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Yup, especially when you are working with os-level dependencies like cuda, conda is the way to go. I think every tensorflow user can approve
by karxxm 5y ago
Yup, especially when you are working with os-level dependencies like cuda, conda is the way to go. I think every tensorflow user can approve
- codethief 5y ago…unless you're working on a project for arm64-based IoT devices (like Nvidia Jetson), where Conda is not available.
- hantusk 5y agoDoes it not work with miniforge/mambaforge? (https://github.com/conda-forge/miniforge https://github.com/conda-forge/miniforge) I use this with my m1 mac, and it works great.
- wdroz 5y agoconda is now available[1] for arm64 (since March[2]). [1] -- https://docs.conda.io/en/latest/miniconda.html#linux-installers https://docs.conda.io/en/latest/miniconda.html#linux-install... [2] -- https://github.com/conda/conda/issues/8297 https://github.com/conda/conda/issues/8297
- codethief 5y agoAwww man. I must have missed that announcement by only a few days.
- dijksterhuis 5y agoQuite confused about this comment as many of the latest tensorflow V2 releases aren't reliably uploaded to conda forge. IIRC there's only like 4 or so of the V2 releases uploaded. You can conda install the CUDA dependencies and then install the required Tensorflow version via conda pip. But that's not much different to installing CUDA manually and then installing tf from system pip. It's much faster and easier to pull a tf Docker image as it's their "officially supported" way to get up and running. So... as a tf user and a sysadmin... Nah. No conda for me thanks.
- karxxm 5y agoOne project uses tf 2.1, one uses 1.15 and the other 2.4 ... for me it is much more convenient to have 3 envs rather than 3 containers or switching the system cuda as needed... I especially had problems debugging through docker containers back in the days, therefore i never picked it up again.