Getting started with Deep Learning
- 1 1. Get started with DeepSense
- 2 2. Download Caffe samples to your home directory
- 3 3. Request an interactive session on a GPU compute node
- 4 4. Start a python2 Jupyter notebook
- 5 5. Port Forwarding
- 6 6. Open the desired sample notebook
- 7 7. Enjoy Deep Learning on DeepSense!
- 8 8. More information
- 9 9. Using another deep learning toolkit such as Tensorflow
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.
2. Download Caffe samples to your home directory
3. Request an interactive session on a GPU compute node
bsub -Is -gpu - bash
4. Start a python2 Jupyter notebook
Source the Caffe deep learning toolkit
Start the notebook
jupyter notebook --no-browser --ip=0.0.0.0
[I 13:32:23.937 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [C 13:32:23.937 NotebookApp] Copy/paste this URL into your browser when you connect for the first time, to login with a token: http://ds-cmgpu-04:8888/?token=68042f40a10b500f3747ae0a232ee209fa4bf1aa384d29ba&token=68042f40a10b500f3747ae0a232ee209fa4bf1aa384d29ba
Copy the URL, host, and port
Copy the URL but don’t paste it in your browser yet.
Make a note of which compute host and port the notebook is running on (e.g. host ds-cmgpu-04 and port 8888 in this case)
5. Port Forwarding
In a separate terminal window from your local computer, forward your local port to the remote host:
ssh -l <username> login1.deepsense.ca -L <port>:<remote_host>:<port>
ssh -l user1 login1.deepsense.ca -L 8888:ds-cmgpu-04:8888
Enter the copied URL in your web browser but change the remote host name to “localhost” before pressing enter.
6. Open the desired sample notebook
Be sure to enter the location of the “caffe-samples” directory in your home directory as your caffe-root in the Caffe example notebooks.
7. Enjoy Deep Learning on DeepSense!
8. More information
Go to Caffe's website for tutorials and example programs that you can run to get started. See the following links to a couple of the example programs:
LeNet MNIST Tutorial - Train a neural network to understand handwritten digits.
CIFAR-10 tutorial - Train a convolutional neural network to classify small images.
9. Using another deep learning toolkit such as Tensorflow
- Ensure any Anaconda dependencies are installed
- for tensorflow, run
- for tensorflow, run
- Source the appropriate toolkit instead of caffe-activate
- Download example notebooks for the deep learning toolkit to your home directory,
git clone https://github.com/aymericdamien/TensorFlow-Examples.git
The TensorFlow home page has various information, including Tutorials, How-To documents, and a Getting Started guide.
Additional tutorials and examples are available from the community, for example: