From 80ccb6d95c3c8c93de519d0e4ea72afe7afcf1b3 Mon Sep 17 00:00:00 2001 From: John McFarland Date: Mon, 19 Dec 2022 10:30:28 -0600 Subject: [PATCH] Update links from workshops -> general --- README.md | 1 - general/Optimized_TF/README.md | 2 +- .../python/openai_rllib/simple-example-gpu/README.md | 8 ++++---- 3 files changed, 5 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 386996de2..dd66db4b7 100644 --- 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 index 70b7f8754..59a2a7d78 100644 --- 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 index 8c2e95836..9f146390e 100644 --- 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=//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 -``` \ No newline at end of file +```