Differences

This shows you the differences between two versions of the page.

--- how_do_i [2022/02/14 22:00] – [Transfer Files To/From the Cluster] updated scp method deadline
+++ how_do_i [2024/03/11 00:02] (current) – [Request Library or Application] deadline
@@ Line 3: / Line 3: @@
 This page include answers to many of the commonly asked questions about the cluster. Please send any questions you may have to ''deadline@basement-supercomputing.com'' For a faster response use DSL in the subject line.
+==== Request Library or Application ====
+As a valid user with a login account, you may request libraries (i.e. Python libraries) or an application to be installed on the DSL cluster. Keep in mind not all libraries/applications can be installed due to compatibility issues.
+<form>
+Action mail deadline@eadline.org,sjagannathan@saintpeters.edu
+Thanks "Thanks for your submission."
+Fieldset "Need Help with Library or Applications?"
+Textbox  "Name (first/ last)" "=Your Name"
+Textbox "Your Login"  "=Login"
+email "Your E-Mail Address" "=Email Address"
+textarea "Ask General Questions or Request Library/Application do you need?"
+static "**Please do not submit bugs or operational issues here**"
+submit "Submit Query"
+</form>
 ==== Transfer Files To/From the Cluster ====
 (14-Feb-2022)
@@ Line 12: / Line 30: @@
 ===Transfer From a Cloud Account===
-A personal cloud account(e.g. Google drive or Microsoft OneDrive) can be accessed from your cluster account using the ''rclone'' command. This command will allow transfer to and from the cloud into your local cluster account. The configuration and use is done in text mode using a terminal window from any the DSL lab
+A personal cloud account(e.g. Google drive or Microsoft OneDrive) can be accessed from your cluster account using the ''rclone'' command. This command will allow transfer to and from the cloud into your local cluster account. The configuration and use is done in text mode using a terminal window from any the DSL Lab workstations.
-workstation.
 See the [[Using rclone|Using rclone]] page for more information.
@@ Line 46: / Line 63: @@
 ==== Using Python ====
--Feb-2022
+-Mar-2022
 There are two versions of Python installed on the cluster.
@@ Line 86: / Line 103: @@
 </code>
 Contact the system administrator for options.
+=== Run Anaconda Navigator on Your Laptop ===
+You will need to be on the campus wireless network for this procedure to work. General internet access is not available. The following procedure is run from the command line.
+. Log into your account using on the cluster using [[http://mobaxterm.mobatek.net|MobaXterm]] (see above)
+  $ ssh dsl.saintpeters.edu
+. Once logged in, run the following:
+  $ jupyter-notebook --generate-config
+. Edit the config file (use ''vi'' or ''nano''):
+  $ nano .jupyter/jupyter_notebook_config.py
+. Change this line:
+   #c.NotebookApp.ip = 'localhost'
+. To the following (note remove "#") save and exit:
+   c.NotebookApp.ip = '127.0.0.1'
+. Now run from MobaXterm command line:
+  $ anaconda-navigator
 === Python Libraries ===
@@ Line 140: / Line 186: @@
 {{ :wiki:zeppelin-1-dsl.png?600 |}}
-If you have a functional account, you can login (upper right corner) and explore the Zeppelin Notebook. Once you are logged in, your browser should look like the image below. Click on the //Basic Tests (Python, PySpark, sh, and Hive)// notebook, clone a copy and you can try the features.
+If you have a functional account, you can login (upper right corner) and explore the Zeppelin Notebook. Once you are logged in, your browser should look similar to the image below (the listed notebooks will be different).
+Click on the **//Basic Tests (Python, PySpark, sh, and Hive)//** notebook, clone a copy of the notebook so that you can change it and you can try the paragraphs that illustrate the Zeppelin features.
 {{ :wiki:zeppelin-2-dsl.png?600 |}}
@@ Line 181: / Line 229: @@
 ==== Use TensorFlow ====
-TBC
+Tensorflow is available as part of Anaconda Python. The easiest (and recommended) way to use Tensorflow with Python through the high level [[https://keras.io/guides/|Keras]] library.
+It is also possible to use [[https://pythonprogramming.net/tensorflow-introduction-machine-learning-tutorial/|low level Tensorflow]] directly.
+**Note:** Python Keras and Tensorflow can be used on any workstation, Only three workstations have GPUs (limulus7-tf, limulus8-tf, limulus9-tf), however both Keras and Tensorflow will run without using a GPU (applications will run slower).