I use Sepal for data collection through the GEE Python API, data storage, and training of machine learning models. I am looking into dask as parallelization back-end for this work, which is well-established within the ML community. Since dask[complete] is part of the python environment provided within Sepal, I was wondering what is the recommended way to proceed.
At the moment, I am running three terminal windows:
First window: dask-scheduler
Second window: dask-worker tcp://127.0.0.1:8786
Third window: python my_python_script.py
A typical limitation I am facing is accessing the dask Dashboard at 127.0.0.1:38285.
I use Sepal for data collection through the GEE Python API, data storage, and training of machine learning models. I am looking into dask as parallelization back-end for this work, which is well-established within the ML community. Since dask[complete] is part of the python environment provided within Sepal, I was wondering what is the recommended way to proceed.
At the moment, I am running three terminal windows:
dask-schedulerdask-worker tcp://127.0.0.1:8786python my_python_script.pyA typical limitation I am facing is accessing the dask Dashboard at 127.0.0.1:38285.