locking takes long time to solve (and is not using mamba)

[geoHeil]

The following statement:

conda-lock --mamba -f environment.yml -p osx-64
Locking dependencies for ['osx-64']...
INFO:conda_lock.conda_solver:osx-64 using specs [..]

is taking very long to execute. And it looks to me like it is not using mamba as the solver. How can I force it to use mamba (which is installed (--mamba) is specified. But it is still taking very long.

[mariusvniekerk]

Which version of conda-lock, mamba and conda and please post the environment.yml you are trying to solve

[geoHeil]

Eventually it fails with:

RuntimeError: Unable to find installation candidates for pywin32 (304)

The file is:

channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.10
  - pandas
  - scipy
  - geopandas
  - shapely
  - pyshp
  - h3-py
  - h3pandas
  - nodejs
  - openpyxl
  - xlrd
  - odfpy
  - pyarrow
  - s3fs
  - fsspec
  - psycopg2
  - pip
  - pyspark==3.3.0
  - openjdk
  - paramiko
  - pyrosm
  - lxml
  - html5lib
  - pip:
      - dagster>=1.0.4
      - dagster-docker>=0.16.4
      - dagster-ssh>=0.16.4
      - dagster-pandas>=0.16.4
      - dagster-postgres>=0.16.4
      - dagster-dbt>=0.16.4
      - dagster_slack>=0.16.4
      - dagster_aws>=0.16.4
      - dagster-pyspark>=0.16.4
      - dagster-shell>=0.16.4
      - dagster-cloud>=1.0.4
      - dagit>=1.0.4
      - dbt-core
      - dbt-postgres
      - dbt-duckdb
      - polars
      - pytest
      - black
      - isort
      - yamllint
      - h3ronpy
      - whylogs
      - dbt-metabase
      - sqlfluff
      - sqlfluff-templater-dbt
      - openmetadata-ingestion
      - envyaml
      - geoalchemy2>=0.12

mamba versions are:

 conda --version
conda 4.12.0

mamba --version
mamba 0.24.0
conda 4.13.0
 

I just installed conda-lcok today so I guess it is the current version.

[geoHeil]

After upgrading to:

• conda 4.13
• mamba 0.25

the error is:

Message key not found in json! returning the full json text
Could not lock the environment for platform osx-64

[IamShubhamGupto]

Hey is there a solution for this?

[maresb]

Generic advice would be to not mix conda-forge and defaults, and minimize the number of pip dependencies. @IamShubhamGupto please open a new issue with the details of your setup.