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Deep Learning System Lab1 Notebooks

This repo contains various notebooks that introduces basic usages and training of deep learning models and applying pretrained state-of-the-art models.

How to use

The python notebooks are written in Jupyter.

Setup

  • We can run and modify these notebooks if both mxnet and jupyter are installed. Here is an example script to install all these packages on Ubuntu.

  • Intructions to setup on AWS instances to run the notebooks:

    1. Create an AWS account, and apply the student credit from AWS Education.

    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:

      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808

      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance. Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column. In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere. Inbound rules

    3. Once launch is succeed, setup the following variable with proper value

      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem

    4. Now we should be able to ssh to the machine by

       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME

      Here we forward the EC2 machine's 8888 port into localhost.

    5. Clone this repo on the EC2 machine and run jupyter

       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook

    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower. The URL should look like: http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0

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15 stars

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Repository files navigation
Deep Learning System Lab1 Notebooks
This repo contains various notebooks that introduces basic usages and training
of deep learning models and applying pretrained state-of-the-art models.
How to use
The python notebooks are written in Jupyter.
Setup
  • We can run and modify these notebooks if both mxnet and jupyter are
installed. Here is an example script to install all these packages on Ubuntu.
  • Intructions to setup on AWS instances to run the notebooks:
    1. Create an AWS account, and apply the student credit from AWS Education.
    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:
      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808
      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance.
Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column.
In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere.
Inbound rules
    3. Once launch is succeed, setup the following variable with proper value
      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem
    4. Now we should be able to ssh to the machine by
       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME
      Here we forward the EC2 machine's 8888 port into localhost.
    5. Clone this repo on the EC2 machine and run jupyter
       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook
    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower.
The URL should look like:
http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0
About
No description, website, or topics provided.
Resources
Readme
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15 stars
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, 'i');
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Deep Learning System Lab1 Notebooks
This repo contains various notebooks that introduces basic usages and training
of deep learning models and applying pretrained state-of-the-art models.
How to use
The python notebooks are written in Jupyter.
Setup
  • We can run and modify these notebooks if both mxnet and jupyter are
installed. Here is an example script to install all these packages on Ubuntu.
  • Intructions to setup on AWS instances to run the notebooks:
    1. Create an AWS account, and apply the student credit from AWS Education.
    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:
      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808
      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance.
Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column.
In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere.
Inbound rules
    3. Once launch is succeed, setup the following variable with proper value
      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem
    4. Now we should be able to ssh to the machine by
       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME
      Here we forward the EC2 machine's 8888 port into localhost.
    5. Clone this repo on the EC2 machine and run jupyter
       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook
    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower.
The URL should look like:
http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0
About
No description, website, or topics provided.
Resources
Readme
Activity
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15 stars
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})();
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try {
var __m = "*";
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Deep Learning System Lab1 Notebooks
This repo contains various notebooks that introduces basic usages and training
of deep learning models and applying pretrained state-of-the-art models.
How to use
The python notebooks are written in Jupyter.
Setup
  • We can run and modify these notebooks if both mxnet and jupyter are
installed. Here is an example script to install all these packages on Ubuntu.
  • Intructions to setup on AWS instances to run the notebooks:
    1. Create an AWS account, and apply the student credit from AWS Education.
    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:
      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808
      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance.
Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column.
In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere.
Inbound rules
    3. Once launch is succeed, setup the following variable with proper value
      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem
    4. Now we should be able to ssh to the machine by
       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME
      Here we forward the EC2 machine's 8888 port into localhost.
    5. Clone this repo on the EC2 machine and run jupyter
       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook
    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower.
The URL should look like:
http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0
About
No description, website, or topics provided.
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, 'i');
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
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Repository files navigation
Deep Learning System Lab1 Notebooks
This repo contains various notebooks that introduces basic usages and training
of deep learning models and applying pretrained state-of-the-art models.
How to use
The python notebooks are written in Jupyter.
Setup
  • We can run and modify these notebooks if both mxnet and jupyter are
installed. Here is an example script to install all these packages on Ubuntu.
  • Intructions to setup on AWS instances to run the notebooks:
    1. Create an AWS account, and apply the student credit from AWS Education.
    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:
      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808
      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance.
Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column.
In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere.
Inbound rules
    3. Once launch is succeed, setup the following variable with proper value
      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem
    4. Now we should be able to ssh to the machine by
       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME
      Here we forward the EC2 machine's 8888 port into localhost.
    5. Clone this repo on the EC2 machine and run jupyter
       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook
    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower.
The URL should look like:
http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0
About
No description, website, or topics provided.
Resources
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - dlsys-course/lab1 · GitHub
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Repository files navigation
Deep Learning System Lab1 Notebooks
This repo contains various notebooks that introduces basic usages and training
of deep learning models and applying pretrained state-of-the-art models.
How to use
The python notebooks are written in Jupyter.
Setup
  • We can run and modify these notebooks if both mxnet and jupyter are
installed. Here is an example script to install all these packages on Ubuntu.
  • Intructions to setup on AWS instances to run the notebooks:
    1. Create an AWS account, and apply the student credit from AWS Education.
    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:
      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808
      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance.
Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column.
In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere.
Inbound rules
    3. Once launch is succeed, setup the following variable with proper value
      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem
    4. Now we should be able to ssh to the machine by
       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME
      Here we forward the EC2 machine's 8888 port into localhost.
    5. Clone this repo on the EC2 machine and run jupyter
       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook
    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower.
The URL should look like:
http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
15 stars
Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
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function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - dlsys-course/lab1 · GitHub
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Repository files navigation
Deep Learning System Lab1 Notebooks
This repo contains various notebooks that introduces basic usages and training
of deep learning models and applying pretrained state-of-the-art models.
How to use
The python notebooks are written in Jupyter.
Setup
  • We can run and modify these notebooks if both mxnet and jupyter are
installed. Here is an example script to install all these packages on Ubuntu.
  • Intructions to setup on AWS instances to run the notebooks:
    1. Create an AWS account, and apply the student credit from AWS Education.
    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:
      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808
      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance.
Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column.
In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere.
Inbound rules
    3. Once launch is succeed, setup the following variable with proper value
      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem
    4. Now we should be able to ssh to the machine by
       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME
      Here we forward the EC2 machine's 8888 port into localhost.
    5. Clone this repo on the EC2 machine and run jupyter
       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook
    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower.
The URL should look like:
http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
15 stars
Watchers
1 watching
Forks
0 forks
Report repository
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Deep Learning System Lab1 Notebooks
This repo contains various notebooks that introduces basic usages and training
of deep learning models and applying pretrained state-of-the-art models.
How to use
The python notebooks are written in Jupyter.
Setup
  • We can run and modify these notebooks if both mxnet and jupyter are
installed. Here is an example script to install all these packages on Ubuntu.
  • Intructions to setup on AWS instances to run the notebooks:
    1. Create an AWS account, and apply the student credit from AWS Education.
    2. Launch a g2.2xlarge or p2.2xlarge instance. The AMI Ids that you can use:
      AWS Region NameRegionAMI Id
      US West (Oregon)us-west-2ami-dfb13ebf
      US East (N. Virginia)us-east-1ami-e7c96af1
      EU (Ireland)eu-west-1ami-6e5d6808
      Remember to open the TCP port 22 and 8888 in the security group. You can modify the security group after you create the instance.
Find the Instances page at the EC2 Dashboard, and click the security group of the instance you created, which is at the last column.
In the Inbound tab, add the SSH rule and customized TCP Rule with port 8888 from anywhere.
Inbound rules
    3. Once launch is succeed, setup the following variable with proper value
      export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
export PERM=~/Downloads/my.pem
    4. Now we should be able to ssh to the machine by
       chmod 400 $PERM
ssh -i $PERM -L 8888:localhost:8888 ec2-user@HOSTNAME
      Here we forward the EC2 machine's 8888 port into localhost.
    5. Clone this repo on the EC2 machine and run jupyter
       sudo yum install -y graphviz
git clone https://github.com/dlsys-course/lab1.git
cd lab1
jupyter notebook
    6. Wait until jupyter creates the notebook and copy the URL it outputs to your brower.
The URL should look like:
http://localhost:8888/?token=5b870699ac133c42c56400de91f66256b89ab211ee38c7e0
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