Getting started with Deep Learning - Revision history

Bgeetika at 14:26, 3 December 2020

2020-12-03T14:26:46Z

Bgeetika at 15:30, 2 December 2020

2020-12-02T15:30:00Z

Bgeetika at 15:27, 2 December 2020

2020-12-02T15:27:45Z

Jnewport: /* Note */

2020-07-07T18:47:47Z

‎Note

Jnewport: /* Note */

2020-07-07T18:47:25Z

‎Note

Cwhidden: Update with information about IBM WMLA conda channel

2020-06-17T19:25:35Z

Update with information about IBM WMLA conda channel

Cwhidden: /* ssh command port forwarding */

2020-04-28T18:29:08Z

‎ssh command port forwarding

Cwhidden: /* 1. Get started with DeepSense */

2019-12-05T19:57:43Z

‎1. Get started with DeepSense

Cwhidden: /* PuTTY port forwarding on Windows */

2019-12-03T18:08:24Z

‎PuTTY port forwarding on Windows

Cwhidden at 16:46, 3 December 2019

2019-12-03T16:46:20Z

@@ Line 23: / Line 23: @@
 <code>/opt/DL/caffe/bin/caffe-install-samples</code>
-<!-- TODO: still need to set up queues to fairly share GPUs -->
+== 3. More information ==
 Go to Caffe's [http://caffe.berkeleyvision.org/ website] for tutorials and example programs that you can run to get started.
@@ Line 102: / Line 33: @@
 [http://caffe.berkeleyvision.org/gathered/examples/cifar10.html CIFAR-10 tutorial] - Train a convolutional neural network to classify small images.
-== 9. Using another deep learning toolkit such as Tensorflow ==
+== 4. Using another deep learning toolkit such as Tensorflow ==
@@ Line 126: / Line 57: @@
    https://github.com/aymericdamien/TensorFlow-Examples
 </div> <!-- autonum -->

← Older revision		Revision as of 15:30, 2 December 2020
Line 1:		Line 1:
	<div class="noautonum">		<div class="noautonum">

−	~~== Note ==~~
−	On June 26 we updated the GPU compute nodes to a new version of IBM Watson Machine Learning Accelerator. This changes the way you access deep learning packages like Tensorflow and Pytorch. Instead of "activating" these packages, you will be able to install new versions directly in your anaconda environment.
−
−	We are actively updating the wiki documentation to explain the new method of accessing deep learning packages. Please bear with us during these updates as some documentation may still refer to the old method of "activating" deep learning packages

	== 1. Get started with DeepSense ==		== 1. Get started with DeepSense ==

@@ Line 8: / Line 8: @@
 == 1. Get started with DeepSense ==
-Follow all the steps from [[Getting started]]. This tutorial assumes you can log on to the DeepSense compute platform and have a version of Anaconda python on your path. We recommend installing Anaconda in your home directory before starting this tutorial (See [[Installing local software]]).
+Follow all the steps from [[Getting started]]. This tutorial assumes you can log on to the DeepSense compute platform and have a version of Anaconda python on your path. We recommend installing Anaconda in your home directory before starting this tutorial (See [[Installing Software]]).
 == 2. Prepare Caffe and download Caffe samples to your home directory ==
@@ Line 14: / Line 14: @@
 (New method)
-Activate your anaconda environment. See [[Installing local software]] for how to create an environment. We will assume you have created one called "caffe".
+Activate your anaconda environment. See [[Installing Software]] for how to create an environment. We will assume you have created one called "caffe".
   conda activate caffe
 Add the IBM-AI anaconda channel if you have not done so already

@@ Line 4: / Line 4: @@
 On June 26 we updated the GPU compute nodes to a new version of IBM Watson Machine Learning Accelerator. This changes the way you access deep learning packages like Tensorflow and Pytorch. Instead of "activating" these packages, you will be able to install new versions directly in your anaconda environment.
-We are actively updating the wiki documentation to explain the new method of accessing deep learning packages (see [[Getting started with Deep Learning]]). Please bear with us during these updates as some documentation may still refer to the old method of "activating" deep learning packages
+We are actively updating the wiki documentation to explain the new method of accessing deep learning packages. Please bear with us during these updates as some documentation may still refer to the old method of "activating" deep learning packages
 == 1. Get started with DeepSense ==

@@ Line 2: / Line 2: @@
 == Note ==
-On June 26 we will update the GPU compute nodes to a new version of IBM Watson Machine Learning Accelerator. This will change the way you access deep learning packages like Tensorflow and Pytorch. Instead of "activating" these packages, you will be able to install new versions directly in your anaconda environment.
+On June 26 we updated the GPU compute nodes to a new version of IBM Watson Machine Learning Accelerator. This changes the way you access deep learning packages like Tensorflow and Pytorch. Instead of "activating" these packages, you will be able to install new versions directly in your anaconda environment.
-We are actively updating the wiki documentation to explain the new method of accessing deep learning packages. Please bear with us during these updates as some documentation may still refer to the old method of "activating" deep learning packages
+We are actively updating the wiki documentation to explain the new method of accessing deep learning packages (see [[Getting started with Deep Learning]]). Please bear with us during these updates as some documentation may still refer to the old method of "activating" deep learning packages
 == 1. Get started with DeepSense ==

@@ Line 1: / Line 1: @@
 <div class="noautonum">
 == 1. Get started with DeepSense ==
@@ Line 5: / Line 10: @@
 Follow all the steps from [[Getting started]]. This tutorial assumes you can log on to the DeepSense compute platform and have a version of Anaconda python on your path. We recommend installing Anaconda in your home directory before starting this tutorial (See [[Installing local software]]).
-== 2. Download Caffe samples to your home directory ==
+== 2. Prepare Caffe and download Caffe samples to your home directory ==
 <code>/opt/DL/caffe/bin/caffe-install-samples</code>
@@ Line 17: / Line 35: @@
 == 4. Start a python2 Jupyter notebook ==
-=== Source the Caffe deep learning toolkit ===
+=== (Old method only) Source the Caffe deep learning toolkit ===
 <code>source /opt/DL/caffe/bin/caffe-activate</code>
@@ Line 90: / Line 108: @@
 == 9. Using another deep learning toolkit such as Tensorflow ==
 * Ensure any Anaconda dependencies are installed
 ** for tensorflow, run <code>/opt/DL/tensorflow/bin/install_dependencies</code>

← Older revision		Revision as of 18:29, 28 April 2020
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	for example, <code>ssh -l user1 login1.deepsense.ca -L 8888:ds-cmgpu-04:8888</code>		for example, <code>ssh -l user1 login1.deepsense.ca -L 8888:ds-cmgpu-04:8888</code>
		+
		+	Note that you may need to use a different <local_port> than 8888 if you have other web services running on your local computer. In particular, if you run a jupyter notebook locally then it will use port 8888 and you will try to connect to the local jupyter notebook instead of the cluster notebook. In this case close your port forwarding and try again with 8889 or another unused port.

	=== PuTTY port forwarding on Windows ===		=== PuTTY port forwarding on Windows ===

← Older revision		Revision as of 18:08, 3 December 2019
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	Enter <code><remote_host>:<remote_port></code> in the Destination field. For example, <code>ds-cmgpu-04:8888</code>.		Enter <code><remote_host>:<remote_port></code> in the Destination field. For example, <code>ds-cmgpu-04:8888</code>.
		+
		+	Press the <code>Add</code> button to add the port forwarding rule to your PuTTY session.

	Finally, open the session as usual.		Finally, open the session as usual.

@@ Line 42: / Line 42: @@
 == 5. Port Forwarding ==
-In a separate terminal window from your local computer, forward your local port to the remote host:
+In a separate terminal window from your local computer, forward your local port to the remote host.
-<code> ssh -l <username> login1.deepsense.ca -L <port>:<remote_host>:<port></code>
+=== ssh command port forwarding ===
 for example, <code>ssh -l user1 login1.deepsense.ca -L 8888:ds-cmgpu-04:8888</code>
 Enter the copied URL in your web browser but change the remote host name to “localhost” before pressing enter.
@@ Line 55: / Line 70: @@
 '''Note''': On our macs, this worked in Chrome, but not in Safari.  Unfortunately, there was no error reported, it simply could not connect.
 Be sure to enter the location of the “caffe-samples” directory in your home directory as your caffe-root in the Caffe example notebooks.