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*update_transaction_codes.py* parses Android services source code to find out transaction codes and saves them in YAML format for later use. *transaction_codes.yaml* is an example of the resulting file. *service_call_test.py* shows how it can be used
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Write Your Own Custom Image Dataset for Tensorflow
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The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:
How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
How does PyTorch cwrap work to generate code for Tensor methods?
How does PyTorch's build system take all of these components to compile and generate a workable application?
Extending the Python Interpreter
PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.
The only way I've succeeded so far is to employ SSH.
Assuming you are new to this like me, first I'd like to share with you that your Mac has a SSH config file in a .ssh directory. The config file is where you draw relations of your SSH keys to each GitHub (or Bitbucket) account, and all your SSH keys generated are saved into .ssh directory by default. You can navigate to it by running cd ~/.ssh within your terminal, open the config file with any editor, and it should look something like this:
Amazon Linux 2 - install docker & docker-compose using 'sudo amazon-linux-extras' command
UPDATE (March 2020, thanks @ic): I don't know the exact AMI version but yum install docker now works on the latest Amazon Linux 2. The instructions below may still be relevant depending on the vintage AMI you are using.
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