[PYTHON] Use jupyter on AWS GPU instance

When I want to use GPU by trial and error of machine learning, I use AWS because my PC does not have GPU. The way at that time.

environment

procedure

  1. Rent a GPU instance

  2. Search AWS Marketplace for "Deep Learning AMI" and select the AMI that appears. (I will use Ubuntu this time)

  3. Select "g2.2xlarge" as the instance type

  4. Instance details settings. Here's the right IAM role. If possible, make it a spot instance and it will be cheaper

  5. Security group settings Change the SSH connection source from the default 0.0.0.0/0 to'My IP'

  6. Create.

  7. Set up SSH transfer

  8. Download the key pair (* .pem) and convert it to a .ppk file with PuTTYgen.

  9. Open [Settings]> [SSH Transfer] from the TeraTerm menu.

  10. From [Add], enter 8888 for [Local Port], localhost for [Remote Host], and 8888 for [Port]. Do not enter anything in [Listen]. (It is important that the remote host is localhost. The transfer destination from the place connected by SSH is the machine itself)

  11. Log in at the terminal

  12. Enter [email protected] in the host name.

  13. Select [RSA / ... Use key] as the authentication method and specify the .ppk file (converted by PuTTY) as the private key.

  14. After logging in, start jupyter. $ jupyter notebook

  15. View in browser The login URL will appear in the terminal, so enter that URL in your browser to open it.

Recommended Posts

Use jupyter on AWS GPU instance
Jupyter on AWS
Run TensorFlow on a GPU instance on AWS
Try Tensorflow with a GPU instance on AWS
Building an environment to run ChainerMN on a GPU instance on AWS
Start jupyter notebook on GPU server (remote server)
You can use Dash on Jupyter jupyter_dash
GPU check of PC on jupyter notebook
Use vim keybindings on Docker-launched Jupyter Notebook
Golang on jupyter
June 2017 version to build Tensorflow / Keras environment on GPU instance of AWS
Create an AWS GPU instance to train StyleNet
[Windows] Memo to use Keras on GPU [Tensorflow-GPU]
Hello X3DOM on Jupyter
Easily launch jupyter notebook on AWS and access locally
# 2 Build a Python environment on AWS EC2 instance (ubuntu18.04)
Deployment procedure on AWS (2) Server (EC2 instance) environment settings
Use pyvenv on Windows
Run GPU version tensorflow on AWS EC2 Spot Instances
Use Ansible on Windows
Install Docker on AWS
Use QuTiP on Windows
Use pip on Windows
Use nim with Jupyter
Build Keras environment on AWS E2 G2 instance February 2017 version
Install octave_kernel on Jupyter [additional]
Run Jupyter on Ubuntu on Windows
Use matplotlib on Ubuntu 12 & Python
Use music21 on Google Colaboratory
Use pip with Jupyter Notebook
Display PIL images on Jupyter
Use Github Desktop on Linux
Smoothly reload modules on jupyter
High charts on Jupyter notebook
View PDF on Jupyter Notebook
Use Cython with Jupyter Notebook
Install Chainer 1.6 (GPU) on Windows 7.
Process on GPU using chainer.cuda.elementwise
Label images on jupyter lab
Use matplot libwidget on mac
Install Caffe on Ubuntu 14.04 (GPU)
Use sshpass on Amazon linux2
Run YOLO v3 on AWS v2
Use Python on Windows (PyCharm)
Run Jupyter Notebook on windows
Use NeoPixel on Raspberry Pi
How to use Jupyter Notebook
Use Linux on Windows 10 (WSL2)
Run YOLO v3 on AWS
Use AWS interpreter with Pycharm
xgboost (python) on EC2 Spot instance environment prepared by AWS Lambda
# 3 Build a Python (Django) environment on AWS EC2 instance (ubuntu18.04) part2
Create an AWS Cloud9 development environment on your Amazon EC2 instance
How to use Jupyter on the front end of supercomputer ITO