# Do not print anything if this is not being used interactively
[ -z "$PS1" ] && return

# Set up attractive prompt
export PS1="\[\e[31m\]tf-docker\[\e[m\] \[\e[33m\]\w\[\e[m\] > "
export TERM=xterm-256color
alias grep="grep --color=auto"
alias ls="ls --color=auto"
# Fix nvidia-docker

covering security practices for the entirety of the TensorFlow ecosystem.

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).

  /// Finds all entries matching the regular expression given by `glob`.
  ///
  /// Pattern must match the entire entry name, not just a substring.
  ///
  /// pattern: { term }
  /// term:
  ///   '*': matches any sequence of non-'/' characters
  ///   '?': matches a single non-'/' character
  ///   '[' [ '^' ] { match-list } ']':
  ///        matches any single character (not) on the list

        *   Dynamic shapes are supported for generators by calling the Model on
            the first batch we "peek" from the generator. This used to happen
            implicitly in `Model._standardize_user_data`. Long-term, a solution
            where the `DataAdapter` doesn't need to call the Model is probably
            preferable.
    *   The SavedModel format now supports all Keras built-in layers (including