Transition to MLWorkspace docker and setup makefile with environment commands

.gitignore

@@ -0,0 +1,5 @@
+.vscode/
+.DS_Store
+.idea/
+.ipynb_checkpoints/
+.workspace/

Dockerfile

@@ -1,9 +0,0 @@
-FROM pytorch/pytorch
-
-RUN apt-get update && apt-get install -y software-properties-common && apt-get update
-RUN add-apt-repository -y ppa:git-core/ppa && apt-get update && apt-get install -y git libglib2.0-dev
-
-COPY requirements.txt ./
-RUN pip install -r requirements.txt
-
-RUN pip install jupyterlab

Makefile

@@ -0,0 +1,33 @@
+#################################################################################
+# GLOBALS                                                                       #
+#################################################################################
+
+PROJECT_DIR := $(shell dirname $(realpath $(lastword $(MAKEFILE_LIST))))
+PROJECT_NAME = savta_depth
+PYTHON_INTERPRETER = python3
+
+ifeq (,$(shell which conda))
+HAS_CONDA=False
+else
+HAS_CONDA=True
+endif
+
+#################################################################################
+# COMMANDS                                                                      #
+#################################################################################
+
+env:
+ifeq (True,$(HAS_CONDA))
+	@echo ">>> Detected conda, creating conda environment."
+	conda create --name $(PROJECT_NAME) python=3
+	@echo ">>> New conda env created. Activate with:\nconda activate $(PROJECT_NAME)"
+else
+	@echo ">>> No conda detected, creating venv environment."
+	$(PYTHON_INTERPRETER) -m venv env
+	@echo ">>> New virtual env created. Activate with:\nsource env/bin/activate ."
+endif
+
+requirements:
+	@echo ">>> Installing requirements. Make sure your virtual environment is activated."
+	$(PYTHON_INTERPRETER) -m pip install -U pip setuptools wheel
+	$(PYTHON_INTERPRETER) -m pip install -r requirements.txt

README.md

@@ -14,20 +14,40 @@ If you'd like to take part, please follow the guide.
 * Next, clone the repository you just forked by typing the following command in your terminal:
   ```bash
   $ git clone https://dagshub.com/<your-dagshub-username>/SavtaDepth.git
-  $ dvc checkout #use this to get the data, models etc
   ```
-* To get your environment up and running docker is the best way to go.
-  We created a dockerfile that has all you need in it and will install all requirements in the 'requirements.txt' file as well as run a jupyter lab instance.
-    * Just open the terminal in your project directory and type `docker build "savta_depth_dev" ."
-    * After the docker image is created run the following commands:
+* To get your environment up and running docker is the best way to go. We use an instance of [MLWorkspace](https://github.com/ml-tooling/ml-workspace). 
+    * You can Just run the following commands to get it started.
+
+        ```bash
+        $ chmod +x run_dev_env.sh
+        $ ./run_dev_env.sh
+        ```
+
+    * Open localhost:8080 to see the workspace you have created. You will be asked for a token – enter `dagshub_savta`
+    * In the top right you have a menu called `Open Tool`. Click that button and choose terminal (alternatively open VSCode and open terminal there) and type in the following commands to install a virtualenv and dependencies:
+
+        ```bash
+        $ make env
+        $ conda activate savta_depth
+        $ make requirements
+        ```
+* Pull the dvc files to your workspace by typing:
+
     ```bash
-    $ chmod +x run_dev_env.sh
-    $ ./run_dev_env.sh
+    $ dvc checkout #use this to get the data, models etc
     ```
-    * Open localhost:8888 and you are good to go
-* After you are finished your modification, don't forget to push your code to DAGsHub, and your dvc managed files to your dvc remote. In order to setup a dvc remote please refer to [this guide](https://dagshub.com/docs/getting-started/set-up-remote-storage-for-data-and-models/).
-* Create a Pull Request on DAGsHub!
-* 🐶
+
+* After you are finished your modification, make sure to do the following:
+    * Freeze your virtualenv by typing in the terminal:
+
+        ```bash
+        pip freeze > requirements.txt
+        ```
+
+    * Push your code to DAGsHub, and your dvc managed files to your dvc remote. In order to setup a dvc remote please refer to [this guide](https://dagshub.com/docs/getting-started/set-up-remote-storage-for-data-and-models/).
+    * Create a Pull Request on DAGsHub!
+    * 🐶
+
 ### TODO:
 - [ ] Web UI
 - [ ] Testing various datasets as basis for training

requirements.txt

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+certifi==2020.6.20

run_dev_env.sh

@@ -1,10 +1,7 @@
-docker run --rm -p 8888:8888 \
-  --ipc=host \
-  --volume="$PWD:/workspace" \
-  savta_depth_dev jupyter lab \
-    --ip=0.0.0.0 \
-    --port=8888 \
-    --allow-root \
-    --no-browser \
-    --NotebookApp.token='' \
-    --NotebookApp.password=''
+docker run -d \
+    -p 8080:8080 \
+    --name "ml-workspace" -v "${PWD}:/workspace" \
+    --env AUTHENTICATE_VIA_JUPYTER="dagshub_savta" \
+    --shm-size 512m \
+    --restart always \
+    mltooling/ml-workspace:latest

src/code/make_dataset.py

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+print('hello world')