From 5251aee8e12fcad64a9cb6cdf3218879c18fb68e Mon Sep 17 00:00:00 2001 From: James Lamb Date: Fri, 23 Jun 2023 11:36:11 -0500 Subject: [PATCH] [docs] more fixes for broken links --- docs/FAQ.rst | 8 ++++---- docs/Parallel-Learning-Guide.rst | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/FAQ.rst b/docs/FAQ.rst index 2a09fd674e4c..4fb6e86cac3a 100644 --- a/docs/FAQ.rst +++ b/docs/FAQ.rst @@ -176,8 +176,8 @@ For C/C++ users, any OpenMP feature cannot be used before the fork happens. If a fork happens (example: using OpenMP for forking), OpenMP will hang inside the forked sessions. Use new processes instead and copy memory as required by creating new processes instead of forking (or, use Intel compilers). -Cloud platform container services may cause LightGBM to hang, if they use Linux fork to run multiple containers on a -single instance. For example, LightGBM hangs in AWS Batch array jobs, which `use the ECS agent +Cloud platform container services may cause LightGBM to hang, if they use Linux fork to run multiple containers on a +single instance. For example, LightGBM hangs in AWS Batch array jobs, which `use the ECS agent `__ to manage multiple running jobs. Setting ``nthreads=1`` mitigates the issue. 12. Why is early stopping not enabled by default in LightGBM? @@ -220,7 +220,7 @@ If you are using any Python package that depends on ``threadpoolctl``, you also .. code-block:: console - /root/miniconda/envs/test-env/lib/python3.8/site-packages/threadpoolctl.py:546: RuntimeWarning: + /root/miniconda/envs/test-env/lib/python3.8/site-packages/threadpoolctl.py:546: RuntimeWarning: Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at the same time. Both libraries are known to be incompatible and this can cause random crashes or deadlocks on Linux when loaded in the @@ -231,7 +231,7 @@ If you are using any Python package that depends on ``threadpoolctl``, you also Detailed description of conflicts between multiple OpenMP instances is provided in the `following document `__. -**Solution**: Assuming you are using LightGBM Python-package and conda as a package manager, we strongly recommend using ``conda-forge`` channel as the only source of all your Python package installations because it contains built-in patches to workaround OpenMP conflicts. Some other workarounds are listed `here `__. +**Solution**: Assuming you are using LightGBM Python-package and conda as a package manager, we strongly recommend using ``conda-forge`` channel as the only source of all your Python package installations because it contains built-in patches to workaround OpenMP conflicts. Some other workarounds are listed `here `__. If this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them. diff --git a/docs/Parallel-Learning-Guide.rst b/docs/Parallel-Learning-Guide.rst index f30f4c0b1e22..247eba6c8193 100644 --- a/docs/Parallel-Learning-Guide.rst +++ b/docs/Parallel-Learning-Guide.rst @@ -520,7 +520,7 @@ See `the mars documentation`_ for usage examples. .. _the Dask prediction example: https://github.com/microsoft/lightgbm/tree/master/examples/python-guide/dask/prediction.py -.. _the Dask worker documentation: https://distributed.dask.org/en/latest/worker.html#memory-management +.. _the Dask worker documentation: https://distributed.dask.org/en/stable/worker-memory.html .. _the metrics functions from dask-ml: https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics