Closed beckernick closed 2 months ago
If I create the same environment but pin Jupyterlab to v3, the solver pulls in Jupyterlab v3.6.3 and nvdashboard v0.8, with the same build hashes as the packages in the 23.04 runtime container, but different versions and build hashes for other Jupyter-related packages. In this bare metal conda environment, I'm able to use NVDashboard in Jupyterlab.
So just to be clear when you install this manually with JupyterLab pinned to v3 does the dashboard work? Or is it blank link in the RAPIDS container?
If I create the same environment but pin Jupyterlab to v3, the solver pulls in Jupyterlab v3.6.3 and nvdashboard v0.8, with the same build hashes as the packages in the 23.04 runtime container, but different versions and build hashes for other Jupyter-related packages. In this bare metal conda environment, I'm able to use NVDashboard in Jupyterlab.
So just to be clear when you install this manually with JupyterLab pinned to v3 does the dashboard work? Or is it blank link in the RAPIDS container?
Yes, with this setup it works.
mamba create -n rapids-23.04 -c rapidsai -c nvidia -c conda-forge rapids=23.04 python=3.10 cudatoolkit=11.8 jupyterlab=3 jupyterlab-nvdashboard
Perhaps relevant. If I remove the conda installed jupyterlab-nvdashboard and then install with pip, things appear to work:
docker run --gpus all --rm -it -p 8888:8888 -p 8797:8787 -p 8796:8786 nvcr.io/nvidia/rapidsai/rapidsai-core:23.04-cuda11.8-runtime-ubuntu22.04-py3.8
mamba remove jupyterlab-nvdashboard
pip install jupyterlab-nvdashboard
jupyter-lab
Out of curiosity is this still the case when using the conda-forge
package? If not, maybe there is some difference between the two that is worth investigating
Out of curiosity is this still the case when using the
conda-forge
package? If not, maybe there is some difference between the two that is worth investigating
^ @beckernick curious if you had a chance to try the suggestion above?
When I run the RAPIDS 23.04 runtime containers locally, the NVDashboard Jupyter widget shows a blank screen. I'd like to be able to use Jupyterlab-NVDashboard in the 23.04 runtime containers.
When I create a 23.04 environment that includes Jupyterlab (unpinned) and install NVDashboard (unpinned) via conda, the solver pulls in jupyterlab-nvdashboard v0.5.0 from the rapidsai channel rather than the current v0.8. It also pulls in Jupyterlab 4.0, which perhaps is related. In this environment, NVDashboard doesn't show up in the Jupyterlab left panel as an extension option. If I got to the explicit endpoint at http://localhost:8889/GPU-Utilization, I get a 404 error.
If I create the same environment but pin Jupyterlab to v3, the solver pulls in Jupyterlab v3.6.3 and nvdashboard v0.8, with the same build hashes as the packages in the 23.04 runtime container, but different versions and build hashes for other Jupyter-related packages. In this bare metal conda environment, I'm able to use NVDashboard in Jupyterlab.
In the container, bokeh v2.4.3 comes from conda-forge. In the raw environment, it comes from PyPI.
Happy to provide full container/raw environment dependency diffs, if useful.