  In software development through integrated development environments (IDEs),
code completion is one of the most widely used features. Nevertheless, majority
of integrated development environments only support completion of methods and
APIs, or arguments.
  In this paper, we introduce IntelliCode Compose $-$ a general-purpose
multilingual code completion tool which is capable of predicting sequences of
code tokens of arbitrary types, generating up to entire lines of syntactically
correct code. It leverages state-of-the-art generative transformer model
trained on 1.2 billion lines of source code in Python, $C\#$, JavaScript and
TypeScript programming languages. IntelliCode Compose is deployed as a
cloud-based web service. It makes use of client-side tree-based caching,
efficient parallel implementation of the beam search decoder, and compute graph
optimizations to meet edit-time completion suggestion requirements in the
Visual Studio Code IDE and Azure Notebook.
  Our best model yields an average edit similarity of $86.7\%$ and a perplexity
of 1.82 for Python programming language.
