Change environment name from tf2 to homl3

INSTALL.md

@@ -28,18 +28,18 @@ Once Anaconda (or Miniconda) is installed, run the following command to update t
 ## Install the GPU Driver and Libraries
 If you have a TensorFlow-compatible GPU card (NVidia card with Compute Capability ≥ 3.5), and you want TensorFlow to use it, then you should download the latest driver for your card from [nvidia.com](https://www.nvidia.com/Download/index.aspx?lang=en-us) and install it. You will also need NVidia's CUDA and cuDNN libraries, but the good news is that they will be installed automatically when you install the tensorflow-gpu package from Anaconda. However, if you don't use Anaconda, you will have to install them manually. If you hit any roadblock, see TensorFlow's [GPU installation instructions](https://tensorflow.org/install/gpu) for more details.
 
-## Create the `tf2` Environment
-Next, make sure you're in the `handson-ml3` directory and run the following command. It will create a new `conda` environment containing every library you will need to run all the notebooks (by default, the environment will be named `tf2`, but you can choose another name using the `-n` option):
+## Create the `homl3` Environment
+Next, make sure you're in the `handson-ml3` directory and run the following command. It will create a new `conda` environment containing every library you will need to run all the notebooks (by default, the environment will be named `homl3`, but you can choose another name using the `-n` option):
 
     $ conda env create -f environment.yml
 
 Next, activate the new environment:
 
-    $ conda activate tf2
+    $ conda activate homl3
 
 
 ## Start Jupyter
-You're almost there! You just need to register the `tf2` conda environment to Jupyter. The notebooks in this project will default to the environment named `python3`, so it's best to register this environment using the name `python3` (if you prefer to use another name, you will have to select it in the "Kernel > Change kernel..." menu in Jupyter every time you open a notebook):
+You're almost there! You just need to register the `homl3` conda environment to Jupyter. The notebooks in this project will default to the environment named `python3`, so it's best to register this environment using the name `python3` (if you prefer to use another name, you will have to select it in the "Kernel > Change kernel..." menu in Jupyter every time you open a notebook):
 
     $ python3 -m ipykernel install --user --name=python3
 
@@ -55,7 +55,7 @@ When you're done with Jupyter, you can close it by typing Ctrl-C in the Terminal
 
     $ cd $HOME # or whatever development directory you chose earlier
     $ cd handson-ml3
-    $ conda activate tf2
+    $ conda activate homl3
     $ jupyter notebook
 
 ## Update This Project and its Libraries
@@ -79,10 +79,10 @@ Next, let's update the libraries. First, let's update `conda` itself:
 
     $ conda update -c defaults -n base conda
 
-Then we'll delete this project's `tf2` environment:
+Then we'll delete this project's `homl3` environment:
 
     $ conda activate base
-    $ conda env remove -n tf2
+    $ conda env remove -n homl3
 
 And recreate the environment:
 
@@ -90,5 +90,5 @@ And recreate the environment:
 
 Lastly, we reactivate the environment and start Jupyter:
 
-    $ conda activate tf2
+    $ conda activate homl3
     $ jupyter notebook

README.md

@@ -44,7 +44,7 @@ Next, clone this project by opening a terminal and typing the following commands
 Next, run the following commands:
 
     $ conda env create -f environment.yml
-    $ conda activate tf2
+    $ conda activate homl3
     $ python -m ipykernel install --user --name=python3
 
 Finally, start Jupyter:

docker/Dockerfile

@@ -43,7 +43,7 @@ RUN chown ${username}:${username} ${workdir}
 USER ${username}
 WORKDIR ${workdir}
 
-ENV PATH /opt/conda/envs/tf2/bin:$PATH
+ENV PATH /opt/conda/envs/homl3/bin:$PATH
 
 # The config below enables diffing notebooks with nbdiff (and nbdiff support
 # in git diff command) after connecting to the container by "make exec" (or

docker/Dockerfile.gpu

@@ -94,7 +94,7 @@ RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/lib
 #################################################
 
 ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
-ENV PATH /opt/conda/bin:/opt/conda/envs/tf2/bin:$PATH
+ENV PATH /opt/conda/bin:/opt/conda/envs/homl3/bin:$PATH
 
 # Next we need to install miniconda
 

docker/README.md

@@ -133,7 +133,7 @@ If you are using Docker 19.03 or above, you can run:
 
 ```bash
 $ cd /path/to/project/handson-ml3
-$ docker run --name handson-ml3 --gpus all -p 8888:8888 -p 6006:6006 --log-opt mode=non-blocking --log-opt max-buffer-size=50m -v `pwd`:/home/devel/handson-ml3 ageron/handson-ml3:latest-gpu /opt/conda/envs/tf2/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
+$ docker run --name handson-ml3 --gpus all -p 8888:8888 -p 6006:6006 --log-opt mode=non-blocking --log-opt max-buffer-size=50m -v `pwd`:/home/devel/handson-ml3 ageron/handson-ml3:latest-gpu /opt/conda/envs/homl3/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
 ```
 
 If you are using an older version of Docker, then replace `--gpus all` with `--runtime=nvidia`.

docker/docker-compose.yml

@@ -19,7 +19,7 @@ services:
       - "6006:6006"
     volumes:
       - ../:/home/devel/handson-ml3
-    command: /opt/conda/envs/tf2/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
+    command: /opt/conda/envs/homl3/bin/jupyter notebook --ip='0.0.0.0' --port=8888 --no-browser
     #deploy:
     #  resources:
     #    reservations: