Using Jupyter Notebooks to Run Deep Learning Algorithms - AWS Online Tech Talks
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Jupyter Notebook is a popular choice for running machine learning algorithms. In this tech talk, we will cover how Jupyter Notebooks can be securely accessed by creating a ssh tunnel between the client device and the instance. Examples include creating a ssh tunnel, pointing out the configurations on EC2 to allow security allow communication, and create a deep-learning neural network on a notebook and running this algorithm on a GPU.
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Using Jupyter Notebooks to Run Deep Learning Algorithms - AWS Online Tech Talks
- 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pratap Ramamurthy Partner Solutions Architect Aug 29, 2017 Using Jupyter Notebooks to Run Deep Learning Algorithms
- 2. Agenda • Customers running Deep Learning on AWS • Three things you need • AWS instance with GPUs • Amazon Deep Learning AMI • Deep Learning Software Stack • Deep dive into Jupyter Notebook setup on AWS • Demo • Advanced Cluster Architecture on AWS
- 3. Customers running Deep Learning on AWS
- 4. Things you need to run Jupyter Notebook Hardware Operating system ML Software stack
- 5. Hardware: AWS P2 Instance type • Up to 16 NVIDIA K80 GPUs • 40,000 parallel processing cores • Single precision: 70 teraflops • Double precision: 23 teraflops • GPUDirect™
- 7. Deep Learning AMI – GPU acceleration software
- 8. Deep Learning Frameworks pre-installed
- 9. What is Jupyter notebook?
- 10. Jupyter running on your own computer Laptop Browser http://localhost:8888/ Jupyter Kernel Port: 8888
- 11. Architecture - Jupyter on single instance Laptop Browser Jupyter Kernel
- 12. SSH tunnel explained Laptop Browser http://localhost:9000 Jupyter Kernel Listening on localhost:8888 Secure tunnel on port 22
- 13. Steps to run Jupyter Notebook on AWS 1. Launch an AWS instance from AWS console A. Choose Deep Learning AMI B. Choose P2 instance type C. Configure Security Group 2. On the instance A. SSH to the instance B. Start Jupyter notebook on the instance 3. On your laptop A. Create SSH tunnel between laptop and instance B. Access Jupyter Notebook through browser
- 14. SSH to instance ssh -i key.pem ec2-user@ip-address ssh -nNt -L localhost:9000:localhost:8888 -i key.pem ec2-user@ip-address Create a tunnel to the instance
- 15. Demo
- 16. References • AWS P2 Instance type: https://aws.amazon.com/ec2/instance-types/p2/ • Deep Learning AMI: https://aws.amazon.com/amazon-ai/amis/ • Deep Learning Cluster CloudFormation: https://github.com/awslabs/deeplearning-cfn
Editor's Notes
- So, to do machine learning well, you not only need substantial machine learning capabilities across all three layers of the stack, but also need the right data store, security, and analytics services to work hand-in-hand. Between our current array of ML capabilities and new things on the horizon; the combination of our more open and flexible approach; the easy integration with the best storage, analytics, security, and infrastructure platform in the world; and the clear attractiveness of not having to manage the data and security model across multiple platforms, most customers conclude that AWS is the best choice for ML. This is why we have so many more customer references than you’ll find anywhere else for machine learning. It’s also still early in this space, and a lot will change. You’ll want to partner with somebody who is customer obsessed, listens well, and is willing to do what it takes to make you successful over a long period of time without trying to force you down one particular direction. We think AWS is unique in these regards.
- The AWS Deep Learning AMIs running on Amazon EC2 P2 instances are pre-installed with NVIDIA CUDA and cuDNN drivers for all supported deep learning frameworks, to substantially accelerate the time to complete your computations. When using Intel-based processors for your Amazon EC2 instances, the Deep Learning AMIs come integrated with the Intel Math Kernel Library (MKL) to accelerate math processing and neural network routines. The AWS Deep Learning AMIs come installed with Jupyter (formerly iPython) notebooks loaded with Python 2.7 and Python 3.4 kernels, along with your favorite popular Python packages, including the AWS SDK for Python. To simplify package management and deployment, the AWS Deep Learning AMIs install the Anaconda2 and Anaconda3 Data Science platform, for large-scale data processing, predictive analytics, and scientific computing.
- Jupyter Notebooks are an open document format based on JSON. They contain a complete record of the user's sessions and embed code, narrative text, equations and rich output. The Notebook communicates with computational Kernels using the Interactive Computing Protocol, an open network protocol based on JSON data over ZMQ and WebSockets. Kernels are processes that run interactive code in a particular programming language and return output to the user. Kernels also respond to tab completion and introspection requests.