Minimal updates for scipy

Author: Martin Durant

05-distributed-dataframes-and-efficiency.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "# Personal Dask Cluster\n",
     "\n",
-    "Go to http://35.192.17.108/hub/login for your own Dask cluster. Use your first and last name provided to PyCon for the username and \"dask\" for the password.\n",
+    "Go to http://<TBD>/hub/login for your own Dask cluster. Use your first and last name provided to SciPy for the username and \"dask\" for the password.\n",
     "(We don't actually do any authentication).\n",
     "\n",
     "# Distributed DataFrames and Efficiency\n",
@@ -31,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pycon_utils import make_cluster\n",
+    "from scipy_utils import make_cluster\n",
     "from dask.distributed import Client"
    ]
   },

06-distributed-advanced.ipynb

@@ -25,7 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pycon_utils import make_cluster\n",
+    "from scipy_utils import make_cluster\n",
     "from dask.distributed import Client"
    ]
   },

07-machine-learning.ipynb

@@ -250,7 +250,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pycon_utils import make_cluster\n",
+    "from scipy_utils import make_cluster\n",
     "import dask_ml.joblib\n",
     "from dask.distributed import Client\n",
     "\n",

README.md

@@ -1,11 +1,12 @@
 # Parallel Data Analysis with Dask
 
-Materials for the [Dask tutorial at PyCon 2018](https://us.pycon.org/2018/schedule/presentation/47/).
+Materials for the [Dask tutorial at SciPy 2018](https://scipy2018.scipy.org/ehome/index.php?eventid=299527&tabid=711308&cid=2229599&sessionid=21547348&sessionchoice=1&).
 
 ## First Time Setup
 
-If you don't have `git` installed, you can download a ZIP copy of the repository using the green button above.
-Note that the file will be called `dask-tutorial-pycon-2018-master`, instead of `dask-tutorial-pycon-2018`.
+If you don't have `git` installed, you can download a ZIP copy of the repository using the green button 
+("Clone or Download"->"Download ZIP") above.
+In this case the file will be called `dask-tutorial-scipy-2018-master`, instead of `dask-tutorial-scipy-2018`.
 Adjust the commands below accordingly.
 
 
@@ -15,17 +16,17 @@ Adjust the commands below accordingly.
 # Update conda
 conda update conda
 
-# Clone the repository. Or download the ZIP and add `-master` to the name.
-git clone https://github.com/TomAugspurger/dask-tutorial-pycon-2018
+# Clone the repository, or download the ZIP and decompress
+git clone https://github.com/martindurant/dask-tutorial-scipy-2018
 
 # Enter the repository
-cd dask-tutorial-pycon-2018
+cd dask-tutorial-scipy-2018
 
 # Create the environment
 conda env create
 
 # Activate the environment
-conda activate dask-pycon
+conda activate dask-scipy
 
 # Download data
 python prep_data.py
@@ -37,11 +38,11 @@ jupyter lab
 If you aren't using conda
 
 ```
-# Clone the repository. Or download the ZIP and add `-master` to the name.
-git clone https://github.com/TomAugspurger/dask-tutorial-pycon-2018
+# Clone the repository, or download the ZIP and decompress
+git clone https://github.com/martindurant/dask-tutorial-scipy-2018
 
 # Enter the repository
-cd dsak-tutorial-pycon-2018
+cd dsak-tutorial-scipy-2018
 
 # Create a virtualenv
 python3 -m venv .env
@@ -63,5 +64,6 @@ jupyter lab
 
 ## Connect to the Cluster
 
-We have a [pangeo](https://github.com/pangeo-data/pangeo) deployment running that'll provide everyone with their own cluster to try out Dask on some larger problems.
-You can log into the cluster by going to:
+We have a [pangeo](https://github.com/pangeo-data/pangeo) deployment running that'll provide everyone with their own 
+cluster to try out Dask on some larger problems.
+You can log into the cluster by going to: TBD

environment.yml

@@ -1,4 +1,4 @@
-name: dask-pycon
+name: dask-scipy
 dependencies:
  - dask
  - dask-ml