installing requirements in kaggle

[setayeshk]

Hello,
I'm trying to install the requirements on Kaggle, but I'm encountering several issues.

Conda Installation: Installing the requirements using Conda takes an extremely long time, and the progress seems to be stuck with no visible advancement.
Has anyone successfully installed the requirements on Kaggle?
this is my code for the conda environment:

!conda create -n medsam2 python=3.12.4 -y > /dev/null
!source /opt/conda/bin/activate medsam2
!sudo rm /opt/conda/bin/python > /dev/null
!sudo ln -s /opt/conda/envs/medsam2/bin/python3 /opt/conda/bin/python > /dev/null
!sudo rm /opt/conda/bin/python3 > /dev/null
!sudo ln -sf /opt/conda/envs/medsam2/bin/python3 /opt/conda/bin/python3 > /dev/null

I tried to install them through pip directly ( using a requirements.txt i created from the yml file and even directly) but the default python version on kaggle is 3.10 (i tried changing that and even with the 3.12.4 version I encountered errors )
Here are some of the errors I get:

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
cudf 24.8.2 requires cubinlinker, which is not installed.
cudf 24.8.2 requires cupy-cuda11x>=12.0.0, which is not installed.
cudf 24.8.2 requires ptxcompiler, which is not installed.
cuml 24.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.
dask-cudf 24.8.2 requires cupy-cuda11x>=12.0.0, which is not installed.
ucxx 0.39.1 requires libucx>=1.15.0, which is not installed.
accelerate 0.33.0 requires numpy<2.0.0,>=1.17, but you have numpy 2.0.1 which is incompatible.
albucore 0.0.13 requires numpy<2,>=1.24.4, but you have numpy 2.0.1 which is incompatible.
apache-beam 2.46.0 requires cloudpickle~=2.2.1, but you have cloudpickle 3.0.0 which is incompatible.
apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.8 which is incompatible.
apache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 2.0.1 which is incompatible.
apache-beam 2.46.0 requires protobuf<4,>3.12.2, but you have protobuf 4.25.4 which is incompatible.
apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 16.1.0 which is incompatible.
beatrix-jupyterlab 2024.66.154055 requires jupyterlab~=3.6.0, but you have jupyterlab 4.2.4 which is incompatible.
bigframes 0.22.0 requires google-cloud-bigquery[bqstorage,pandas]>=3.10.0, but you have google-cloud-bigquery 2.34.4 which is incompatible.
bigframes 0.22.0 requires google-cloud-storage>=2.0.0, but you have google-cloud-storage 1.44.0 which is incompatible.
bigframes 0.22.0 requires pandas<2.1.4,>=1.5.0, but you have pandas 2.2.2 which is incompatible.
cudf 24.8.2 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.6.0 which is incompatible.
cudf 24.8.2 requires numpy<2.0a0,>=1.23, but you have numpy 2.0.1 which is incompatible.
dask-cuda 24.8.2 requires numpy<2.0a0,>=1.23, but you have numpy 2.0.1 which is incompatible.
dask-cudf 24.8.2 requires numpy<2.0a0,>=1.23, but you have numpy 2.0.1 which is incompatible.
dataproc-jupyter-plugin 0.1.79 requires pydantic~=1.10.0, but you have pydantic 2.8.2 which is incompatible.
distributed 2024.7.1 requires dask==2024.7.1, but you have dask 2024.8.1 which is incompatible.
fitter 1.7.1 requires numpy<2.0.0,>=1.20.0, but you have numpy 2.0.1 which is incompatible.
gensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.0.1 which is incompatible.
gensim 4.3.3 requires scipy<1.14.0,>=1.7.0, but you have scipy 1.14.0 which is incompatible.
google-cloud-aiplatform 0.6.0a1 requires google-api-core[grpc]<2.0.0dev,>=1.22.2, but you have google-api-core 2.11.1 which is incompatible.
google-cloud-automl 1.0.1 requires google-api-core[grpc]<2.0.0dev,>=1.14.0, but you have google-api-core 2.11.1 which is incompatible.
google-cloud-bigquery 2.34.4 requires packaging<22.0dev,>=14.3, but you have packaging 24.1 which is incompatible.
google-cloud-bigquery 2.34.4 requires protobuf<4.0.0dev,>=3.12.0, but you have protobuf 4.25.4 which is incompatible.
google-cloud-bigtable 1.7.3 requires protobuf<4.0.0dev, but you have protobuf 4.25.4 which is incompatible.
google-cloud-vision 2.8.0 requires protobuf<4.0.0dev,>=3.19.0, but you have protobuf 4.25.4 which is incompatible.
ibis-framework 7.1.0 requires numpy<2,>=1, but you have numpy 2.0.1 which is incompatible.
ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 16.1.0 which is incompatible.
kfp 2.5.0 requires google-cloud-storage<3,>=2.2.1, but you have google-cloud-storage 1.44.0 which is incompatible.
kfp 2.5.0 requires protobuf<4,>=3.13.0, but you have protobuf 4.25.4 which is incompatible.
kfp-pipeline-spec 0.2.2 requires protobuf<4,>=3.13.0, but you have protobuf 4.25.4 which is incompatible.
libpysal 4.9.2 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.
momepy 0.7.2 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.
numba 0.58.1 requires numpy<1.27,>=1.22, but you have numpy 2.0.1 which is incompatible.
opentelemetry-exporter-otlp 1.25.0 requires opentelemetry-exporter-otlp-proto-http==1.25.0, but you have opentelemetry-exporter-otlp-proto-http 1.21.0 which is incompatible. .....

I would be really grateful if anyone could help me with this.

[rabiaedayilmaz]

Hi,

You can use this example usage on colab notebook and rearrange for kaggle to test: #38

[setayeshk]

I finally fixed the issue and shared the steps in: #26