@tqchen thanks for your reply. I agree we should not overly constrain dependencies. my thinking is that, purely considering the non-dev dependencies (i.e. those we might surface to `pip`/`conda`), there are two types of dependenceis: - direct dependencies - indirect dependencies (I.e. dependencies of dependencies)
my opinion is that direct dependencies should go in the TVM analogue of the pytorch `requirements.txt` and should be also included in setup.py `install_requires`. indirect dependencies shouldn't be given to pip/conda at all. I do sort of think that specifying at least the major version (I.e. `tensorflow ^2`) for each of these is not unreasonable. given that it's always possible with pip to install a later version of tensorflow with a subsequent `pip install` command, i'm even leaning towards specifying the minor version there too. i'd love to hear opinions from others--my thought is that `pip install tvm`, ran all on its own in a fresh virtualenv, should produce a working installation, and if you need to install a different version of `tensorflow` because you're doing something technically unsupported, pip allows you to do that. also, regardless of being a TVM developer or user, it should be possible to install exactly those versions that the CI used for stable local use. this should probably not be the default flow, so they shouldn't be placed in setup.py's `install_requires`. but, users should be able to download this `ci_constraint.txt` file, and it should be obvious which one to download if you want to e.g. `pip install tvm==0.8.0`. --- [Visit Topic](https://discuss.tvm.apache.org/t/rfc-consolidating-tvm-python-dependencies/8329/4) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/2730b5a3fe204a7a8b5fca03424ae493435df668bc28941d65abb7f984178fc7).
