diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 000000000..7e735eb33
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,31 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[BUG] Title describing the bug"
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior. E.g. which data did you use, what were the commands that you executed, did you modify the code, etc.
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Description of the dataset**
+Provide details on the dataset that you use. Is it e.g. an out-of-the-box CAMELS dataset, or did you create your own csv/netCDF files. In that case, providing a data sample might be beneficial.
+
+**Logs & Screenshots**
+Please provide the full stack trace if any exception occured. If applicable, add screenshots to help explain your problem.
+
+**Desktop & Environment (please complete the following information):**
+ - OS: [e.g. Linux, Windows, iOS]
+ - The git commit if you cloned the repo, or the version number in `neuralhydrology/__about__.py`)
+ - The Python version and a list of installed Python packages. If you use conda, you can create this list via `conda env export`.
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 000000000..9f26d9c1d
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: "[REQUEST] Title describing the feature request"
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/docs/source/tutorials/data-prerequisites.nblink b/docs/source/tutorials/data-prerequisites.nblink
new file mode 100644
index 000000000..895c5a246
--- /dev/null
+++ b/docs/source/tutorials/data-prerequisites.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../../../examples/00-Data-Prerequisites/prerequisites.ipynb"
+}
\ No newline at end of file
diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst
index e6152b78e..42223f93a 100644
--- a/docs/source/tutorials/index.rst
+++ b/docs/source/tutorials/index.rst
@@ -4,6 +4,9 @@ Tutorials
 All tutorials are based on Jupyter notebooks that are hosted on GitHub. 
 If you want to run the code yourself, you can find the notebooks in the `examples folder <https://github.com/neuralhydrology/neuralhydrology/tree/master/examples>`__ of the NeuralHydrology GitHub repository.
 
+| **Data Prerequisites**
+| For most of our tutorials you will need some data to train and evaluate models. In all of these examples we use the publicly available CAMELS US dataset. :doc:`This tutorial <data-prerequisites>` will guide you through the download process of the different dataset pieces and explain how the code expects the local folder structure.
+
 | **Introduction to NeuralHydrology**
 | If you're new to the NeuralHydrology package, :doc:`this tutorial <introduction>` is the place to get started. It walks you through the basic command-line and API usage patterns, and you get to train and evaluate your first model.
 
@@ -26,6 +29,7 @@ If you want to run the code yourself, you can find the notebooks in the `example
    :maxdepth: 1
    :caption: Contents:
 
+   data-prerequisites
    introduction
    adding-gru
    add-dataset
diff --git a/docs/source/usage/quickstart.rst b/docs/source/usage/quickstart.rst
index 3c5c5660f..0a2b5a2be 100644
--- a/docs/source/usage/quickstart.rst
+++ b/docs/source/usage/quickstart.rst
@@ -56,9 +56,7 @@ Data
 Training and evaluating models requires a dataset.
 If you're unsure where to start, a common dataset is CAMELS US, available at
 `CAMELS US (NCAR) <https://ral.ucar.edu/solutions/products/camels>`_.
-Download the "CAMELS time series meteorology, observed flow, meta data" and place the actual data folder
-(``basin_dataset_public_v1p2``) in a directory.
-This directory will be referred to as the "data directory", or ``data_dir``.
+This dataset is used in all of our tutorials and we have a `dedicated tutorial <../tutorials/data-prerequisites.nblink>`_ with download instructions that you might want to look at.
 
 
 Training a model
diff --git a/examples/00-Data-Prerequisites/prerequisites.ipynb b/examples/00-Data-Prerequisites/prerequisites.ipynb
new file mode 100644
index 000000000..d86874603
--- /dev/null
+++ b/examples/00-Data-Prerequisites/prerequisites.ipynb
@@ -0,0 +1,78 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Prerequisites \n",
+    "\n",
+    "All of our tutorials in which you train and evaluate a model use the [CAMELS US](https://ral.ucar.edu/solutions/products/camels) data set, either in its original form or with some extensions. \n",
+    "In this notebook, we will guide you through the process of downloading all essential data set pieces and explain how NeuralHydrology expects the folder structure of the CAMELS US dataset so that you will be able to run all of the tutorials.\n",
+    "\n",
+    "## CAMELS US meteorological time series and streamflow data\n",
+    "\n",
+    "The meteorological time series serve in most of our tutorials as model inputs, while the streamflow time series are the target values. You can download both from the [NCAR Homepage](https://ral.ucar.edu/solutions/products/camels). Click on \"\tCAMELS time series meteorology, observed flow, meta data (.zip)\" under \"CAMELS hydrometeorological time series\" or use [this](https://ral.ucar.edu/sites/default/files/public/product-tool/camels-catchment-attributes-and-meteorology-for-large-sample-studies-dataset-downloads/basin_timeseries_v1p2_metForcing_obsFlow.zip) direct link. The downloaded zip file, called `basin_timeseries_v1p2_metForcing_obsFlow.zip` contains two folders: `basin_dataset_public` (empty, 0 bytes) and `basin_dataset_public_v1p2` (not empty, 14.9 GB). Extract the second one (basin_dataset_public_v1p2) to any place you like and probably rename it something more meaningful, like `CAMELS_US`. This folder is referred to as the root directory of the CAMELS US dataset. Among others, it should contain the following subdirectories:\n",
+    "\n",
+    "```\n",
+    "CAMELS_US/              # originally named basin_dataset_public_v1p2\n",
+    "- basin_mean_forcings/  # contains the meteorological time series data \n",
+    "- usgs_streamflow/      # contains the streamflow data\n",
+    "- ...\n",
+    "```\n",
+    "\n",
+    "**NOTE**: In the default configs of our tutorials, we assume that the data is stored in `neuralhydrology/data/CAMELS_US`. If you stored the data elsewhere, either create a symbolic link to this location or change the `data_dir` argument in the `.yml` configs of the corresponding tutorials to point to your local CAMELS US root directory.\n",
+    "\n",
+    "\n",
+    "## Hourly forcing and streamflow data for CAMELS US basins\n",
+    "\n",
+    "(required for Tutorial 04 - Multi-Timescale Prediction)\n",
+    "\n",
+    "To be able to run this example yourself, you will need to download the [hourly NLDAS forcings and the hourly streamflow data](https://doi.org/10.5281/zenodo.4072700). Within the CAMELS US root directory, place the `nldas_hourly` and `usgs-streamflow` folders into a directory called `hourly` (`/path/to/CAMELS_US/hourly/{nldas_hourly,usgs-streamflow}`).\n",
+    "Alternatively, you can place the hourly netCDF file (`usgs-streamflow-nldas_hourly.nc`) from [Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.4072700) inside the `hourly/` folder instead of the NLDAS and streamflow csv files. Loading from netCDF will be faster than from the csv files. In case of the first option (downloading the two folders), the CAMELS US folder structure from above would extend to:\n",
+    "\n",
+    "```\n",
+    "CAMELS_US/              # originally named basin_dataset_public_v1p2\n",
+    "- basin_mean_forcings/  # contains the meteorological time series data \n",
+    "- usgs_streamflow/      # contains the streamflow data\n",
+    "- hourly/               # newly created folder to store the hourly forcing and streamflow data\n",
+    "    - nldas_hourly/     # NLDAS hourly forcing data\n",
+    "    - usgs-streamflow/  # hourly streamflow data\n",
+    "- ...\n",
+    "```\n",
+    "\n",
+    "In case you downloaded the `usgs-streamflow-nldas_hourly.nc` it should like this:\n",
+    "\n",
+    "```\n",
+    "CAMELS_US/                              # originally named basin_dataset_public_v1p2\n",
+    "- basin_mean_forcings/                  # contains the meteorological time series data \n",
+    "- usgs_streamflow/                      # contains the streamflow data\n",
+    "- hourly/                               # newly created folder to store the hourly forcing and streamflow data\n",
+    "    - usgs-streamflow-nldas_hourly.nc   # netCDF file containing hourly forcing and streamflow data\n",
+    "- ...\n",
+    "```\n",
+    "\n",
+    "## CAMELS US catchment attributes\n",
+    "\n",
+    "(required for Tutorial 06 - How-to Finetuning)\n",
+    "\n",
+    "When training a deep learning model, such as an LSTM, on data from more than one basin it is recommended to also use static catchment attributes as model inputs, alongside the meteorological forcings (see e.g. [this paper](https://hess.copernicus.org/articles/23/5089/2019/)). In tutorial 06, we use the static catchment attributes from the CAMELS US dataset that can be downloaded one the [same homeage](https://ral.ucar.edu/solutions/products/camels), a bit further down. Search for the section called \"CAMELS catchment attributes\". Here, download the only listed zip file \"CAMELS Attributes (.zip)\" or use [this](https://ral.ucar.edu/sites/default/files/public/product-tool/camels-catchment-attributes-and-meteorology-for-large-sample-studies-dataset-downloads/camels_attributes_v2.0.zip) direct link. The downloaded archive contains a folder called `camels_attributes_v2.0`. Extract this folder into the CAMELS US root directory (at the same level of `basin_mean_forcings` and `usgs_streamflow`). So your folder structure should at least look like this:\n",
+    "\n",
+    "```\n",
+    "CAMELS_US/                  # originally named basin_dataset_public_v1p2\n",
+    "- basin_mean_forcings/      # contains the meteorological time series data \n",
+    "- usgs_streamflow/          # contains the streamflow data\n",
+    "- camels_attributes_v2.0/   # extracted catchment attributes\n",
+    "- ...\n",
+    "```\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/01-Introduction/Introduction.ipynb b/examples/01-Introduction/Introduction.ipynb
index 4877e2390..1d9b53ea9 100644
--- a/examples/01-Introduction/Introduction.ipynb
+++ b/examples/01-Introduction/Introduction.ipynb
@@ -5,7 +5,11 @@
    "metadata": {},
    "source": [
     "# Introduction to NeuralHydrology\n",
-    "Before we start: This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/01-Introduction).\n",
+    "\n",
+    "**Before we start**\n",
+    "\n",
+    "- This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/01-Introduction).\n",
+    "- To be able to run this notebook locally, you need to download the publicly available CAMELS US rainfall-runoff dataset. See the [Data Prerequisites Tutorial](data-prerequisites.nblink) for a detailed description on where to download the data and how to structure your local dataset folder.\n",
     "\n",
     "The Python package NeuralHydrology was was developed with a strong focus on research. The main application area is hydrology, however, in principle the code can be used with any data. To allow fast iteration of research ideas, we tried to develop the package as modular as possible so that new models, new data sets, new loss functions, new regularizations, new metrics etc. can be integrated with minor effort.\n",
     "\n",
@@ -20,10 +24,6 @@
     "\n",
     "For every run that you start, a new folder will be created. This folder is used to store the model and optimizer checkpoints, train data means/stds (needed for scaling during inference), tensorboard log file (can be used to monitor and compare training runs visually), validation results (optionally) and training progress figures (optionally, e.g., model predictions and observations for _n_ random basins). During inference, the evaluation results will also be stored in this directory (e.g., test period results).\n",
     "\n",
-    "### Data requirements\n",
-    "\n",
-    "This tutorial uses data from the publicly available [CAMELS US dataset](https://ral.ucar.edu/solutions/products/camels). If you want to run this tutorial yourself, make sure to download the dataset (streamflow data, meteorological forcings and attributes) from the NCAR homepage.\n",
-    "\n",
     "\n",
     "### TensorBoard logging\n",
     "By default, the training progress is logged in TensorBoard files (add `log_tensorboard: False` to the config to disable TensorBoard logging). If you installed a Python environment from one of our environment files, you have TensorBoard already installed. If not, you can install TensorBoard with:\n",
@@ -100,6 +100,7 @@
     "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "import torch\n",
     "from neuralhydrology.evaluation import metrics\n",
     "from neuralhydrology.nh_run import start_run, eval_run"
    ]
@@ -110,7 +111,10 @@
    "source": [
     "### Train a model for a single config file\n",
     "\n",
-    "The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow` and `camels_attributes_v2.0`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `1_basin.yml` config file that is located in the same directory as this notebook."
+    "**Note**\n",
+    "\n",
+    "- The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow` and `camels_attributes_v2.0`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `1_basin.yml` config file that is located in the same directory as this notebook.\n",
+    "- By default, the config (`1_basin.yml`) assumes that you have a CUDA-capable NVIDIA GPU (see config argument `device`). In case you don't have any or you have one but want to train on the CPU, you can either change the config argument to `device: cpu` or pass `gpu=-1` to the `start_run()` function."
    ]
   },
   {
@@ -313,7 +317,13 @@
     }
    ],
    "source": [
-    "start_run(config_file=Path(\"1_basin.yml\"))"
+    "# by default we assume that you have at least one CUDA-capable NVIDIA GPU\n",
+    "if torch.cuda.is_available():\n",
+    "    start_run(config_file=Path(\"1_basin.yml\"))\n",
+    "\n",
+    "# fall back to CPU-only mode\n",
+    "else:\n",
+    "    start_run(config_file=Path(\"1_basin.yml\"), gpu=-1)"
    ]
   },
   {
@@ -321,7 +331,7 @@
    "metadata": {},
    "source": [
     "### Evaluate run on test set\n",
-    "The run directory that needs to be specified for evaluation is printed in the output log above. Since the folder name is created dynamically (including the date and time of the start of the run) you will need to change the `run_dir` argument according to your local directory name."
+    "The run directory that needs to be specified for evaluation is printed in the output log above. Since the folder name is created dynamically (including the date and time of the start of the run) you will need to change the `run_dir` argument according to your local directory name. By default, it will use the same device as during the training process."
    ]
   },
   {
diff --git a/examples/02-Adding-Models/adding-gru.ipynb b/examples/02-Adding-Models/adding-gru.ipynb
index a4305f817..885a7f536 100644
--- a/examples/02-Adding-Models/adding-gru.ipynb
+++ b/examples/02-Adding-Models/adding-gru.ipynb
@@ -21,7 +21,6 @@
    "outputs": [],
    "source": [
     "import inspect\n",
-    "from pathlib import Path\n",
     "from typing import Dict\n",
     "\n",
     "import torch\n",
@@ -30,7 +29,6 @@
     "from neuralhydrology.modelzoo import get_model\n",
     "from neuralhydrology.modelzoo.head import get_head\n",
     "from neuralhydrology.modelzoo.basemodel import BaseModel\n",
-    "from neuralhydrology.modelzoo.template import TemplateModel\n",
     "from neuralhydrology.utils.config import Config"
    ]
   },
diff --git a/examples/03-Adding-Datasets/adding-camels-cl.ipynb b/examples/03-Adding-Datasets/adding-camels-cl.ipynb
index 8865480cc..ab1443559 100644
--- a/examples/03-Adding-Datasets/adding-camels-cl.ipynb
+++ b/examples/03-Adding-Datasets/adding-camels-cl.ipynb
@@ -35,14 +35,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
     "from pathlib import Path\n",
     "from typing import List, Dict, Union\n",
     "\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "import xarray\n",
-    "from tqdm import tqdm\n",
     "\n",
     "from neuralhydrology.datasetzoo.basedataset import BaseDataset\n",
     "from neuralhydrology.utils.config import Config"
diff --git a/examples/04-Multi-Timescale/1_basin.yml b/examples/04-Multi-Timescale/1_basin.yml
index 23fcacb40..e8f2722a3 100644
--- a/examples/04-Multi-Timescale/1_basin.yml
+++ b/examples/04-Multi-Timescale/1_basin.yml
@@ -136,16 +136,10 @@ data_dir: ../../data/CAMELS_US
 # can be either a list of forcings or a single forcing product
 forcings:
   - nldas_hourly
-  - maurer_extended
   - daymet
 
 dynamic_inputs:
   1D:
-    - prcp(mm/day)_maurer_extended
-    - srad(W/m2)_maurer_extended
-    - tmax(C)_maurer_extended
-    - tmin(C)_maurer_extended
-    - vp(Pa)_maurer_extended
     - prcp(mm/day)_daymet
     - srad(W/m2)_daymet
     - tmax(C)_daymet
@@ -163,11 +157,6 @@ dynamic_inputs:
     - total_precipitation_nldas_hourly
     - wind_u_nldas_hourly
     - wind_v_nldas_hourly
-    - prcp(mm/day)_maurer_extended
-    - srad(W/m2)_maurer_extended
-    - tmax(C)_maurer_extended
-    - tmin(C)_maurer_extended
-    - vp(Pa)_maurer_extended
     - prcp(mm/day)_daymet
     - srad(W/m2)_daymet
     - tmax(C)_daymet
diff --git a/examples/04-Multi-Timescale/multi-timescale.ipynb b/examples/04-Multi-Timescale/multi-timescale.ipynb
index 04da646d5..859a49194 100644
--- a/examples/04-Multi-Timescale/multi-timescale.ipynb
+++ b/examples/04-Multi-Timescale/multi-timescale.ipynb
@@ -5,7 +5,11 @@
    "metadata": {},
    "source": [
     "# Multi-Timescale Prediction\n",
-    "Before we start: This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/04-Multi-Timescale).\n",
+    "\n",
+    "**Before we start**\n",
+    "\n",
+    "- This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/04-Multi-Timescale).\n",
+    "- To be able to run this notebook locally, you need to download the publicly available CAMELS US rainfall-runoff dataset and a publicly available extensions for hourly forcing and streamflow data. See the [Data Prerequisites Tutorial](data-prerequisites.nblink) for a detailed description on where to download the data and how to structure your local dataset folder. Note the special [section](data-prerequisites.nblink#Hourly-forcing-and-streamflow-data-for-CAMELS-US-basins)  with additional requirements for this tutorial.\n",
     "\n",
     "This notebook showcases some ways to use the **MTS-LSTM** from our recent publication to generate predictions at multiple timescales: [**\"Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network\"**](https://hess.copernicus.org/articles/25/2045/2021/).\n",
     "\n",
@@ -18,10 +22,6 @@
     "Not only does this take ages to train and evaluate, but also the training procedure becomes quite unstable and it is theoretically really hard for the model to learn dependencies over that many time steps.\n",
     "What's more, the daily and hourly predictions might end up being inconsistent, because the two models are entirely unrelated.\n",
     "\n",
-    "## Data requirements\n",
-    "\n",
-    "To be able to run this example yourself, you will need to download the [hourly NLDAS forcings and the hourly streamflow data](https://doi.org/10.5281/zenodo.4072700) and the [extended Maurer forcings](https://doi.org/10.4211/hs.17c896843cf940339c3c3496d0c1c077). Within the CAMELS US root directory, place the `nldas_hourly` into a folder called `hourly` (`/path/to/CAMELS_US/hourly/nldas_hourly`) and the `maurer_extended` folder into `basin_mean_forcing` (`/path/to/CAMELS_US/basin_mean_forcing/maurer_extended`)\n",
-    "\n",
     "## MTS-LSTM\n",
     "\n",
     "MTS-LSTM solves these issues: We can use a single model to predict both hourly and daily discharge, and with some tricks, we can push the model toward predictions that are consistent across timescales.\n",
@@ -75,12 +75,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pickle\n",
     "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "import torch\n",
     "from neuralhydrology.evaluation import metrics, get_tester\n",
-    "from neuralhydrology.nh_run import start_run, eval_run\n",
+    "from neuralhydrology.nh_run import start_run\n",
     "from neuralhydrology.utils.config import Config"
    ]
   },
@@ -142,12 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "{'1D': ['prcp(mm/day)_maurer_extended',\n",
-       "  'srad(W/m2)_maurer_extended',\n",
-       "  'tmax(C)_maurer_extended',\n",
-       "  'tmin(C)_maurer_extended',\n",
-       "  'vp(Pa)_maurer_extended',\n",
-       "  'prcp(mm/day)_daymet',\n",
+       "{'1D': ['prcp(mm/day)_daymet',\n",
        "  'srad(W/m2)_daymet',\n",
        "  'tmax(C)_daymet',\n",
        "  'tmin(C)_daymet',\n",
@@ -163,11 +158,6 @@
        "  'total_precipitation_nldas_hourly',\n",
        "  'wind_u_nldas_hourly',\n",
        "  'wind_v_nldas_hourly',\n",
-       "  'prcp(mm/day)_maurer_extended',\n",
-       "  'srad(W/m2)_maurer_extended',\n",
-       "  'tmax(C)_maurer_extended',\n",
-       "  'tmin(C)_maurer_extended',\n",
-       "  'vp(Pa)_maurer_extended',\n",
        "  'prcp(mm/day)_daymet',\n",
        "  'srad(W/m2)_daymet',\n",
        "  'tmax(C)_daymet',\n",
@@ -193,7 +183,10 @@
     "\n",
     "We start model training of our single-basin toy example with `start_run`.\n",
     "\n",
-    "**Note** The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow`, `hourly` and `camels_attributes_v2.0`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `1_basin.yml` config file that is located in the same directory as this notebook."
+    "**Note** \n",
+    "\n",
+    "- The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow`, and `hourly`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `1_basin.yml` config file that is located in the same directory as this notebook.\n",
+    "- By default, the config (`1_basin.yml`) assumes that you have a CUDA-capable NVIDIA GPU (see config argument `device`). In case you don't have any or you have one but one to train on the CPU, you can either change the config argument to `device: cpu` or pass `gpu=-1` to the `start_run()` function."
    ]
   },
   {
@@ -207,189 +200,195 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-05 22:02:00,435: Logging to /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0501_220200/output.log initialized.\n",
-      "2022-01-05 22:02:00,436: ### Folder structure created at /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0501_220200\n",
-      "2022-01-05 22:02:00,436: ### Run configurations for test_run\n",
-      "2022-01-05 22:02:00,436: experiment_name: test_run\n",
-      "2022-01-05 22:02:00,436: use_frequencies: ['1H', '1D']\n",
-      "2022-01-05 22:02:00,437: train_basin_file: 1_basin.txt\n",
-      "2022-01-05 22:02:00,437: validation_basin_file: 1_basin.txt\n",
-      "2022-01-05 22:02:00,437: test_basin_file: 1_basin.txt\n",
-      "2022-01-05 22:02:00,437: train_start_date: 1999-10-01 00:00:00\n",
-      "2022-01-05 22:02:00,437: train_end_date: 2008-09-30 00:00:00\n",
-      "2022-01-05 22:02:00,438: validation_start_date: 1996-10-01 00:00:00\n",
-      "2022-01-05 22:02:00,438: validation_end_date: 1999-09-30 00:00:00\n",
-      "2022-01-05 22:02:00,438: test_start_date: 1989-10-01 00:00:00\n",
-      "2022-01-05 22:02:00,439: test_end_date: 1996-09-30 00:00:00\n",
-      "2022-01-05 22:02:00,439: device: cpu\n",
-      "2022-01-05 22:02:00,439: validate_every: 5\n",
-      "2022-01-05 22:02:00,439: validate_n_random_basins: 1\n",
-      "2022-01-05 22:02:00,439: metrics: ['NSE']\n",
-      "2022-01-05 22:02:00,440: model: mtslstm\n",
-      "2022-01-05 22:02:00,440: shared_mtslstm: False\n",
-      "2022-01-05 22:02:00,440: transfer_mtslstm_states: {'h': 'linear', 'c': 'linear'}\n",
-      "2022-01-05 22:02:00,440: head: regression\n",
-      "2022-01-05 22:02:00,441: output_activation: linear\n",
-      "2022-01-05 22:02:00,441: hidden_size: 20\n",
-      "2022-01-05 22:02:00,441: initial_forget_bias: 3\n",
-      "2022-01-05 22:02:00,441: output_dropout: 0.4\n",
-      "2022-01-05 22:02:00,442: optimizer: Adam\n",
-      "2022-01-05 22:02:00,442: loss: MSE\n",
-      "2022-01-05 22:02:00,442: regularization: ['tie_frequencies']\n",
-      "2022-01-05 22:02:00,442: learning_rate: {0: 0.01, 30: 0.005, 40: 0.001}\n",
-      "2022-01-05 22:02:00,443: batch_size: 256\n",
-      "2022-01-05 22:02:00,443: epochs: 50\n",
-      "2022-01-05 22:02:00,443: clip_gradient_norm: 1\n",
-      "2022-01-05 22:02:00,443: predict_last_n: {'1D': 1, '1H': 24}\n",
-      "2022-01-05 22:02:00,444: seq_length: {'1D': 365, '1H': 336}\n",
-      "2022-01-05 22:02:00,444: num_workers: 8\n",
-      "2022-01-05 22:02:00,444: log_interval: 5\n",
-      "2022-01-05 22:02:00,444: log_tensorboard: False\n",
-      "2022-01-05 22:02:00,444: log_n_figures: 0\n",
-      "2022-01-05 22:02:00,445: save_weights_every: 1\n",
-      "2022-01-05 22:02:00,445: dataset: hourly_camels_us\n",
-      "2022-01-05 22:02:00,445: data_dir: ../../data/CAMELS_US\n",
-      "2022-01-05 22:02:00,446: forcings: ['nldas_hourly', 'maurer_extended', 'daymet']\n",
-      "2022-01-05 22:02:00,446: dynamic_inputs: {'1D': ['prcp(mm/day)_maurer_extended', 'srad(W/m2)_maurer_extended', 'tmax(C)_maurer_extended', 'tmin(C)_maurer_extended', 'vp(Pa)_maurer_extended', 'prcp(mm/day)_daymet', 'srad(W/m2)_daymet', 'tmax(C)_daymet', 'tmin(C)_daymet', 'vp(Pa)_daymet'], '1H': ['convective_fraction_nldas_hourly', 'longwave_radiation_nldas_hourly', 'potential_energy_nldas_hourly', 'potential_evaporation_nldas_hourly', 'pressure_nldas_hourly', 'shortwave_radiation_nldas_hourly', 'specific_humidity_nldas_hourly', 'temperature_nldas_hourly', 'total_precipitation_nldas_hourly', 'wind_u_nldas_hourly', 'wind_v_nldas_hourly', 'prcp(mm/day)_maurer_extended', 'srad(W/m2)_maurer_extended', 'tmax(C)_maurer_extended', 'tmin(C)_maurer_extended', 'vp(Pa)_maurer_extended', 'prcp(mm/day)_daymet', 'srad(W/m2)_daymet', 'tmax(C)_daymet', 'tmin(C)_daymet', 'vp(Pa)_daymet']}\n",
-      "2022-01-05 22:02:00,446: target_variables: ['qobs_mm_per_hour']\n",
-      "2022-01-05 22:02:00,447: clip_targets_to_zero: ['qobs_mm_per_hour']\n",
-      "2022-01-05 22:02:00,447: number_of_basins: 1\n",
-      "2022-01-05 22:02:00,447: run_dir: /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0501_220200\n",
-      "2022-01-05 22:02:00,448: train_dir: /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0501_220200/train_data\n",
-      "2022-01-05 22:02:00,448: img_log_dir: /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0501_220200/img_log\n",
-      "2022-01-05 22:02:00,449: ### Device cpu will be used for training\n",
-      "2022-01-05 22:02:00,450: No specific hidden size for frequencies are specified. Same hidden size is used for all.\n",
-      "2022-01-05 22:02:00,471: Loading basin data into xarray data set.\n",
-      "100%|██████████| 1/1 [00:01<00:00,  1.32s/it]\n",
-      "2022-01-05 22:02:01,811: Create lookup table and convert to pytorch tensor\n",
-      "100%|██████████| 1/1 [00:00<00:00,  1.02it/s]\n",
-      "# Epoch 1: 100%|██████████| 11/11 [00:03<00:00,  3.25it/s, Loss: 0.9623]\n",
-      "2022-01-05 22:02:06,236: Epoch 1 average loss: 0.8914267908443104\n",
-      "# Epoch 2: 100%|██████████| 11/11 [00:03<00:00,  3.39it/s, Loss: 0.4364]\n",
-      "2022-01-05 22:02:09,496: Epoch 2 average loss: 0.6788762049241499\n",
-      "# Epoch 3: 100%|██████████| 11/11 [00:03<00:00,  3.30it/s, Loss: 0.7639]\n",
-      "2022-01-05 22:02:12,836: Epoch 3 average loss: 0.592302988875996\n",
-      "# Epoch 4: 100%|██████████| 11/11 [00:03<00:00,  3.18it/s, Loss: 0.6960]\n",
-      "2022-01-05 22:02:16,303: Epoch 4 average loss: 0.5118056481534784\n",
-      "# Epoch 5: 100%|██████████| 11/11 [00:03<00:00,  3.16it/s, Loss: 0.2698]\n",
-      "2022-01-05 22:02:19,789: Epoch 5 average loss: 0.4462957273830067\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  1.19it/s]\n",
-      "2022-01-05 22:02:20,632: Epoch 5 average validation loss: 0.30068 -- Median validation metrics: NSE_1H: 0.47241, NSE_1D: 0.53858\n",
-      "# Epoch 6: 100%|██████████| 11/11 [00:03<00:00,  3.21it/s, Loss: 0.3741]\n",
-      "2022-01-05 22:02:24,063: Epoch 6 average loss: 0.4128344790502028\n",
-      "# Epoch 7: 100%|██████████| 11/11 [00:03<00:00,  3.15it/s, Loss: 0.4862]\n",
-      "2022-01-05 22:02:27,569: Epoch 7 average loss: 0.39042010361498053\n",
-      "# Epoch 8: 100%|██████████| 11/11 [00:03<00:00,  3.06it/s, Loss: 0.2964]\n",
-      "2022-01-05 22:02:31,180: Epoch 8 average loss: 0.34517697312615137\n",
-      "# Epoch 9: 100%|██████████| 11/11 [00:03<00:00,  3.21it/s, Loss: 0.5691]\n",
-      "2022-01-05 22:02:34,614: Epoch 9 average loss: 0.3443178046833385\n",
-      "# Epoch 10: 100%|██████████| 11/11 [00:03<00:00,  2.87it/s, Loss: 0.3075]\n",
-      "2022-01-05 22:02:38,460: Epoch 10 average loss: 0.32101957499980927\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.28it/s]\n",
-      "2022-01-05 22:02:38,903: Epoch 10 average validation loss: 0.29418 -- Median validation metrics: NSE_1H: 0.50551, NSE_1D: 0.63188\n",
-      "# Epoch 11: 100%|██████████| 11/11 [00:03<00:00,  3.17it/s, Loss: 0.1896]\n",
-      "2022-01-05 22:02:42,381: Epoch 11 average loss: 0.3042802959680557\n",
-      "# Epoch 12: 100%|██████████| 11/11 [00:03<00:00,  3.20it/s, Loss: 0.2736]\n",
-      "2022-01-05 22:02:45,826: Epoch 12 average loss: 0.3128009248863567\n",
-      "# Epoch 13: 100%|██████████| 11/11 [00:03<00:00,  3.02it/s, Loss: 0.2377]\n",
-      "2022-01-05 22:02:49,483: Epoch 13 average loss: 0.28694039989601483\n",
-      "# Epoch 14: 100%|██████████| 11/11 [00:03<00:00,  2.95it/s, Loss: 0.2256]\n",
-      "2022-01-05 22:02:53,227: Epoch 14 average loss: 0.27768654173070734\n",
-      "# Epoch 15: 100%|██████████| 11/11 [00:03<00:00,  3.10it/s, Loss: 0.2803]\n",
-      "2022-01-05 22:02:56,785: Epoch 15 average loss: 0.2704837227409536\n",
+      "2022-02-07 21:47:48,198: Logging to /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0702_214748/output.log initialized.\n",
+      "2022-02-07 21:47:48,199: ### Folder structure created at /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0702_214748\n",
+      "2022-02-07 21:47:48,199: ### Run configurations for test_run\n",
+      "2022-02-07 21:47:48,199: experiment_name: test_run\n",
+      "2022-02-07 21:47:48,199: use_frequencies: ['1H', '1D']\n",
+      "2022-02-07 21:47:48,200: train_basin_file: 1_basin.txt\n",
+      "2022-02-07 21:47:48,200: validation_basin_file: 1_basin.txt\n",
+      "2022-02-07 21:47:48,200: test_basin_file: 1_basin.txt\n",
+      "2022-02-07 21:47:48,200: train_start_date: 1999-10-01 00:00:00\n",
+      "2022-02-07 21:47:48,201: train_end_date: 2008-09-30 00:00:00\n",
+      "2022-02-07 21:47:48,201: validation_start_date: 1996-10-01 00:00:00\n",
+      "2022-02-07 21:47:48,201: validation_end_date: 1999-09-30 00:00:00\n",
+      "2022-02-07 21:47:48,202: test_start_date: 1989-10-01 00:00:00\n",
+      "2022-02-07 21:47:48,202: test_end_date: 1996-09-30 00:00:00\n",
+      "2022-02-07 21:47:48,202: device: cpu\n",
+      "2022-02-07 21:47:48,202: validate_every: 5\n",
+      "2022-02-07 21:47:48,203: validate_n_random_basins: 1\n",
+      "2022-02-07 21:47:48,203: metrics: ['NSE']\n",
+      "2022-02-07 21:47:48,203: model: mtslstm\n",
+      "2022-02-07 21:47:48,203: shared_mtslstm: False\n",
+      "2022-02-07 21:47:48,204: transfer_mtslstm_states: {'h': 'linear', 'c': 'linear'}\n",
+      "2022-02-07 21:47:48,204: head: regression\n",
+      "2022-02-07 21:47:48,204: output_activation: linear\n",
+      "2022-02-07 21:47:48,204: hidden_size: 20\n",
+      "2022-02-07 21:47:48,204: initial_forget_bias: 3\n",
+      "2022-02-07 21:47:48,205: output_dropout: 0.4\n",
+      "2022-02-07 21:47:48,205: optimizer: Adam\n",
+      "2022-02-07 21:47:48,205: loss: MSE\n",
+      "2022-02-07 21:47:48,205: regularization: ['tie_frequencies']\n",
+      "2022-02-07 21:47:48,206: learning_rate: {0: 0.01, 30: 0.005, 40: 0.001}\n",
+      "2022-02-07 21:47:48,206: batch_size: 256\n",
+      "2022-02-07 21:47:48,206: epochs: 50\n",
+      "2022-02-07 21:47:48,206: clip_gradient_norm: 1\n",
+      "2022-02-07 21:47:48,207: predict_last_n: {'1D': 1, '1H': 24}\n",
+      "2022-02-07 21:47:48,207: seq_length: {'1D': 365, '1H': 336}\n",
+      "2022-02-07 21:47:48,207: num_workers: 8\n",
+      "2022-02-07 21:47:48,208: log_interval: 5\n",
+      "2022-02-07 21:47:48,208: log_tensorboard: False\n",
+      "2022-02-07 21:47:48,208: log_n_figures: 0\n",
+      "2022-02-07 21:47:48,208: save_weights_every: 1\n",
+      "2022-02-07 21:47:48,209: dataset: hourly_camels_us\n",
+      "2022-02-07 21:47:48,209: data_dir: ../../data/CAMELS_US\n",
+      "2022-02-07 21:47:48,209: forcings: ['nldas_hourly', 'daymet']\n",
+      "2022-02-07 21:47:48,209: dynamic_inputs: {'1D': ['prcp(mm/day)_daymet', 'srad(W/m2)_daymet', 'tmax(C)_daymet', 'tmin(C)_daymet', 'vp(Pa)_daymet'], '1H': ['convective_fraction_nldas_hourly', 'longwave_radiation_nldas_hourly', 'potential_energy_nldas_hourly', 'potential_evaporation_nldas_hourly', 'pressure_nldas_hourly', 'shortwave_radiation_nldas_hourly', 'specific_humidity_nldas_hourly', 'temperature_nldas_hourly', 'total_precipitation_nldas_hourly', 'wind_u_nldas_hourly', 'wind_v_nldas_hourly', 'prcp(mm/day)_daymet', 'srad(W/m2)_daymet', 'tmax(C)_daymet', 'tmin(C)_daymet', 'vp(Pa)_daymet']}\n",
+      "2022-02-07 21:47:48,209: target_variables: ['qobs_mm_per_hour']\n",
+      "2022-02-07 21:47:48,210: clip_targets_to_zero: ['qobs_mm_per_hour']\n",
+      "2022-02-07 21:47:48,210: number_of_basins: 1\n",
+      "2022-02-07 21:47:48,210: run_dir: /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0702_214748\n",
+      "2022-02-07 21:47:48,210: train_dir: /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0702_214748/train_data\n",
+      "2022-02-07 21:47:48,210: img_log_dir: /home/frederik/Projects/neuralhydrology/examples/04-Multi-Timescale/runs/test_run_0702_214748/img_log\n",
+      "2022-02-07 21:47:48,212: ### Device cpu will be used for training\n",
+      "2022-02-07 21:47:48,213: No specific hidden size for frequencies are specified. Same hidden size is used for all.\n",
+      "2022-02-07 21:47:48,238: Loading basin data into xarray data set.\n",
+      "100%|██████████| 1/1 [00:01<00:00,  1.33s/it]\n",
+      "2022-02-07 21:47:49,587: Create lookup table and convert to pytorch tensor\n",
+      "100%|██████████| 1/1 [00:00<00:00,  1.04it/s]\n",
+      "# Epoch 1: 100%|██████████| 11/11 [00:03<00:00,  3.43it/s, Loss: 0.5613]\n",
+      "2022-02-07 21:47:53,818: Epoch 1 average loss: 0.9446556568145752\n",
+      "# Epoch 2: 100%|██████████| 11/11 [00:03<00:00,  3.36it/s, Loss: 0.6071]\n",
+      "2022-02-07 21:47:57,108: Epoch 2 average loss: 0.7158133712681857\n",
+      "# Epoch 3: 100%|██████████| 11/11 [00:03<00:00,  3.03it/s, Loss: 0.6686]\n",
+      "2022-02-07 21:48:00,752: Epoch 3 average loss: 0.6419699598442424\n",
+      "# Epoch 4: 100%|██████████| 11/11 [00:04<00:00,  2.74it/s, Loss: 0.5472]\n",
+      "2022-02-07 21:48:04,791: Epoch 4 average loss: 0.5561340884728865\n",
+      "# Epoch 5: 100%|██████████| 11/11 [00:03<00:00,  2.84it/s, Loss: 0.5187]\n",
+      "2022-02-07 21:48:08,686: Epoch 5 average loss: 0.4664872884750366\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  1.16it/s]\n",
+      "2022-02-07 21:48:09,556: Epoch 5 average validation loss: 0.28649 -- Median validation metrics: NSE_1H: 0.53499, NSE_1D: 0.51014\n",
+      "# Epoch 6: 100%|██████████| 11/11 [00:03<00:00,  3.11it/s, Loss: 0.4976]\n",
+      "2022-02-07 21:48:13,109: Epoch 6 average loss: 0.4247425902973522\n",
+      "# Epoch 7: 100%|██████████| 11/11 [00:03<00:00,  3.05it/s, Loss: 0.4753]\n",
+      "2022-02-07 21:48:16,724: Epoch 7 average loss: 0.3831088461659171\n",
+      "# Epoch 8: 100%|██████████| 11/11 [00:03<00:00,  3.26it/s, Loss: 0.2981]\n",
+      "2022-02-07 21:48:20,105: Epoch 8 average loss: 0.35663946921175177\n",
+      "# Epoch 9: 100%|██████████| 11/11 [00:03<00:00,  3.32it/s, Loss: 0.2949]\n",
+      "2022-02-07 21:48:23,428: Epoch 9 average loss: 0.338488906621933\n",
+      "# Epoch 10: 100%|██████████| 11/11 [00:03<00:00,  3.06it/s, Loss: 0.2331]\n",
+      "2022-02-07 21:48:27,037: Epoch 10 average loss: 0.31584957242012024\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  1.91it/s]\n",
+      "2022-02-07 21:48:27,572: Epoch 10 average validation loss: 0.28327 -- Median validation metrics: NSE_1H: 0.56991, NSE_1D: 0.57565\n",
+      "# Epoch 11: 100%|██████████| 11/11 [00:03<00:00,  3.10it/s, Loss: 0.5352]\n",
+      "2022-02-07 21:48:31,132: Epoch 11 average loss: 0.31939939477226953\n",
+      "# Epoch 12: 100%|██████████| 11/11 [00:03<00:00,  3.34it/s, Loss: 0.2578]\n",
+      "2022-02-07 21:48:34,439: Epoch 12 average loss: 0.29360065541484137\n",
+      "# Epoch 13: 100%|██████████| 11/11 [00:03<00:00,  3.07it/s, Loss: 0.3209]\n",
+      "2022-02-07 21:48:38,038: Epoch 13 average loss: 0.3003786666826768\n",
+      "# Epoch 14: 100%|██████████| 11/11 [00:03<00:00,  3.29it/s, Loss: 0.2610]\n",
+      "2022-02-07 21:48:41,398: Epoch 14 average loss: 0.2855734960599379\n",
+      "# Epoch 15: 100%|██████████| 11/11 [00:03<00:00,  3.37it/s, Loss: 0.1881]\n",
+      "2022-02-07 21:48:44,671: Epoch 15 average loss: 0.278819359161637\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  1.96it/s]\n",
+      "2022-02-07 21:48:45,185: Epoch 15 average validation loss: 0.26410 -- Median validation metrics: NSE_1H: 0.62576, NSE_1D: 0.62524\n",
+      "# Epoch 16: 100%|██████████| 11/11 [00:03<00:00,  3.04it/s, Loss: 0.4897]\n",
+      "2022-02-07 21:48:48,812: Epoch 16 average loss: 0.27201272411779925\n",
+      "# Epoch 17: 100%|██████████| 11/11 [00:03<00:00,  3.38it/s, Loss: 0.3502]\n",
+      "2022-02-07 21:48:52,081: Epoch 17 average loss: 0.26862989230589435\n",
+      "# Epoch 18: 100%|██████████| 11/11 [00:03<00:00,  3.35it/s, Loss: 0.1765]\n",
+      "2022-02-07 21:48:55,371: Epoch 18 average loss: 0.27555477212775836\n",
+      "# Epoch 19: 100%|██████████| 11/11 [00:03<00:00,  2.94it/s, Loss: 0.1875]\n",
+      "2022-02-07 21:48:59,128: Epoch 19 average loss: 0.2604310038414868\n",
+      "# Epoch 20: 100%|██████████| 11/11 [00:03<00:00,  3.34it/s, Loss: 0.2523]\n",
+      "2022-02-07 21:49:02,437: Epoch 20 average loss: 0.25056117366660724\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.53it/s]\n",
+      "2022-02-07 21:49:02,836: Epoch 20 average validation loss: 0.24424 -- Median validation metrics: NSE_1H: 0.62498, NSE_1D: 0.66103\n",
+      "# Epoch 21: 100%|██████████| 11/11 [00:03<00:00,  3.24it/s, Loss: 0.3689]\n",
+      "2022-02-07 21:49:06,244: Epoch 21 average loss: 0.24260467968203805\n",
+      "# Epoch 22: 100%|██████████| 11/11 [00:03<00:00,  3.37it/s, Loss: 0.2397]\n",
+      "2022-02-07 21:49:09,522: Epoch 22 average loss: 0.24095887623049997\n",
+      "# Epoch 23: 100%|██████████| 11/11 [00:03<00:00,  3.29it/s, Loss: 0.2768]\n",
+      "2022-02-07 21:49:12,874: Epoch 23 average loss: 0.2202805063941262\n",
+      "# Epoch 24: 100%|██████████| 11/11 [00:03<00:00,  3.21it/s, Loss: 0.1695]\n",
+      "2022-02-07 21:49:16,315: Epoch 24 average loss: 0.21233159574595364\n",
+      "# Epoch 25: 100%|██████████| 11/11 [00:03<00:00,  3.27it/s, Loss: 0.1206]\n",
+      "2022-02-07 21:49:19,699: Epoch 25 average loss: 0.22572010349143634\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]\n",
+      "2022-02-07 21:49:20,165: Epoch 25 average validation loss: 0.24335 -- Median validation metrics: NSE_1H: 0.63686, NSE_1D: 0.69674\n",
+      "# Epoch 26: 100%|██████████| 11/11 [00:03<00:00,  3.26it/s, Loss: 0.1754]\n",
+      "2022-02-07 21:49:23,550: Epoch 26 average loss: 0.211635635657744\n",
+      "# Epoch 27: 100%|██████████| 11/11 [00:03<00:00,  2.98it/s, Loss: 0.2026]\n",
+      "2022-02-07 21:49:27,259: Epoch 27 average loss: 0.2034458206458525\n",
+      "# Epoch 28: 100%|██████████| 11/11 [00:03<00:00,  3.19it/s, Loss: 0.2870]\n",
+      "2022-02-07 21:49:30,721: Epoch 28 average loss: 0.2164496427232569\n",
+      "# Epoch 29: 100%|██████████| 11/11 [00:03<00:00,  3.15it/s, Loss: 0.1549]\n",
+      "2022-02-07 21:49:34,228: Epoch 29 average loss: 0.21828937259587375\n",
+      "2022-02-07 21:49:34,230: Setting learning rate to 0.005\n",
+      "# Epoch 30: 100%|██████████| 11/11 [00:03<00:00,  3.31it/s, Loss: 0.1830]\n",
+      "2022-02-07 21:49:37,569: Epoch 30 average loss: 0.2085901444608515\n",
       "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.61it/s]\n",
-      "2022-01-05 22:02:57,171: Epoch 15 average validation loss: 0.25227 -- Median validation metrics: NSE_1H: 0.61628, NSE_1D: 0.66668\n",
-      "# Epoch 16: 100%|██████████| 11/11 [00:03<00:00,  3.19it/s, Loss: 0.2409]\n",
-      "2022-01-05 22:03:00,633: Epoch 16 average loss: 0.26037282022562896\n",
-      "# Epoch 17: 100%|██████████| 11/11 [00:03<00:00,  3.15it/s, Loss: 0.2221]\n",
-      "2022-01-05 22:03:04,136: Epoch 17 average loss: 0.26930478621612897\n",
-      "# Epoch 18: 100%|██████████| 11/11 [00:03<00:00,  3.29it/s, Loss: 0.1612]\n",
-      "2022-01-05 22:03:07,487: Epoch 18 average loss: 0.2666787789626555\n",
-      "# Epoch 19: 100%|██████████| 11/11 [00:03<00:00,  3.22it/s, Loss: 0.2696]\n",
-      "2022-01-05 22:03:10,909: Epoch 19 average loss: 0.25731936503540387\n",
-      "# Epoch 20: 100%|██████████| 11/11 [00:03<00:00,  3.26it/s, Loss: 0.2770]\n",
-      "2022-01-05 22:03:14,298: Epoch 20 average loss: 0.25585508752952923\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.46it/s]\n",
-      "2022-01-05 22:03:14,708: Epoch 20 average validation loss: 0.24542 -- Median validation metrics: NSE_1H: 0.61112, NSE_1D: 0.70481\n",
-      "# Epoch 21: 100%|██████████| 11/11 [00:03<00:00,  2.97it/s, Loss: 0.2555]\n",
-      "2022-01-05 22:03:18,419: Epoch 21 average loss: 0.25178179009394214\n",
-      "# Epoch 22: 100%|██████████| 11/11 [00:03<00:00,  3.15it/s, Loss: 0.2188]\n",
-      "2022-01-05 22:03:21,921: Epoch 22 average loss: 0.24913157251748172\n",
-      "# Epoch 23: 100%|██████████| 11/11 [00:03<00:00,  3.02it/s, Loss: 0.1947]\n",
-      "2022-01-05 22:03:25,569: Epoch 23 average loss: 0.2425225173885172\n",
-      "# Epoch 24: 100%|██████████| 11/11 [00:03<00:00,  3.08it/s, Loss: 0.3461]\n",
-      "2022-01-05 22:03:29,146: Epoch 24 average loss: 0.23867659135298294\n",
-      "# Epoch 25: 100%|██████████| 11/11 [00:03<00:00,  3.05it/s, Loss: 0.2238]\n",
-      "2022-01-05 22:03:32,761: Epoch 25 average loss: 0.24060925028540872\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.26it/s]\n",
-      "2022-01-05 22:03:33,207: Epoch 25 average validation loss: 0.21608 -- Median validation metrics: NSE_1H: 0.65161, NSE_1D: 0.73109\n",
-      "# Epoch 26: 100%|██████████| 11/11 [00:03<00:00,  2.91it/s, Loss: 0.2291]\n",
-      "2022-01-05 22:03:37,002: Epoch 26 average loss: 0.21928481486710635\n",
-      "# Epoch 27: 100%|██████████| 11/11 [00:03<00:00,  3.11it/s, Loss: 0.2630]\n",
-      "2022-01-05 22:03:40,549: Epoch 27 average loss: 0.20643671398813074\n",
-      "# Epoch 28: 100%|██████████| 11/11 [00:03<00:00,  3.18it/s, Loss: 0.1596]\n",
-      "2022-01-05 22:03:44,016: Epoch 28 average loss: 0.21437353302132\n",
-      "# Epoch 29: 100%|██████████| 11/11 [00:03<00:00,  3.17it/s, Loss: 0.1860]\n",
-      "2022-01-05 22:03:47,501: Epoch 29 average loss: 0.21698082306168295\n",
-      "2022-01-05 22:03:47,505: Setting learning rate to 0.005\n",
-      "# Epoch 30: 100%|██████████| 11/11 [00:03<00:00,  2.96it/s, Loss: 0.1903]\n",
-      "2022-01-05 22:03:51,238: Epoch 30 average loss: 0.20377679846503519\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.28it/s]\n",
-      "2022-01-05 22:03:51,681: Epoch 30 average validation loss: 0.20297 -- Median validation metrics: NSE_1H: 0.68475, NSE_1D: 0.71835\n",
-      "# Epoch 31: 100%|██████████| 11/11 [00:03<00:00,  3.05it/s, Loss: 0.1482]\n",
-      "2022-01-05 22:03:55,301: Epoch 31 average loss: 0.19455843351104044\n",
-      "# Epoch 32: 100%|██████████| 11/11 [00:03<00:00,  3.13it/s, Loss: 0.4414]\n",
-      "2022-01-05 22:03:58,828: Epoch 32 average loss: 0.2104977328668941\n",
-      "# Epoch 33: 100%|██████████| 11/11 [00:03<00:00,  3.06it/s, Loss: 0.1850]\n",
-      "2022-01-05 22:04:02,430: Epoch 33 average loss: 0.19503808292475613\n",
-      "# Epoch 34: 100%|██████████| 11/11 [00:03<00:00,  2.97it/s, Loss: 0.1966]\n",
-      "2022-01-05 22:04:06,139: Epoch 34 average loss: 0.18598955463279376\n",
-      "# Epoch 35: 100%|██████████| 11/11 [00:03<00:00,  2.96it/s, Loss: 0.1608]\n",
-      "2022-01-05 22:04:09,862: Epoch 35 average loss: 0.19413248652761633\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.40it/s]\n",
-      "2022-01-05 22:04:10,282: Epoch 35 average validation loss: 0.21501 -- Median validation metrics: NSE_1H: 0.69470, NSE_1D: 0.72395\n",
-      "# Epoch 36: 100%|██████████| 11/11 [00:03<00:00,  2.86it/s, Loss: 0.1538]\n",
-      "2022-01-05 22:04:14,144: Epoch 36 average loss: 0.1822088442065499\n",
-      "# Epoch 37: 100%|██████████| 11/11 [00:03<00:00,  3.01it/s, Loss: 0.1710]\n",
-      "2022-01-05 22:04:17,810: Epoch 37 average loss: 0.17988274923779748\n",
-      "# Epoch 38: 100%|██████████| 11/11 [00:03<00:00,  3.00it/s, Loss: 0.1768]\n",
-      "2022-01-05 22:04:21,488: Epoch 38 average loss: 0.17677149176597595\n",
-      "# Epoch 39: 100%|██████████| 11/11 [00:03<00:00,  2.94it/s, Loss: 0.1229]\n",
-      "2022-01-05 22:04:25,242: Epoch 39 average loss: 0.17491780492392453\n",
-      "2022-01-05 22:04:25,244: Setting learning rate to 0.001\n",
-      "# Epoch 40: 100%|██████████| 11/11 [00:03<00:00,  2.96it/s, Loss: 0.1929]\n",
-      "2022-01-05 22:04:28,964: Epoch 40 average loss: 0.1730633560906757\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.49it/s]\n",
-      "2022-01-05 22:04:29,369: Epoch 40 average validation loss: 0.19447 -- Median validation metrics: NSE_1H: 0.71396, NSE_1D: 0.73444\n",
-      "# Epoch 41: 100%|██████████| 11/11 [00:03<00:00,  2.92it/s, Loss: 0.2180]\n",
-      "2022-01-05 22:04:33,147: Epoch 41 average loss: 0.16681656105951828\n",
-      "# Epoch 42: 100%|██████████| 11/11 [00:03<00:00,  3.13it/s, Loss: 0.1429]\n",
-      "2022-01-05 22:04:36,674: Epoch 42 average loss: 0.16383536566387524\n",
-      "# Epoch 43: 100%|██████████| 11/11 [00:03<00:00,  2.95it/s, Loss: 0.2649]\n",
-      "2022-01-05 22:04:40,417: Epoch 43 average loss: 0.16868004406040366\n",
-      "# Epoch 44: 100%|██████████| 11/11 [00:03<00:00,  2.97it/s, Loss: 0.1421]\n",
-      "2022-01-05 22:04:44,135: Epoch 44 average loss: 0.17060250450264325\n",
-      "# Epoch 45: 100%|██████████| 11/11 [00:03<00:00,  2.96it/s, Loss: 0.0992]\n",
-      "2022-01-05 22:04:47,860: Epoch 45 average loss: 0.1576978394931013\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.26it/s]\n",
-      "2022-01-05 22:04:48,307: Epoch 45 average validation loss: 0.19342 -- Median validation metrics: NSE_1H: 0.71255, NSE_1D: 0.73650\n",
-      "# Epoch 46: 100%|██████████| 11/11 [00:03<00:00,  2.87it/s, Loss: 0.0935]\n",
-      "2022-01-05 22:04:52,148: Epoch 46 average loss: 0.1623991077596491\n",
-      "# Epoch 47: 100%|██████████| 11/11 [00:03<00:00,  3.13it/s, Loss: 0.1071]\n",
-      "2022-01-05 22:04:55,677: Epoch 47 average loss: 0.15839685567400671\n",
-      "# Epoch 48: 100%|██████████| 11/11 [00:03<00:00,  3.04it/s, Loss: 0.1115]\n",
-      "2022-01-05 22:04:59,310: Epoch 48 average loss: 0.15985127335244959\n",
-      "# Epoch 49: 100%|██████████| 11/11 [00:03<00:00,  3.16it/s, Loss: 0.1089]\n",
-      "2022-01-05 22:05:02,806: Epoch 49 average loss: 0.16960356519980865\n",
-      "# Epoch 50: 100%|██████████| 11/11 [00:03<00:00,  3.11it/s, Loss: 0.1285]\n",
-      "2022-01-05 22:05:06,356: Epoch 50 average loss: 0.16915929723869672\n",
-      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  1.94it/s]\n",
-      "2022-01-05 22:05:06,875: Epoch 50 average validation loss: 0.19416 -- Median validation metrics: NSE_1H: 0.71073, NSE_1D: 0.73827\n"
+      "2022-02-07 21:49:37,956: Epoch 30 average validation loss: 0.24921 -- Median validation metrics: NSE_1H: 0.66604, NSE_1D: 0.68654\n",
+      "# Epoch 31: 100%|██████████| 11/11 [00:03<00:00,  3.20it/s, Loss: 0.2203]\n",
+      "2022-02-07 21:49:41,406: Epoch 31 average loss: 0.20122347501191226\n",
+      "# Epoch 32: 100%|██████████| 11/11 [00:03<00:00,  2.98it/s, Loss: 0.1803]\n",
+      "2022-02-07 21:49:45,113: Epoch 32 average loss: 0.18575408241965555\n",
+      "# Epoch 33: 100%|██████████| 11/11 [00:03<00:00,  3.52it/s, Loss: 0.1512]\n",
+      "2022-02-07 21:49:48,254: Epoch 33 average loss: 0.19562652842565018\n",
+      "# Epoch 34: 100%|██████████| 11/11 [00:03<00:00,  3.39it/s, Loss: 0.2088]\n",
+      "2022-02-07 21:49:51,515: Epoch 34 average loss: 0.19269944727420807\n",
+      "# Epoch 35: 100%|██████████| 11/11 [00:03<00:00,  3.33it/s, Loss: 0.1470]\n",
+      "2022-02-07 21:49:54,831: Epoch 35 average loss: 0.19431324845010584\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.41it/s]\n",
+      "2022-02-07 21:49:55,255: Epoch 35 average validation loss: 0.26080 -- Median validation metrics: NSE_1H: 0.64356, NSE_1D: 0.70908\n",
+      "# Epoch 36: 100%|██████████| 11/11 [00:03<00:00,  3.32it/s, Loss: 0.2186]\n",
+      "2022-02-07 21:49:58,581: Epoch 36 average loss: 0.18218417194756595\n",
+      "# Epoch 37: 100%|██████████| 11/11 [00:03<00:00,  3.38it/s, Loss: 0.2972]\n",
+      "2022-02-07 21:50:01,846: Epoch 37 average loss: 0.18907048891891132\n",
+      "# Epoch 38: 100%|██████████| 11/11 [00:03<00:00,  3.51it/s, Loss: 0.1795]\n",
+      "2022-02-07 21:50:04,996: Epoch 38 average loss: 0.17613499002023178\n",
+      "# Epoch 39: 100%|██████████| 11/11 [00:03<00:00,  3.17it/s, Loss: 0.1411]\n",
+      "2022-02-07 21:50:08,475: Epoch 39 average loss: 0.17041322724385696\n",
+      "2022-02-07 21:50:08,477: Setting learning rate to 0.001\n",
+      "# Epoch 40: 100%|██████████| 11/11 [00:03<00:00,  3.29it/s, Loss: 0.1876]\n",
+      "2022-02-07 21:50:11,828: Epoch 40 average loss: 0.1721354682337154\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.47it/s]\n",
+      "2022-02-07 21:50:12,236: Epoch 40 average validation loss: 0.26332 -- Median validation metrics: NSE_1H: 0.65371, NSE_1D: 0.70555\n",
+      "# Epoch 41: 100%|██████████| 11/11 [00:03<00:00,  3.25it/s, Loss: 0.1846]\n",
+      "2022-02-07 21:50:15,628: Epoch 41 average loss: 0.1741275123574517\n",
+      "# Epoch 42: 100%|██████████| 11/11 [00:03<00:00,  3.00it/s, Loss: 0.1275]\n",
+      "2022-02-07 21:50:19,321: Epoch 42 average loss: 0.1714395826513117\n",
+      "# Epoch 43: 100%|██████████| 11/11 [00:03<00:00,  3.22it/s, Loss: 0.1667]\n",
+      "2022-02-07 21:50:22,753: Epoch 43 average loss: 0.15418590943921695\n",
+      "# Epoch 44: 100%|██████████| 11/11 [00:03<00:00,  3.44it/s, Loss: 0.2510]\n",
+      "2022-02-07 21:50:25,965: Epoch 44 average loss: 0.16351520202376627\n",
+      "# Epoch 45: 100%|██████████| 11/11 [00:03<00:00,  3.36it/s, Loss: 0.1200]\n",
+      "2022-02-07 21:50:29,253: Epoch 45 average loss: 0.1528610194271261\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.50it/s]\n",
+      "2022-02-07 21:50:29,656: Epoch 45 average validation loss: 0.26752 -- Median validation metrics: NSE_1H: 0.66364, NSE_1D: 0.69454\n",
+      "# Epoch 46: 100%|██████████| 11/11 [00:03<00:00,  3.44it/s, Loss: 0.1278]\n",
+      "2022-02-07 21:50:32,863: Epoch 46 average loss: 0.16621418500488455\n",
+      "# Epoch 47: 100%|██████████| 11/11 [00:03<00:00,  3.45it/s, Loss: 0.1621]\n",
+      "2022-02-07 21:50:36,065: Epoch 47 average loss: 0.15735290199518204\n",
+      "# Epoch 48: 100%|██████████| 11/11 [00:03<00:00,  3.48it/s, Loss: 0.1281]\n",
+      "2022-02-07 21:50:39,242: Epoch 48 average loss: 0.16454698822715066\n",
+      "# Epoch 49: 100%|██████████| 11/11 [00:03<00:00,  3.08it/s, Loss: 0.1877]\n",
+      "2022-02-07 21:50:42,822: Epoch 49 average loss: 0.16933431340889496\n",
+      "# Epoch 50: 100%|██████████| 11/11 [00:03<00:00,  3.43it/s, Loss: 0.1055]\n",
+      "2022-02-07 21:50:46,044: Epoch 50 average loss: 0.1589285826141184\n",
+      "# Validation: 100%|██████████| 1/1 [00:00<00:00,  2.29it/s]\n",
+      "2022-02-07 21:50:46,485: Epoch 50 average validation loss: 0.26471 -- Median validation metrics: NSE_1H: 0.66322, NSE_1D: 0.69730\n"
      ]
     }
    ],
    "source": [
-    "start_run(config_file=Path(\"1_basin.yml\"))"
+    "# by default we assume that you have at least one CUDA-capable NVIDIA GPU\n",
+    "if torch.cuda.is_available():\n",
+    "    start_run(config_file=Path(\"1_basin.yml\"))\n",
+    "\n",
+    "# fall back to CPU-only mode\n",
+    "else:\n",
+    "    start_run(config_file=Path(\"1_basin.yml\"), gpu=-1)"
    ]
   },
   {
@@ -403,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2022-01-05 22:07:30,580: No specific hidden size for frequencies are specified. Same hidden size is used for all.\n",
-      "2022-01-05 22:07:30,595: Using the model weights from runs/test_run_0501_220200/model_epoch050.pt\n",
-      "# Evaluation: 100%|██████████| 1/1 [00:01<00:00,  1.23s/it]\n"
+      "2022-02-07 21:53:55,669: No specific hidden size for frequencies are specified. Same hidden size is used for all.\n",
+      "2022-02-07 21:53:55,682: Using the model weights from runs/test_run_0702_214748/model_epoch050.pt\n",
+      "# Evaluation: 100%|██████████| 1/1 [00:01<00:00,  1.15s/it]\n"
      ]
     },
     {
@@ -421,13 +420,13 @@
        "dict_keys(['01022500'])"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "run_dir = Path(\"runs/test_run_0501_220200\")  # you'll find this path in the output of the training above.\n",
+    "run_dir = Path(\"runs/test_run_0702_214748\")  # you'll find this path in the output of the training above.\n",
     "\n",
     "# create a tester instance and start evaluation\n",
     "tester = get_tester(cfg=Config(run_dir / \"config.yml\"), run_dir=run_dir, period=\"test\", init_model=True)\n",
@@ -446,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -454,22 +453,22 @@
      "output_type": "stream",
      "text": [
       "Daily metrics:\n",
-      "  NSE: 0.798\n",
+      "  NSE: 0.790\n",
       "  MSE: 0.002\n",
-      "  RMSE: 0.048\n",
-      "  KGE: 0.770\n",
-      "  Alpha-NSE: 0.793\n",
-      "  Beta-NSE: -0.002\n",
-      "  Pearson-r: 0.900\n",
-      "  FHV: -22.331\n",
-      "  FMS: -19.091\n",
-      "  FLV: 58.134\n",
+      "  RMSE: 0.049\n",
+      "  KGE: 0.789\n",
+      "  Alpha-NSE: 0.819\n",
+      "  Beta-NSE: -0.007\n",
+      "  Pearson-r: 0.892\n",
+      "  FHV: -21.162\n",
+      "  FMS: -16.080\n",
+      "  FLV: 18.003\n",
       "  Peak-Timing: 0.625\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1152x720 with 1 Axes>"
       ]
@@ -508,12 +507,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1152x720 with 1 Axes>"
       ]
diff --git a/examples/05-Inspecting-LSTMs/inspecting-lstms.ipynb b/examples/05-Inspecting-LSTMs/inspecting-lstms.ipynb
index 4f3534807..8be98cfcf 100644
--- a/examples/05-Inspecting-LSTMs/inspecting-lstms.ipynb
+++ b/examples/05-Inspecting-LSTMs/inspecting-lstms.ipynb
@@ -8,7 +8,10 @@
    "source": [
     "# CustomLSTM: Inspecting LSTM States and Activations\n",
     "\n",
-    "Before we start: This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/05-Inspecting-LSTMs).\n",
+    "**Before we start**\n",
+    "\n",
+    "- This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/05-Inspecting-LSTMs).\n",
+    "- To be able to run this notebook locally, you need to download the publicly available CAMELS US rainfall-runoff dataset. See the [Data Prerequisites Tutorial](data-prerequisites.nblink) for a detailed description on where to download the data and how to structure your local dataset folder.\n",
     "\n",
     "This tutorial shows how to use `CustomLSTM` to inspect the states and activations of a trained LSTM.\n",
     "In previous publications, we have seen that the internals of LSTM seem to resemble physically meaningful quantities. For instance, [this publication](https://link.springer.com/chapter/10.1007/978-3-030-28954-6_19) found cells that are highly correlated to snow water equivalent (SWE), even though the LSTM had never seen SWE data during training.\n",
@@ -35,7 +38,6 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "import torch\n",
-    "from torch import nn\n",
     "from torch.utils.data import DataLoader\n",
     "\n",
     "from neuralhydrology.datasetzoo import get_dataset, camelsus\n",
@@ -50,7 +52,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To start, let's train a \"normal\" LSTM (i.e., a `CudaLSTM`), just like we did in the introduction tutorial. (Again, for quick results, we train the model on a single basin. If you actually care about good predictions, don't do this. Train one model on lots of basins combined.). The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow` and `camels_attributes_v2.0`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `1_basin.yml` config file that is located in the same directory as this notebook."
+    "To start, let's train a \"normal\" LSTM (i.e., a `CudaLSTM`), just like we did in the introduction tutorial. (Again, for quick results, we train the model on a single basin. If you actually care about good predictions, don't do this. Train one model on lots of basins combined.). \n",
+    "\n",
+    "\n",
+    "**Note**\n",
+    "\n",
+    "- The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow` and `camels_attributes_v2.0`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `1_basin.yml` config file that is located in the same directory as this notebook.\n",
+    "- By default, the config (`1_basin.yml`) assumes that you have a CUDA-capable NVIDIA GPU (see config argument `device`). In case you don't have any or you have one but want to train on the CPU, you can either change the config argument to `device: cpu` or pass `gpu=-1` to the `start_run()` function."
    ]
   },
   {
@@ -193,7 +201,13 @@
    ],
    "source": [
     "config_file = Path(\"1_basin.yml\")\n",
-    "start_run(config_file=config_file)"
+    "# by default we assume that you have at least one CUDA-capable NVIDIA GPU\n",
+    "if torch.cuda.is_available():\n",
+    "    start_run(config_file=config_file)\n",
+    "\n",
+    "# fall back to CPU-only mode\n",
+    "else:\n",
+    "    start_run(config_file=config_file, gpu=-1)"
    ]
   },
   {
diff --git a/examples/06-Finetuning/finetuning.ipynb b/examples/06-Finetuning/finetuning.ipynb
index 43d9622ad..840da9211 100644
--- a/examples/06-Finetuning/finetuning.ipynb
+++ b/examples/06-Finetuning/finetuning.ipynb
@@ -5,7 +5,11 @@
    "metadata": {},
    "source": [
     "# How-to Finetune\n",
-    "Before we start: This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/06-Finetuning).\n",
+    "\n",
+    "**Before we start**\n",
+    "\n",
+    "- This tutorial is rendered from a Jupyter notebook that is hosted on GitHub. If you want to run the code yourself, you can find the notebook and configuration files [here](https://github.com/neuralhydrology/neuralhydrology/tree/master/examples/06-Finetuning).\n",
+    "- To be able to run this notebook locally, you need to download the publicly available CAMELS US rainfall-runoff dataset and a publicly available extensions for hourly forcing and streamflow data. See the [Data Prerequisites Tutorial](data-prerequisites.nblink) for a detailed description on where to download the data and how to structure your local dataset folder. Note the special [section](data-prerequisites.nblink#CAMELS-US-catchment-attributes) with additional requirements for this tutorial.\n",
     "\n",
     "This tutorial shows how to adapt a pretrained model to a different, eventually much smaller dataset, a concept called finetuning. Finetuning is well-established in machine learning and thus nothing new. Generally speaking, the idea is to use a (very) large and diverse dataset to learn a general understanding of the underlying problem first and then, in a second step, adapt this general model to the target data. Usually, especially if the available target data is limited, pretraining plus finetuning yields (much) better results than only considering the final target data. \n",
     "\n",
@@ -15,11 +19,7 @@
     "\n",
     "**Note**: Finetuning can be a tedious task and is usually very sensitive to the learning rate as well as the number of epochs used for finetuning. One reason is that the pretrained models are usually quite large. In fact, most often they are much larger than what would be possible to train for just a single basin. So during finetuning, we have to make sure that this large capacity is not negatively impacting our model results. Common approaches are to a) only allow parts of the model to be adapted during finetuning and/or b) to train with a much lower learning rate. So far, no publication was published that presents a universally working approach for finetuning in hydrology. So be aware that the results may vary and you might need to invest some time before finding a good strategy. However, in our experience it was always possible to get better results _with_ finetuning than without.\n",
     "\n",
-    "**To summarize**: If you are interested in getting the best-performing Deep Learning model for a single basin, pretraining on a large and diverse dataset, followed by finetuning the pretrained model on your target basin is the way to go.\n",
-    "\n",
-    "## Data requirements\n",
-    "\n",
-    "This tutorial uses data from the publicly available [CAMELS US dataset](https://ral.ucar.edu/solutions/products/camels). If you want to run this tutorial yourself, make sure to download the dataset (streamflow data, meteorological forcings and attributes) from the NCAR homepage."
+    "**To summarize**: If you are interested in getting the best-performing Deep Learning model for a single basin, pretraining on a large and diverse dataset, followed by finetuning the pretrained model on your target basin is the way to go."
    ]
   },
   {
@@ -31,9 +31,8 @@
     "# Imports\n",
     "from pathlib import Path\n",
     "\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "\n",
+    "import torch\n",
     "from neuralhydrology.nh_run import start_run, eval_run, finetune"
    ]
   },
@@ -54,7 +53,9 @@
     "\n",
     "For more details, take a look at the config print-out below.\n",
     "\n",
-    "**Note**: The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow` and `camels_attributes_v2.0`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `531_basins.yml` config file that is located in the same directory as this notebook."
+    "**Note**\n",
+    "- The config file assumes that the CAMELS US dataset is stored under `data/CAMELS_US` (relative to the main directory of this repository) or a symbolic link exists at this location. Make sure that this folder contains the required subdirectories `basin_mean_forcing`, `usgs_streamflow` and `camels_attributes_v2.0`. If your data is stored at a different location and you can't or don't want to create a symbolic link, you will need to change the `data_dir` argument in the `531_basins.yml` config file that is located in the same directory as this notebook.\n",
+    "- By default, the config (`531_basins.yml`) assumes that you have a CUDA-capable NVIDIA GPU (see config argument `device`). In case you don't have any or you have one but want to train on the CPU, you can either change the config argument to `device: cpu` or pass `gpu=-1` to the `start_run()` function. Please note that training such a model on such a large dataset on CPU takes a very long time. "
    ]
   },
   {
@@ -142,8 +143,13 @@
     }
    ],
    "source": [
-    "config_file = Path(\"531_basins.yml\")\n",
-    "start_run(config_file=config_file)"
+    "# by default we assume that you have at least one CUDA-capable NVIDIA GPU\n",
+    "if torch.cuda.is_available():\n",
+    "    start_run(config_file=Path(\"531_basins.yml\"))\n",
+    "\n",
+    "# fall back to CPU-only mode\n",
+    "else:\n",
+    "    start_run(config_file=Path(\"531_basins.yml\"), gpu=-1)"
    ]
   },
   {
diff --git a/neuralhydrology/__about__.py b/neuralhydrology/__about__.py
index c68196d1c..a955fdae1 100644
--- a/neuralhydrology/__about__.py
+++ b/neuralhydrology/__about__.py
@@ -1 +1 @@
-__version__ = "1.2.0"
+__version__ = "1.2.1"
diff --git a/neuralhydrology/datasetzoo/hourlycamelsus.py b/neuralhydrology/datasetzoo/hourlycamelsus.py
index cd45c6edb..40ec3b649 100644
--- a/neuralhydrology/datasetzoo/hourlycamelsus.py
+++ b/neuralhydrology/datasetzoo/hourlycamelsus.py
@@ -212,8 +212,16 @@ def load_hourly_us_discharge(data_dir: Path, basin: str) -> pd.DataFrame:
     pd.Series
         Time-index Series of the discharge values (mm/hour)
     """
+    pattern = '**/*usgs-hourly.csv'
     discharge_path = data_dir / 'hourly' / 'usgs_streamflow'
-    files = list(discharge_path.glob('**/*usgs-hourly.csv'))
+    files = list(discharge_path.glob(pattern))
+    
+    # https://github.com/neuralhydrology/neuralhydrology/issues/67 streamflow folder names are different in code
+    # vs. on Zenodo. We allow both ("-", "_") to not break any existing data directories.
+    if len(files) == 0:
+        discharge_path = discharge_path.parent / 'usgs-streamflow'
+        files = list(discharge_path.glob(pattern))
+    
     file_path = [f for f in files if basin in f.stem]
     if file_path:
         file_path = file_path[0]
diff --git a/neuralhydrology/datautils/pet.py b/neuralhydrology/datautils/pet.py
index 3a7210581..3e235e9a9 100644
--- a/neuralhydrology/datautils/pet.py
+++ b/neuralhydrology/datautils/pet.py
@@ -7,7 +7,7 @@ def get_priestley_taylor_pet(t_min: np.ndarray, t_max: np.ndarray, s_rad: np.nda
                              doy: np.ndarray) -> np.ndarray:
     """Calculate potential evapotranspiration (PET) as an approximation following the Priestley-Taylor equation.
 
-    The ground head flux G is assumed to be 0 at daily time steps (see Newman et al., 2015 [#]_). The 
+    The ground heat flux G is assumed to be 0 at daily time steps (see Newman et al., 2015 [#]_). The 
     equations follow FAO-56 (Allen et al., 1998 [#]_).
 
     Parameters
diff --git a/neuralhydrology/utils/logging_utils.py b/neuralhydrology/utils/logging_utils.py
index 723c8e95a..66deee3b0 100644
--- a/neuralhydrology/utils/logging_utils.py
+++ b/neuralhydrology/utils/logging_utils.py
@@ -42,7 +42,7 @@ def get_git_hash() -> Optional[str]:
     try:
         if subprocess.call(["git", "-C", current_dir, "branch"], stderr=subprocess.DEVNULL,
                            stdout=subprocess.DEVNULL) == 0:
-            return subprocess.check_output(["git", "-C", current_dir, "describe", "--always"])
+            return subprocess.check_output(["git", "-C", current_dir, "describe", "--always"]).strip().decode('ascii')
     except OSError:
         return None  # likely, git is not installed.