Step by step to train depth learning models on Amazon Cloud Server AWS

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Summary Time In UDacity Unmanned Term1

For me that I have not contacted AWS, it can be said that it is a pit, and there is no very complete and detailed tutorial on the Internet. This project can be said tortured me for the longest, and I am the most Painful Project.

Let everyone walk less and more, and share it, hoping to help everyone.

Note: Because of the virtual environment provided by the UDacity provided by UDacity, the following use carnd instead of EC2-User. Everyone needs to pay attention to replacement during use.

login AWS server

This part is relatively simple, I simply write. There are many related information on the Internet. If you don’t know, you can check it, or you can check the help documentation of AWS.

1. Open Instance

2.ssh carnd @ xxx (xxx is public DNS)

3. Enter the password

4. Successfully Success

Upload and Reference Data </ ??b>

If you want to train the model, you need a lot of data. To train these models on the AWS GPU, you must first upload these data to the AWS server to reference.

So we need to use the SCP command to compare the local file to the server on the local Terminal .

scp xxxxx (local path) Carnd (EC2-USERS) @xxx (public DNS): / home / carnd (EC2-users) / </ code> </ pre>

(note that this step must be on the local Terminal.)

Using the file command to open the target folder on the server’s Terminal:

LS / home / carnd / </ code> </ pre>

then decompressed:

unzip data. Zip </ code> </ pre>

where data.zip is the file name to decompress, unzip is a decompression command.

(note that this step must be operated on the server’s Terminal)

This will be referenced in the subsequent program

Login Jupyter Notebook:

Because Jupyter Notebook is a more IDE that everyone used, it is also easy to use, so let’s explain how to open Jupyter Notebook on the server.

< P> 1. Enter:

jup = "language-text"> jurs </ code> </ pre>

2. then Run the following command in the local Notebook:

from iPython.lib import passwd
Passwd () </ code> </ pre>

running the program to get such a password & # 34; SHA1: B592A9CF2EC6: B99EDB2FD3D0727E336185A0B0EAB561AA5333A43 & # 34;

then enter on the Terminal of the login server :

vi ~ / .jupyter / jupyter_notebook_config.py </ code> </ pre>

then enter i in the open document, at this time To insert (Insert) instructions, use it to copy the following fields to the document header (Note to replace the following password to the front you get the password), my carnd environment does not need a key, so you only need to enter the three lines. .

c = get_config () # get the config object
C.NoteBookApp.certfile = u & # 39; /Home/ubuntu/ssl/cert.pem& # 39; # path to the certificate we generated
C.Notebookapp.keyfile = u & # 39; /Home/ubuntu/ssl/cert.key& # 39; # path to the certificate key we generated
C.IPkernelapp.pylab = & # 39; inline & # 39; # in-line first when Using matplotlib
C.NotebookApp.ip = & # 39; * & # 39; # Serve the notebooks locally
C.NoteBookapp.open_browser = false # do not open a browser window by Default When Using Notebooks
c.NotebookApp.password = & # 39; sha1: b592a9cf2ec6:. b99edb2fd3d0727e336185a0b0eab561aa533a43 & # 39; # this is the password hash that we generated earlier </ code> </ pre>

After completion, the need to exit the screen.

Press ESC before entering “: wq”, the command in the quotation marks intended to save changes and exit, and the interface returns to the normal server’s Terminal interface.

Enter:

sudo ssh -i awskeys.peys.pe 3: 127.0.0.1: 8888 ubuntu @ EC2-54-147-126-214.compute-1.amazonaws.com </ code> </ pre>

Everyone above is replaced and changed as appropriate.

Continue to enter

mkdir notebooks in Terminal
CD Notebooks </ code> </ pre>

You need to enter the activation Carnd-Term1 (which is also EC2-User) before opening Notebook, which is a pre-installed program in the virtual environment.

Source Activate carnd-term1 </ code> </ pre>

then enter the command:

jupyter Notebook - NO-Browser </ code> </ pre>

Because port is changed to 8888, enter XXXX: 888 in the browser, where xxxx is public DNS or public DNS or public IP can be.

Next, you can train your model on Jupyter Notebook.

The above is the method of using the AWS, mainly to log in to AWS, upload the data to AWS and extract, and open Jupyter Notebook on AWS.

I wish all life learners, all in the process of learning ~

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