From 130c1460b452f8289533896ae683823c8a462ec0 Mon Sep 17 00:00:00 2001 From: Anup Kumar Date: Mon, 8 Jun 2020 18:31:57 +0200 Subject: [PATCH] Update readme --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 4157186..8909d7f 100644 --- a/README.md +++ b/README.md @@ -28,19 +28,19 @@ License: MIT License ## (To reproduce this work) How to create a sample tool recommendation model: -**Note**: To reproduce this work after training on complete model, it is required to have a large compute resource (with 20-30 GB RAM) and it takes > 24 hrs on a VMs with 20 cores. However, the following steps can be used to create a sample tool recommendation model on a subset of workflows: +**Note**: To reproduce this work after training on complete model, it is required to have a decent compute resource (with at least 10 GB RAM) and it takes > 24 hrs to create a trained model on complete set of workflows (~ 18,000). However, the following steps can be used to create a sample tool recommendation model on a subset of workflows: 1. Install the dependencies by executing the following lines: * `conda env create -f environment.yml` * `conda activate tool_prediction_gru_wc` -2. Execute `sh train.sh` (https://github.com/anuprulez/galaxy_tool_recommendation/blob/master/train.sh). It runs on a subset of workflows. Use file `data/worflow-connection-04-20.tsv` in the training script to train on complete set of workflows (It takes a long time to finish). +2. Execute `sh train.sh` (https://github.com/anuprulez/galaxy_tool_recommendation/blob/master/train.sh). It runs on a subset of workflows. Use file `data/worflow-connection-20-04.tsv` in the training script to train on complete set of workflows (It takes a long time to finish). 3. After successful finish (~2-3 minutes), a trained model is created at `data/<>.hdf5`. 4. Put this trained model file at `ipython_script/data/<>.hdf5` and execute the ipython notebook. -5. A model trained on all workflows is present at `ipython_script/data/tool_recommendation_model_20_04.hdf5` which can be used to predict tools using the IPython notebook `ipython_script/tool_recommendation_gru_wc.ipynb` +5. A model trained on all workflows is present at `ipython_script/data/tool_recommendation_model_20_05.hdf5` which can be used to predict tools using the IPython notebook `ipython_script/tool_recommendation_gru_wc.ipynb` ## Data description: