Update installation and first run docs

MANIFEST.in

@@ -0,0 +1,3 @@
+recursive-include ganslate *.sh
+recursive-include ganslate/utils/cli/cookiecutter_templates *
+recursive-exclude ganslate/utils/cli/cookiecutter_templates __pycache__/*

docs/installation.md

@@ -7,28 +7,18 @@ You can install `ganslate` either through a docker setup or directly on your sys
 Dockerized setup is the easiest way to get started with the framework. If you do not have docker installed, you can follow instructions [here](https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository)
 
 
-First, you can build the docker image by running the following commands at the repository root level
+You can run the docker image, which will give you access to a container with all dependencies installed, using,
 
 ```console
-cd docker/
-docker build -t ganslate:latest .
+docker run --gpus all -it surajpaib/ganslate:latest
 ```
 
-Once the docker image is built, it can be run using,
+This will drop down to a shell and [you can now check out the quickstart page](quickstart.md)
 
-```console
-docker run --gpus all -it \
-	--shm-size=24gb --volume=<data_dir>:/data --volume=<repo_dir>:/ganslate \
-	--name=ganslate ganslate:latest /bin/bash
-```
 
-The docker container [mounts volumes from the host system](https://docs.docker.com/storage/volumes/) to allow easier persistence of data. 
 
-`<data_dir>` must be replaced with the full path of a directory where your data is located.
-`<repo_dir>` must be replaced with the path to the `ganslate` repository was cloned. 
-
-!!! note
-	`<data_dir>` can initially point to an empty directory to simplify setup. Data can be moved into this directory later as docker mounts the directory to the container. 
+!!! note 
+	To get your data into the docker container, you can use volume mounts. The docker container [mounts volumes from the host system](https://docs.docker.com/storage/volumes/) to allow easier persistence of data. This can be done as `docker run --gpus all --volume=<data_dir>:/data -it ganslate:latest`. `<data_dir>` must be replaced with the full path of a directory where your data is located, this will then be mounted on the `/data` path within the docker
 
 ## Local
 

docs/tutorials/horse2zebra.md

@@ -1,24 +1,19 @@
 # Your First Run (horse2zebra)
 
-For both types of install, running the basic horse2zebra example is similar.
-#### Data download
-The horse2zebra dataset can be downloaded using instructions below,
-1. Open a terminal inside the cloned repository and run, 
+For both types of install, running the basic horse2zebra example is the same.
 
+Once the installation is complete and you can access the CLI as show in the [quickstart](../quickstart.md), run
 ```console
-cd projects/horse2zebra
-bash download_cyclegan_dataset.sh horse2zebra .
+ganslate your-first-run
 ```
-
-!!! note
-    The horse2zebra dataset will be downloaded at the root-level of the ganslate directory. This can be changed by providing a <data_dir> to the command `bash download_cyclegan_dataset.sh horse2zebra <data_dir>. However, the yaml files need to be manually changed. For non-advanced users, it is best to stick to the default location
+On running this, a few options will show up that can be customized. You may also leave it at its default values. Once the prompts
+are completed, you will have a folder generated with a demo `horse2zebra` project in the path you specified. 
 
 ### Training
 Next, you can run the training using the command below,
 
 ```console
-cd /code
-python tools/train.py config=projects/horse2zebra/experiments/default.yaml
+ganslate train config=<path_specified>/default.yaml
 ```
 
 !!! note

ganslate/utils/cli/cookiecutter_templates/your_first_run/cookiecutter.json

@@ -0,0 +1,10 @@
+{
+    "project_name": "horse2zebra",
+    "number_of_iterations": 117700,
+    "batch_size": 1,
+    "logging_frequency": 500,
+    "checkpointing_frequency": 2000,
+    "generator_model": ["Resnet2D", "Unet2D"],
+    "cycle_consistency_ssim_percentage": 0.84,
+    "path": "."
+}

ganslate/utils/cli/cookiecutter_templates/your_first_run/{{ cookiecutter.project_name }}/default.yaml

@@ -0,0 +1,66 @@
+project_dir: null
+
+train:
+    output_dir: "./checkpoints/horse2zebra_default"
+    cuda: True
+    n_iters:  {{ cookiecutter.number_of_iterations }} 
+    n_iters_decay: {{ cookiecutter.number_of_iterations }} 
+    batch_size:  {{ cookiecutter.batch_size }}
+    mixed_precision: True
+
+    logging:
+        freq: {{ cookiecutter.logging_frequency }}
+        wandb:
+            project: "horse2zebra"
+
+    checkpointing:
+        freq: {{ cookiecutter.checkpointing_frequency }} 
+
+    dataset: 
+        name: "ImageDataset"
+        root: "{{ cookiecutter.path }}/{{ cookiecutter.project_name }}/datasets/horse2zebra/train/"
+        num_workers: 16
+        image_channels: 3
+        preprocess: "resize"
+        load_size: 256
+        flip: True
+
+    gan:  
+        name: "CycleGAN"
+
+        generator:  
+            name: {{ cookiecutter.generator_model }}
+            n_residual_blocks: 9
+            in_out_channels:
+                AB: [3, 3]
+
+        discriminator:  
+            name: "PatchGAN2D"
+            n_layers: 3
+            in_channels:
+                B: 3
+
+        optimizer:
+            lambda_AB: 10.0
+            lambda_BA: 10.0
+            lambda_identity: 0
+            proportion_ssim: {{ cookiecutter.cycle_consistency_ssim_percentage }}
+            lr_D: 0.0002
+            lr_G: 0.0004
+
+    metrics:
+        discriminator_evolution: True
+        ssim: True
+
+infer:
+    checkpointing:
+        load_iter: 1
+    dataset:
+        name: "ImageDataset"
+        root: "{{ cookiecutter.path }}/{{ cookiecutter.project_name }}/datasets/horse2zebra/test/"
+        num_workers: 16
+        image_channels: 3
+        preprocess: "resize"
+        load_size: 256
+        flip: False
+

ganslate/utils/cli/interface.py

@@ -42,6 +42,15 @@ def new_project(path):
     template = str(COOKIECUTTER_TEMPLATES_DIR / "new_project")
     cookiecutter(template, output_dir=path)
 
+# First run
+@interface.command(help="Fetch resources for the horse2zebra first run")
+@click.argument("path", default="./")
+def your_first_run(path):
+    template = str(COOKIECUTTER_TEMPLATES_DIR / "your_first_run")
+    project_path = cookiecutter(template, output_dir=path)
+    download_script_path = "ganslate/utils/cli/scripts/download_cyclegan_datasets.sh"
+    subprocess.call(["bash", download_script_path, "horse2zebra", project_path])
+
 # Download project
 @interface.command(help="Download a project.")
 @click.argument("name")
@@ -54,7 +63,7 @@ def download_project(name, path):
 @click.argument("name")
 @click.argument("path")
 def download_dataset(name, path):
-    download_script_path = "ganslate/utils/scripts/download_cyclegan_datasets.sh"
+    download_script_path = "ganslate/utils/cli/scripts/download_cyclegan_datasets.sh"
     subprocess.call(["bash", download_script_path, name, path])
 
 # Install Nvidia Apex

setup.cfg

@@ -5,16 +5,16 @@ author = "ganslate team"
 # author-email = 
 url = https://github.com/Maastro-CDS-Imaging-Group/ganslate
 description = GAN image-to-image translation framework made simple and extensible.
-long-description = file: README.md
-long-description-content-type = text/markdown
+long_description = file: README.md
+long_description_content_type = text/markdown
 license = mit
 platforms = any
 keywords = one, two
 classifiers =
     Development Status :: 3 - Alpha
     Programming Language :: Python :: 3 :: Only
     Intended Audience :: Science/Research
-project-urls =
+project_urls =
     Documentation = https://ganslate.netlify.app/  # TODO: Update
 
 [options]