Pull request #2: Add conda environment and installation

Merge in EAI/platform-demo-scripts from mambaforge-installation-method to switching_to_pytorch_lightning_and_pick_benchmark

* commit '4b169cab5b9f8b277a44d905b52fdfb1f3d76550':
  Add installation method for OSX
  Add conda environment and installation

README.md

@@ -15,17 +15,55 @@ This repo contains notebooks and scripts demonstrating how to:
 This code is based on the [pick-benchmark](https://github.com/seisbench/pick-benchmark), the repository accompanying the paper:
 [Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers](https://doi.org/10.1029/2021JB023499)
 
-### Before running
+### Installation method 1
+
+Please download and install [Mambaforge](https://github.com/conda-forge/miniforge#mambaforge) following the [official guide](https://github.com/conda-forge/miniforge#install).
+
+After successful installation and within the Mambaforge environment please clone this repository: 
+
+```
+git clone ssh://git@git.plgrid.pl:7999/eai/platform-demo-scripts.git
+```
+and please run for Linux or Windows platforms:
+
+```
+cd platform-demo-scripts
+mambaforge env create -f epos-ai-train.yml
+```
+or for OSX:
+```
+cd platform-demo-scripts
+mambaforge env create -f epos-ai-train-osx.yml
+```
+
+This will create a conda environment named `platform-demo-scripts` with all required packages installed.
+
+To run the notebooks and scripts from this repository it is necessary to activate the `platform-demo-scripts` environment by running:
+
+```
+conda activate platform-demo-scripts
+```
+
+### Installation method 2
 
 Please [install Poetry](https://python-poetry.org/docs/#installation), a tool for dependency management and packaging in Python.
 Then we will use only Poetry for creating Python environment and installing dependencies.
 
+Install all dependencies with poetry, run: 
+
+```
+poetry install
+```
+
+To run the notebooks and scripts from this repository it is necessary to activate the poetry environment by running:
+
+```
+poetry shell
+```
+
 ### Usage
 
-1. Install all dependencies with poetry, run: 
-
-   `poetry install`
-2. Prepare .env file with content:
+1. Prepare .env file with content:
    ```
    WANDB_HOST="https://epos-ai.grid.cyfronet.pl/"
    WANDB_API_KEY="your key"
@@ -33,15 +71,11 @@ Then we will use only Poetry for creating Python environment and installing depe
    WANDB_PROJECT="training_seisbench_models_on_igf_data"
    BENCHMARK_DEFAULT_WORKER=2
 
-3. Transform data into seisbench format. (unofficial)
+2. Transform data into seisbench format. (unofficial)
    * Download original data from the [drive](https://drive.google.com/drive/folders/1InVI9DLaD7gdzraM2jMzeIrtiBSu-UIK?usp=drive_link)
    * Run the notebook: `utils/Transforming mseeds to SeisBench dataset.ipynb`
 
-4. Initialize poetry environment:
-
-   `poetry shell`
-
-5. Run the pipeline script:
+3. Run the pipeline script:
 
    `python pipeline.py`
 

epos-ai-train-osx.yml

@@ -0,0 +1,21 @@
+name: epos-ai-train
+channels:
+  - pytorch
+  - nvidia
+dependencies:
+  - python=3.10
+  - pytorch=2.0.1
+  - PyYAML=6.0
+  - python-dotenv=1.0.0
+  - pandas=2.0.3
+  - obspy=1.4.0
+  - wandb=0.15.4
+  - torchmetrics=0.11.4
+  - pytorch-lightning
+  - scikit-learn
+  - openpyxl
+  - jupyterlab
+  - notebook
+  - pip
+  - pip:
+    - seisbench==0.4.1

epos-ai-train.yml

@@ -0,0 +1,22 @@
+name: epos-ai-train
+channels:
+  - pytorch
+  - nvidia
+dependencies:
+  - python=3.10
+  - pytorch=2.0.1
+  - pytorch-cuda
+  - PyYAML=6.0
+  - python-dotenv=1.0.0
+  - pandas=2.0.3
+  - obspy=1.4.0
+  - wandb=0.15.4
+  - torchmetrics=0.11.4
+  - pytorch-lightning
+  - scikit-learn
+  - openpyxl
+  - jupyterlab
+  - notebook
+  - pip
+  - pip:
+    - seisbench==0.4.1