The basic outline of a code which uses the ITensor library is as follows
using ITensors let # ... your own code goes here ... # For example: i = Index(2,"i") j = Index(3,"j") T = randomITensor(i,j) @show T end
The reason we recommend the
let...end block is that code written in the Julia global scope can have some surprising behaviors. Putting your code into a
let block avoids these issues.
Alternatively, you can wrap your code in a function:
using ITensors function main(; d1 = 2, d2 = 3) # ... your own code goes here ... # For example: i = Index(d1,"i") j = Index(d2,"j") T = randomITensor(i,j) @show T end main(; d1 = 4, d2 = 5)
which can be useful in interactive mode, particularly if you might want to run your code with a variety of different arguments.
Now say you put the above code into a file named
code.jl. Then you can run this code on the command line as follows
$ julia code.jl
This script-like mode of running Julia is convenient for running longer jobs, such as on a cluster.
However, sometimes you want to do rapid development when first writing and testing a code. For this kind of work, the long startup and compilation times currently incurred by the Julia compiler can be a nuisance. Fortunately a nice solution is to alternate between modifying your code then running it by loading it into an already running Julia session.
To set up this kind of session, take the following steps:
Enter the interactive mode of Julia, by inputting the command
juliaon the command line. You will now be in the Julia "REPL" (read-eval-print loop) with the prompt
julia>on the left of your screen.
To run a code such as the
code.jlfile discussed above, input the command
Note that you must be in the same folder as
code.jlfor this to work; otherwise input the entire path to the
code.jlfile. The code will run and you will see its output in the REPL.
Now say you want to modify and re-run the code. To do this, just edit the file in an editor in another window, without closing your Julia session. Now run the command
again and your updated code will run, but this time skipping any of the precompilation overhead incurred on previous steps.
The above steps to running a code interactively has a big advantage that you only have to pay the startup time of compiling ITensor and other libraries you are using once. Further changes to your code only incur very small extra compilation times, facilitating rapid development.
The above strategy of running code in the Julia REPL (interactive mode) works well, but still incurs a large start-up penalty for the first run of your code. Fortunately there is a nice way around this issue too: compiling ITensors.jl and making a system image built by the PackageCompiler.jl library.
To use this approach, we have provided a convenient one-line command:
julia> using ITensors; ITensors.compile()
Once ITensors.jl is installed, you can just run this command in an interactive Julia session. It can take a few minutes to run, but you only have to run it once for a given version of ITensors.jl. When it is done, it will create a file
sys_itensors.so in the directory
To use the compiled system image together with Julia, run the
julia command (for interactive mode or scripts) in the following way:
$ julia --sysimage ~/.julia/sysimages/sys_itensors.so
A convenient thing to do is to make an alias in your shell for this command. To do this, edit your
.zshrc or similar file for the shell you use by adding the following line:
alias julia_itensors="julia --sysimage ~/.julia/sysimages/sys_itensors.so -e \"using ITensors\" -i "
where of course you can use the command name you like when defining the alias. Now running commands like
julia_itensors code.jl or
julia_itensors to start an interactive session will have the ITensor system image pre-loaded and you will notice significantly faster startup times. The arguments
-e \"using ITensors\" -i make it so that running
julia_itensors also loads the ITensor library as soon as Julia starts up, so that you don't have to type
using ITensors every time.
If you have compiled a sysimage for ITensor as shown above, you can use it in Jupyter by running the following code:
using IJulia installkernel("julia_ITensors","--sysimage=~/.julia/sysimages/sys_itensors.so")
in the Julia REPL (Julia console).
To load the ITensor sysimage in VS Code, you can add
as an argument under the
julia.additionalArgs setting in your Settings.json file.
For more information on the above, see the following Julia Discourse post.