Update links from workshops -> general

NREL · Dec 19, 2022 · 80ccb6d · 80ccb6d
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diff --git a/README.md b/README.md
@@ -46,7 +46,6 @@ This repository serves as a collection of walkthroughs, utilities, and other res
 
 ## 🔧 Utilities and Tools
 * [Sample Slurm Batch Scripts](/slurm/README.md)
-* [Workshops and Presentations Hosted by NREL HPC Operations Team](/workshops/README.md)
 
 # Contributing
 Please see our [contribution guidelines](CONTRIBUTING.md) for a rundown on how we'd like the contents of this repository to be formatted.
diff --git a/general/Optimized_TF/README.md b/general/Optimized_TF/README.md
@@ -14,7 +14,7 @@ git clone https://github.com/NREL/HPC
 ```
 3. Navigate to the repo
 ```
-cd ./HPC/workshops/Optimized_TF/
+cd ./HPC/general/Optimized_TF/
 ```
 
 4. To install TensorFlow 2.4.0 with Python 3.8 for GPUS run the following

diff --git a/languages/python/openai_rllib/simple-example-gpu/README.md b/languages/python/openai_rllib/simple-example-gpu/README.md
@@ -11,13 +11,13 @@ conda env create --prefix=/<path_to_chosen_directory>/env_example_gpu -f env_exa
 
 ### **Only for Eagle users:** Creating Anaconda environment using Optimized Tensorflow
 
-NREL's HPC group has recently created [a set of optimized Tensorflow drivers](https://github.com/NREL/HPC/tree/master/workshops/Optimized_TF) that maximize the efficiency of utilizing Eagle's Tesla V100 GPU units. The drivers are created for various Python 3 and Tensorflow 2.x.x versions. 
+NREL's HPC group has recently created [a set of optimized Tensorflow drivers](https://github.com/NREL/HPC/tree/master/general/Optimized_TF) that maximize the efficiency of utilizing Eagle's Tesla V100 GPU units. The drivers are created for various Python 3 and Tensorflow 2.x.x versions. 
 
-The repo provides an [Anaconda environment version](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example-gpu/env_example_optimized_tf.yml) for using these drivers. This environment is based on one of the [example environments](https://github.com/NREL/HPC/blob/master/workshops/Optimized_TF/py37tf22.yml) provided in the [Optimized TF repo](https://github.com/NREL/HPC/tree/master/workshops/Optimized_TF).
+The repo provides an [Anaconda environment version](https://github.com/erskordi/HPC/blob/HPC-RL/languages/python/openai_rllib/simple-example-gpu/env_example_optimized_tf.yml) for using these drivers. This environment is based on one of the [example environments](https://github.com/NREL/HPC/blob/master/general/Optimized_TF/py37tf22.yml) provided in the [Optimized TF repo](https://github.com/NREL/HPC/tree/master/general/Optimized_TF).
 
 **The provided Anaconda environment currently works for Python 3.7, Tensorflow 2.2, and the latest Ray version**
 
-*Make sure to follow the [instructions for installing this particular environment](https://github.com/NREL/HPC/tree/master/workshops/Optimized_TF) explicitly!*
+*Make sure to follow the [instructions for installing this particular environment](https://github.com/NREL/HPC/tree/master/general/Optimized_TF) explicitly!*
 
 ## Allocate GPU node 
 
@@ -183,4 +183,4 @@ Result for PPO_CartPole-v0_0339b_00000:
   timesteps_total: 42800
   training_iteration: 2
   trial_id: 0339b_00000
-```
+```