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Issues setting up the environment #2
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Hey @tensarflow, Coincidentally i was also running into the same problem this morning. I found a way around it, however i don't think it is the full solution. I commented these lines out of the environment: Then I've used: $ conda env update --file environment.yaml --prune Then it also gave me the error related to glumpy. Which i installed manually: $ pip install glumpy==1.1.0. Then i ran this line again: $ conda env update --file environment.yaml --prune Now they installed all the files in the environment file except bullet3 and job-runner, which i think need to be installed manually. I don't think this is the best solution, however i think it should work. Kind regards, Stefan |
Thanks for the response @stefanvdpalen, I just found out that the RTX3090 is only compatible with Cuda 11.1 and upwards. So I only defined that version and removed the version specifications of all the other packages, hoping for conda to find the compatible versions (btw, does conda do that?). After that I removed the environment and recreated it with the new This is my current environment.yaml file for conda: name: cosypose
channels:
- conda-forge
- pytorch
dependencies:
- python
- pinocchio
- assimp
- pytorch
- cudatoolkit=11.3
- torchvision
- cudnn
- rclone
- gxx_linux-64
- git-lfs
- ffmpeg
- bzip2
- git
- cmake
- pip
- numpy
- pillow
- ipython
- pip:
- ./deps/bullet3
- ./deps/job-runner
- imageio
- simplejson
- opencv-python
- opencv-contrib-python
- torchnet
- tqdm
- lxml
- transforms3d
- distributed
- joblib
- pandas
- xarray
- pyarrow
- matplotlib
- bokeh
- plyfile
- trimesh
- pycollada
- shapely
- wget
- pypng
- PyOpenGL
- Cython
- ipdb
- colorama
- scikit-video
- scikit-image
- scikit-learn
- pyyaml
- ipykernel
- scipy
- pywavefront
- jupyter-client
- jupyter-core
- jupyter-server-proxy
- jupyterlab
- jupyterlab-server
- dask
- dask-jobqueue
- seaborn |
I ran into the same issue with In the end I had to resort to changing |
I'm also encountering an issue with installing
|
As noted in #10 (comment), initial results seem to indicate that it might be safe to simply replace |
I just submitted a pull request (#11) with my fixes. I would appreciate it if others could test this setup to see if it addresses your issues. Thanks! |
@tensarflow Have your issues been addressed after the PR merge? If so, can we close this issue? 😄 |
Hi there, thanks for the contribution! Awesome work!
Unfortunately I'm having issues setting up the environment for training, inferencing, visualizing etc. For example I get errors regarding Cython not being installed while installing glump, while creating the conda env. Another one is dask, which gave me an error during execution of the synthetic data generation for not being installed after the conda environment was created. Another one is seaborn, that was an issue when executing the data visualization notebook. I tried to find compatible versions and was able to work around some of the errors. Now I am stuck at the jinja2 package. And I'm thinking that I'm doing something wrong because I doubt that this procedure is how you intended the environment.yaml file to work. Could you share the system details of the environment you are using?
I tried many things, including reinstalling conda.
I have a workstation with 2x RTX3090 and an intel i9 processor. Nvidia driver 470 is installed. Conda is up to date.
I would appreciate any input. Thanks in advance.
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