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environment.yaml
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environment.yaml
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# reasons you might want to use `environment.yaml` instead of `requirements.txt`:
# - pip installs packages in a loop, without ensuring dependencies across all packages
# are fulfilled simultaneously, but conda achieves proper dependency control across
# all packages
# - conda allows for installing packages without requiring certain compilers or
# libraries to be available in the system, since it installs precompiled binaries
name: myenv
channels:
- pytorch
- conda-forge
- defaults
# it is strongly recommended to specify versions of packages installed through conda
# to avoid situation when version-unspecified packages install their latest major
# versions which can sometimes break things
# current approach below keeps the dependencies in the same major versions across all
# users, but allows for different minor and patch versions of packages where backwards
# compatibility is usually guaranteed
dependencies:
- pytorch=2.*
- torchvision=0.*
- pytorch-lightning=2.*
- torchmetrics=0.*
- hydra-core=1.*
- rich=13.*
- pre-commit=3.*
- pytest=7.*
# --------- loggers --------- #
# - wandb
# - neptune-client
# - mlflow
# - comet-ml
- pip>=23
- pip:
- hydra-optuna-sweeper
- hydra-colorlog
- pyrootutils