ed1ab_1004
  - pillow=11.1.0=py311h1322bbf_0
  - pip=25.0=pyh8b19718_0
  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_2
  - platformdirs=4.3.6=pyhd8ed1ab_1
  - polars=1.21.0=py311h03f6b34_0
  - pox=0.3.5=pyhd8ed1ab_1
  - ppft=1.7.6.9=pyhd8ed1ab_1
  - proj=9.5.1=h0054346_0
  - prometheus_client=0.21.1=pyhd8ed1ab_0
  - prompt-toolkit=3.0.50=pyha770c72_0
  - propcache=0.2.1=py311h2dc5d0c_1
  - protobuf=5.28.2=py311hfdbb021_0
  - psutil=6.1.1=py311h9ecbd09_0
  - pthread-stubs=0.4=hb9d3cd8_1002
  - ptyprocess=0.7.0=pyhd8ed1ab_1
  - pure_eval=0.2.3=pyhd8ed1ab_1
  - py-xgboost=2.1.3=rapidsai_pyh35aab83_3
  - pyarrow=18.0.0=py311h38be061_2
  - pyarrow-core=18.0.0=py311hcae7c52_2_cuda
  - pycparser=2.22=pyh29332c3_1
  - pyct=0.5.0=pyhd8ed1ab_1
  - pydantic=2.10.6=pyh3cfb1c2_0
  - pydantic-core=2.27.2=py311h9e33e62_0
  - pygments=2.19.1=pyhd8ed1ab_0
  - pylibcudf=25.02.00a367=cuda12_py311_250205_g0136e2411b_367
  - pylibcugraph=25.02.00a96=cuda12_py311_250205_g20ddb5381_96
  - pylibraft=25.02.00a52=cuda12_py311_250205_ge15a112d_52
  - pynacl=1.5.0=py311h9ecbd09_4
  - pynvjitlink=0.5.0=py311hf640dd1_0
  - pynvml=12.0.0=pyhd8ed1ab_0
  - pyogrio=0.10.0=py311hf6089d3_1
  - pyparsing=3.2.1=pyhd8ed1ab_0
  - pyproj=3.7.0=py311h0f98d5a_0
  - pysocks=1.7.1=pyha55dd90_7
  - python=3.11.11=h9e4cc4f_1_cpython
  - python-confluent-kafka=2.5.3=py311h9ecbd09_0
  - python-dateutil=2.9.0.post0=pyhff2d567_1
  - python-dotenv=1.0.1=pyhd8ed1ab_1
  - python-fastjsonschema=2.21.1=pyhd8ed1ab_0
  - python-json-logger=2.0.7=pyhd8ed1ab_0
  - python-multipart=0.0.20=pyhff2d567_0
  - python-tzdata=2025.1=pyhd8ed1ab_0
  - python_abi=3.11=5_cp311
  - pytz=2024.1=pyhd8ed1ab_0
  - pyviz_comms=3.0.4=pyhd8ed1ab_1
  - pywavelets=1.8.0=py311h9f3472d_0
  - pywin32-on-windows=0.1.0=pyh1179c8e_3
  - pyyaml=6.0.2=py311h2dc5d0c_2
  - pyzmq=26.2.1=py311h7deb3e3_0
  - qhull=2020.2=h434a139_5
  - raft-dask=25.02.00a52=cuda12_py311_250205_ge15a112d_52
  - rapids=25.02.00a=cuda12_py311_250128_g057e371_9
  - rapids-dask-dependency=25.02.00a10=py_0
  - rapids-xgboost=25.02.00a=cuda12_py311_250128_g057e371_9
  - rav1e=0.6.6=he8a937b_2
  - rdma-core=55.0=h5888daf_0
  - re2=2024.07.02=h9925aae_2
  - readline=8.2=h8228510_1
  - referencing=0.36.2=pyh29332c3_0
  - requests=2.32.3=pyhd8ed1ab_1
  - rfc3339-validator=0.1.4=pyhd8ed1ab_1
  - rfc3986-validator=0.1.1=pyh9f0ad1d_0
  - rich=13.9.4=pyhd8ed1ab_1
  - rich-toolkit=0.11.3=pyh29332c3_0
  - rmm=25.02.00a44=cuda12_py311_250205_g25501569_44
  - rpds-py=0.22.3=py311h9e33e62_0
  - s2n=1.5.9=h0fd0ee4_0
  - s3transfer=0.11.2=pyhd8ed1ab_0
  - sagemaker-core=1.0.21=pyhd8ed1ab_0
  - sagemaker-python-sdk=2.239.0=pyhff2d567_0
  - schema=0.7.7=pyhd8ed1ab_0
  - scikit-image=0.24.0=py311h7db5c69_3
  - scikit-learn=1.6.1=py311h57cc02b_0
  - scipy=1.15.1=py311hc1ac118_0
  - send2trash=1.8.3=pyh0d859eb_1
  - setuptools=75.8.0=pyhff2d567_0
  - shapely=2.0.7=py311h2fdb869_0
  - shellingham=1.5.4=pyhd8ed1ab_1
  - simpervisor=1.0.0=pyhd8ed1ab_1
  - six=1.17.0=pyhd8ed1ab_0
  - smdebug-rulesconfig=1.0.1=pyhd3deb0d_1
  - snappy=1.2.1=h8bd8927_1
  - sniffio=1.3.1=pyhd8ed1ab_1
  - sortedcontainers=2.4.0=pyhd8ed1ab_1
  - soupsieve=2.5=pyhd8ed1ab_1
  - spdlog=1.14.1=hed91bc2_1
  - sqlite=3.48.0=h9eae976_1
  - stack_data=0.6.3=pyhd8ed1ab_1
  - starlette=0.45.3=pyha770c72_0
  - streamz=0.6.4=pyhd8ed1ab_1
  - svt-av1=2.3.0=h5888daf_0
  - tblib=3.0.0=pyhd8ed1ab_1
  - terminado=0.18.1=pyh0d859eb_0
  - threadpoolctl=3.5.0=pyhc1e730c_0
  - tifffile=2024.12.12=pyhd8ed1ab_0
  - tinycss2=1.4.0=pyhd8ed1ab_0
  - tk=8.6.13=noxft_h4845f30_101
  - toolz=1.0.0=pyhd8ed1ab_1
  - tornado=6.4.2=py311h9ecbd09_0
  - tqdm=4.67.1=pyhd8ed1ab_1
  - traitlets=5.14.3=pyhd8ed1ab_1
  - treelite=4.4.1=py311hd96400b_1
  - typer=0.15.1=pyhd8ed1ab_0
  - typer-slim=0.15.1=pyhd8ed1ab_0
  - typer-slim-standard=0.15.1=hd8ed1ab_0
  - types-python-dateutil=2.9.0.20241206=pyhd8ed1ab_0
  - typing-extensions=4.12.2=hd8ed1ab_1
  - typing_extensions=4.12.2=pyha770c72_1
  - typing_utils=0.1.0=pyhd8ed1ab_1
  - tzdata=2025a=h78e105d_0
  - uc-micro-py=1.0.3=pyhd8ed1ab_1
  - ucx=1.18.0=h8887438_1
  - ucx-py=0.42.00a16=py311_250205_g0aeee98_16
  - ucxx=0.42.00a=cuda12_py3.11_250205_g6a8ede0_31
  - unicodedata2=16.0.0=py311h9ecbd09_0
  - uri-template=1.3.0=pyhd8ed1ab_1
  - uriparser=0.9.8=hac33072_0
  - urllib3=2.3.0=pyhd8ed1ab_0
  - uvicorn=0.34.0=pyh31011fe_0
  - uvicorn-standard=0.34.0=h31011fe_0
  - uvloop=0.21.0=py311h9ecbd09_1
  - watchfiles=1.0.4=py311h9e33e62_0
  - wcwidth=0.2.13=pyhd8ed1ab_1
  - webcolors=24.11.1=pyhd8ed1ab_0
  - webencodings=0.5.1=pyhd8ed1ab_3
  - websocket-client=1.8.0=pyhd8ed1ab_1
  - websockets=14.2=py311h9ecbd09_0
  - wheel=0.45.1=pyhd8ed1ab_1
  - x265=3.5=h924138e_3
  - xarray=2025.1.2=pyhd8ed1ab_0
  - xerces-c=3.2.5=h988505b_2
  - xgboost=2.1.3=rapidsai_pyh0e8b7e3_3
  - xorg-libxau=1.0.12=hb9d3cd8_0
  - xorg-libxdmcp=1.1.5=hb9d3cd8_0
  - xyzservices=2025.1.0=pyhd8ed1ab_0
  - xz=5.6.4=hbcc6ac9_0
  - xz-gpl-tools=5.6.4=hbcc6ac9_0
  - xz-tools=5.6.4=hb9d3cd8_0
  - yaml=0.2.5=h7f98852_2
  - yarl=1.18.3=py311h2dc5d0c_1
  - zeromq=4.3.5=h3b0a872_7
  - zfp=1.0.1=h5888daf_2
  - zict=3.0.0=pyhd8ed1ab_1
  - zipp=3.21.0=pyhd8ed1ab_1
  - zlib=1.3.1=hb9d3cd8_2
  - zlib-ng=2.2.3=h7955e40_0
  - zstandard=0.23.0=py311hbc35293_1
  - zstd=1.5.6=ha6fb4c9_0
prefix: /raid/jlamb/miniforge/envs/rapids
output of 'conda info' (click me)
     active environment : None
       user config file : /home/nfs/jlamb/.condarc
 populated config files : /raid/jlamb/miniforge/.condarc
                          /home/nfs/jlamb/.condarc
          conda version : 24.11.3
    conda-build version : not installed
         python version : 3.12.8.final.0
                 solver : libmamba (default)
       virtual packages : __archspec=1=broadwell
                          __conda=24.11.3=0
                          __cuda=12.2=0
                          __glibc=2.31=0
                          __linux=5.4.0=0
                          __unix=0=0
       base environment : /raid/jlamb/miniforge  (writable)
      conda av data dir : /raid/jlamb/miniforge/etc/conda
  conda av metadata url : None
           channel URLs : https://conda.anaconda.org/conda-forge/linux-64
                          https://conda.anaconda.org/conda-forge/noarch
                          https://conda.anaconda.org/rapidsai-nightly/linux-64
                          https://conda.anaconda.org/rapidsai-nightly/noarch
                          https://conda.anaconda.org/rapidsai/linux-64
                          https://conda.anaconda.org/rapidsai/noarch
          package cache : /raid/jlamb/miniforge/pkgs
                          /home/nfs/jlamb/.conda/pkgs
       envs directories : /raid/jlamb/miniforge/envs
                          /home/nfs/jlamb/.conda/envs
               platform : linux-64
             user-agent : conda/24.11.3 requests/2.32.3 CPython/3.12.8 Linux/5.4.0-182-generic ubuntu/20.04.6 glibc/2.31 solver/libmamba conda-libmamba-solver/24.9.0 libmambapy/1.5.12
                UID:GID : 10349:10004
             netrc file : None
           offline mode : False

Also works with:

Python 3.12 (click me)
mamba create -y -n rapids -c rapidsai-nightly -c conda-forge -c nvidia rapids=25.02 python=3.12 cuda-version=12.8 \
    boto3 \
    ipykernel \
    'sagemaker-python-sdk>=2.239.0'
Python 3.12 + RAPIDS 24.12 (click me)
mamba create -y -n rapids -c conda-forge -c nvidia rapids=24.12 python=3.12 cuda-version=12.8 \
    boto3 \
    ipykernel \
    'sagemaker-python-sdk>=2.239.0'
Python 3.12 + RAPIDS 24.10 (click me)
mamba create -y -n rapids -c conda-forge -c nvidia rapids=24.10 python=3.12 cuda-version=12.8 \
    boto3 \
    ipykernel \
    'sagemaker-python-sdk>=2.239.0'

Thanks so much @claytonparnell !!!

@jameslamb
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The examples I shared in #443 (comment) used therapidsai/ci-conda container image.

Trying to run them today directly in a lifecycle configuration for a SageMaker Notebook Instance (Amazon Linux 2, Jupyter Lab 4), I found that rapids=25.02 is not installable there because Amazon Linux 2 only has GLIBC 2.26.

The following package could not be installed
└─ rapids 25.02**  is not installable because it requires
   └─ cuvs 25.02.* , which requires
      └─ __glibc >=2.28,<3.0.a0 , which is missing on the system.

That has a totally different root cause and should be its own issue. I've opened #520

But RAPIDS 24.10 and RAPIDS 24.12 are working well on SageMaker Notebook Instances with the latest sagemaker-python-sdk packages!

So this issue can be closed as complete.

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