From f96a9512736575d8c3d7c7c963700c4158629746 Mon Sep 17 00:00:00 2001
From: Jannis Klinkenberg <j.klinkenberg@itc.rwth-aachen.de>
Date: Fri, 6 Dec 2024 10:55:27 +0100
Subject: [PATCH] updated README.md

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 tensorflow/cifar10_distributed/README.md | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/tensorflow/cifar10_distributed/README.md b/tensorflow/cifar10_distributed/README.md
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 # TensorFlow - Distributed Training
 
 This folder contains the following 3 example versions for distributed training:
-- **Version 1: (`submit_job_container_single-node.sh`)** A TensorFlow native version that is constraint to a single compute node with multiple GPUs. A single process is serving multiple GPUs with a `tf.distribute.MirroredStrategy`.
-- **Version 2: (`submit_job_container.sh`)** A TensorFlow native version that utilizes multiple processes (1 process per GPU) that work together using a `tf.distribute.MultiWorkerMirroredStrategy`. Although this is not constraint to a single node, it requires a bit more preparation to setup the distributed environment (via `TF_CONFIG` environment variable)
-- **Version 3: (`submit_job_container_horovod.sh`)** A version that is using Horovod ontop of TensorFlow to perform the distributed training and communication of e.g. model weights/updates. Typically, these calls also use 1 process per GPU.
+
+## Version 1: (`submit_job_container_single-node.sh`)
+A TensorFlow native version that is constraint to a single compute node with multiple GPUs. A single process is serving multiple GPUs with a `tf.distribute.MirroredStrategy`.
+
+## Version 2: (`submit_job_container.sh`)
+A TensorFlow native version that utilizes multiple processes (1 process per GPU) that work together using a `tf.distribute.MultiWorkerMirroredStrategy`. Although this is not constraint to a single node, it requires a bit more preparation to setup the distributed environment (via `TF_CONFIG` environment variable)
+
+## Version 3: (`submit_job_container_horovod.sh`)
+A version that is using Horovod ontop of TensorFlow to perform the distributed training and communication of e.g. model weights/updates. Typically, these calls also use 1 process per GPU.
 
 More information and examples concerning Horovod can be found under:
 - https://horovod.readthedocs.io/en/stable/tensorflow.html
-- 
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