# jlrs
[](https://docs.rs/jlrs)
[](https://opensource.org/licenses/MIT)
jlrs is a crate that provides access to most of the Julia C API, it can be used to embed Julia
in Rust applications and to use functionality it provides when writing `ccall`able
functions in Rust. Currently this crate is only tested in combination with Julia 1.6 and 1.8,
but also supports Julia 1.7 and 1.9. Using the current stable version is highly recommended.
The minimum supported Rust version is currently 1.65.
The documentation assumes you're already familiar with the Julia and Rust programming
languages.
An incomplete list of features that are currently supported by jlrs:
- Access arbitrary Julia modules and their content.
- Call Julia functions, including functions that take keyword arguments.
- Handle exceptions or convert them to an error message, optionally with color.
- Include and call your own Julia code.
- Use a custom system image.
- Create values that Julia can use, and convert them back to Rust, from Rust.
- Access the type information and fields of values. The contents of inline and bits-union
fields can be accessed directly.
- Create and use n-dimensional arrays. The `jlrs-ndarray` feature can be enabled for
integration with ndarray.
- Map Julia structs to Rust structs, the Rust implementation can be generated with the
JlrsCore package.
- Structs that can be mapped to Rust include those with type parameters and bits unions.
- Use Julia from multiple threads with an async runtime, these runtimes support scheduling
Julia `Task`s and `await`ing them without blocking the runtime thread.
- Export Rust types, methods and functions to Julia with the `julia_module` macro; libraries
that use this macro can be compiled with BinaryBuilder and distributed as JLLs.
NB: Active development happens on the `dev` branch, the `master` branch points to the most
recently released version.
## Prerequisites
Julia must be installed before jlrs can be used, jlrs is compatible with Julia 1.6 up to and
including Julia 1.9. The JlrsCore package must also have been installed, if this is not the
case it will automatically be added when jlrs is initialized by default. jlrs has not been
tested with juliaup yet on Linux and macOS.
### Linux
The recommended way to install Julia is to download the binaries from the official website,
which is distributed in an archive containing a directory called `julia-x.y.z`. This directory
contains several other directories, including a `bin` directory containing the `julia`
executable.
During compilation, the paths to the header and library are normally detected automatically by
executing the command `which julia`. The path to `julia.h` must be
`$(which julia)/../include/julia/julia.h` and the path to the library
`$(which julia)/../lib/libjulia.so`. If you want to override this default behaviour the
`JULIA_DIR` environment variable must be set to the path to the appropriate `julia.x-y-z`
directory, in this case `$JULIA_DIR/include/julia/julia.h` and
`$JULIA_DIR/lib/libjulia.so` are used instead.
In order to be able to load `libjulia.so` this file must be on the library search path. If
this is not the case you must add `/path/to/julia-x.y.z/lib` to the `LD_LIBRARY_PATH`
environment variable. When the `uv` feature is enabled, `/path/to/julia-x.y.z/lib/julia` must
also be added to `LD_LIBRARY_PATH`. The latter path should not be added to the default path
because this can break tools installed on your system.
### Windows
Julia can be installed using juliaup, or with the installer or portable installation
downloaded from the official website. In the first case, Julia has been likely installed in
`%USERPROFILE%\.julia\juliaup\julia-x.y.z+0~x64`, while using the installer or extracting
allows you to pick the destination. After installation or extraction a folder called
`Julia-x.y.z` exists, which contains several folders including a `bin` folder containing
`julia.exe`. The path to the `bin` folder must be added to the `Path` environment variable.
Julia is automatically detected by executing the command `where julia`. If this returns
multiple locations the first one is used. The default can be overridden by setting the
`JULIA_DIR` environment variable. This doesn't work correctly with juliaup, in this case
the environment variable must be set.
### macOS
Follow the instructions for Linux, but replace `LD_LIBARY_PATH` with `DYLD_LIBARY_PATH`.
## Features
Most functionality of jlrs is only available if the proper features are enabled. These
features generally belong to one of three categories: versions, runtimes and utilities.
### Versions
There are minor incompatibilities between different versions of Julia, to ensure the correct
bindings are used for a particular version of Julia you must enable a version features to use
jlrs. The following version features currently exist:
- `julia-1-6`
- `julia-1-7`
- `julia-1-8`
- `julia-1-9`
Exactly one version feature must be enabled. If no version is enabled, or multiple are, jl-sys
will fail to compile.
If you want your crate to be compatible with multiple versions of Julia, you should reexport
these version features:
```toml
[features]
julia-1-6 = ["jlrs/julia-1-6"]
julia-1-7 = ["jlrs/julia-1-7"]
julia-1-8 = ["jlrs/julia-1-8"]
julia-1-9 = ["jlrs/julia-1-9"]
```
In this case you must provide this feature when you build or run your crate:
`cargo (build,run) --feature julia-1-8`.
### Runtimes
A runtime lets you embed Julia in a Rust application, the following features enable a runtime:
- `sync-rt`
Enables the sync runtime, `Julia`. The sync runtime provides single-threaded, blocking
access to the Julia C API.
- `async-rt`
Enables the async runtime, `AsyncJulia`. The async runtime runs on a separate thread and
can be used from multiple threads. Since Julia 1.9 it's possible to start the async runtime
with multiple worker threads.
- `tokio-rt` and `async-std-rt`
These features provide a backing runtime for the async runtime. The first uses tokio, the
second async-std. The `async-rt` feature is automatically enabled when one of these features
is enabled.
If you're writing a library, either one that will be called from Julia or one that will be
used by a Rust application that embeds Julia, no runtime is required.
### Utilities
In addition to these runtimes, the following utility features are available:
- `prelude`
Provides a prelude module, `jlrs::prelude`. This feature is enabled by default.
- `async`
Enable the features of the async runtime which don't depend on the backing runtime. This
can be used in libraries which provide implementations of tasks that the async runtime can
handle.
- `jlrs-derive`
This feature should be used in combination with the code generation provided by the Reflect
module in the JlrsCore package. This module lets you generate Rust implementations of Julia
structs, this generated code uses custom derive macros made available with this feature to
enable the safe conversion of data from Julia to Rust, and from Rust to Julia in some cases.
- `jlrs-ndarray`
Access the content of a Julia array as an `ArrayView` or `ArrayViewMut` from ndarray.
- `f16`
Adds support for working with Julia's `Float16` type from Rust using half's `f16` type.
- `ccall`
Julia's `ccall` interface can be used to call functions written in Rust from Julia. No
runtime can be used in this case because Julia has already been initialized, when this
feature is enabled the `CCall` struct is available which offers the same functionality as
the sync runtime without initializing Julia. The `julia_module` macro is provided to
easily export functions, types, and data in combination with the macros from the Wrap
module in the JlrsCore package.
- `uv`
This feature enables the method `CCall::uv_async_send`, which can be used to wake a Julia
`AsyncCondition` from Rust. The `ccall` feature is automically enabled when this feature
is used.
- `pyplot`
This feature lets you plot data using the Pyplot package and Gtk 3 from Rust.
- `internal-types`
Provide extra managed types for types that are mostly used internally by Julia.
- `extra-fields`
Provide extra field accessor methods for managed types.
- `i686`
Link with a 32-bit build of Julia on Linux, only used for cross-compilation.
- `windows`
Flag that must be enabled when cross-compiling for Windows from Linux.
- `debug`
Link with a debug build of Julia on Linux.
- `no-link`
Don't link Julia, linking can be skipped when writing libraries that will be loaded by
Julia.
- `yggdrasil`
Flag that must be enabled when compiling with BinaryBuilder.
You can enable all features except `debug`, `i686`, `windows`, `no-link` and `yggdrasil` by
enabling the `full` feature.
## Using this crate
If you want to embed Julia in a Rust application, you must enable a runtime and a version
feature:
`jlrs = {version = "0.18.0-beta.3", features = ["sync-rt", "julia-1-8"]}`
`jlrs = {version = "0.18.0-beta.3", features = ["tokio-rt", "julia-1-8"]}`
`jlrs = {version = "0.18.0-beta.3", features = ["async-std-rt", "julia-1-8"]}`
When Julia is embedded in an application, it must be initialized before it can be used. The
following snippet initializes the sync runtime:
```rust
use jlrs::prelude::*;
// Initializing Julia is unsafe because this can load arbitrary
// Julia code, and because it can race with other crates unrelated
// to jlrs. It returns an error if Julia has already been
// initialized.
let mut julia = unsafe { RuntimeBuilder::new().start().unwrap() };
// A StackFrame must be provided to ensure Julia's GC can be made aware
// of references to Julia data that exist in Rust code.
let mut frame = StackFrame::new();
let _instance = julia.instance(&mut frame);
```
To use the async runtime you must upgrade the `RuntimeBuilder` to an
`AsyncRuntimeBuilder` by providing a backing runtime. Implementations for tokio
and async-std are available if these features have been enabled. When starting the async
runtime, you must declare the maximum number of concurrent tasks as a const generic.
For example, an async runtime backed by tokio and an unbounded channel, that supports 3
concurrent task can be initialized as follows if the `tokio-rt` feature is enabled:
```rust
use jlrs::prelude::*;
// Initializing Julia is unsafe for the same reasons as the sync runtime.
let (_julia, _task_handle) = unsafe {
RuntimeBuilder::new()
.async_runtime::<Tokio>()
.start::<3>()
.unwrap()
};
```
The async runtime can also be spawned as a blocking task on an existing executor:
```rust
use jlrs::prelude::*;
#[tokio::main]
async fn main() {
// Initializing Julia is unsafe for the same reasons as the sync runtime.
let (_julia, _task_handle) = unsafe {
RuntimeBuilder::new()
.async_runtime::<Tokio>()
.start_async::<3>()
.unwrap()
};
}
```
If you're calling Rust from Julia everything has already been initialized. If the `ccall`
feature is enabled `CCall` is available which provides the same functionality as the sync
runtime.
### Calling Julia from Rust
This section will focus on some topics that are common between the sync and async runtimes.
After initialization you have an instance of `Julia` or `AsyncJulia`, both provide a
method called `include` that lets you include files with custom Julia code. In order to
create Julia data and call Julia functions, a scope must be created first.
When the sync runtime is used this can be done by calling the method `Julia::scope`. This
method takes a closure with a single argument, a `GcFrame` (frame). This frame can be used
to access Julia data, and ensure it's not freed by the GC while it's accessible from Rust.
The async runtime can't create a new scope directly, `AsyncJulia` is a handle to the async
runtime which runs on another thread. Instead, the async runtime deals with tasks, each task
runs in its own scope. The simplest kind of task is a blocking task, which can be executed by
calling `AsyncJulia::blocking_task`. This method accepts any closure `Julia::scope` can
handle with the additional requirement that it must be `Send` and `Sync`. It's called a
blocking task because the thread that executes this task is blocked while executing it. The
other kinds of tasks that the async runtime can handle will be introduced later.
Inside the closure provided to `Julia::scope` or `AsyncJulia::blocking_task` it's possible to
interact with Julia. Global Julia data can be accessed through its module system, the methods
`Module::main`, `Module::base`, and `Module::core` can be used to access the `Main`,
`Base`, and `Core` modules respectively. The contents of these modules can then be accessed by
calling `Module::function` which returns a `Function`, `Module::global` which returns a
`Value`, and `Module::submodule` which returns another `Module`. These types are
examples of managed types, handles to data owned by Julia's GC. Most functionality in jlrs
is provided through methods implemented by managed types.
The most generic managed type is `Value`, all other managed types can always be converted to
a `Value`. It provides several methods to allocate new Julia data. The simplest one is
`Value::eval_string`, which evaluates the contents of the string passed to it and returns
the result as a `Value`. For example, you can evaluate `2` to convert it to `Value`. In
practice, this method should rarely be used. It can be used to evaluate simple function calls
like `sqrt(2)`, but it must be parsed, compiled, and can't take any non-literal arguments. Its
most important use-case is importing installed and standard library packages by evaluating an
`import` or `using` statement.
A more interesting method, `Value::new`, can be used with data of any type that implements
`IntoJulia`. This trait is implemented by primitive types like `i8` and `char`. Any type
that implements `IntoJulia` also implements `Unbox` which is used to extract the contents
of a `Value`. Managed types like `Array` don't implement `IntoJulia` or `Unbox`, if they
can be created from Rust they provide methods to do so.
As a simple example, let's convert two numbers to Julia values and add them:
```rust
use jlrs::prelude::*;
// Initializing Julia is unsafe because it can race with another crate that does
// the same.
let mut julia = unsafe { RuntimeBuilder::new().start().unwrap() };
let mut frame = StackFrame::new();
let mut julia = julia.instance(&mut frame);
let i = Value::new(&mut frame, 2u64);
let j = Value::new(&mut frame, 1u32);
// The `+` function can be found in the base module.
let func = Module::base(&frame).function(&mut frame, "+")?;
// Call the function and unbox the result as a `u64`. The result of the function
// call is a nested `Result`; the outer error doesn't contain to any Julia
// data, while the inner error contains the exception if one is thrown. Here the
// exception is converted to the outer error type by calling `into_jlrs_result`, this new
// error contains the error message Julia would have shown.
unsafe { func.call2(&mut frame, i, j) }
.into_jlrs_result()?
.unbox::<u64>()
}).unwrap();
assert_eq!(res, 3);
```
Evaluating raw code and calling Julia functions is always unsafe. Nothing prevents you from
calling a function like `nasaldemons() = unsafe_load(Ptr{Float64}(0x05391A445))`. Similarly,
mutating Julia data is unsafe because nothing prevents you from mutating data that shouldn't
be mutated, e.g. a `DataType`. A full overview of the rules that you should keep in mind can
be found in the `safety` module.
#### Async and persistent tasks
In addition to blocking tasks, the async runtime lets you execute async tasks which implement
the `AsyncTask` trait, and persistent tasks which implement `PersistentTask`. Both of
these traits are async traits.
An async task is similar to a blocking task, except that you must implement the async `run`
method instead of providing a closure. This method takes an `AsyncGcFrame`. This new frame
type not only provides access to the same features as `GcFrame`, it can also be used to call
async methods provided by the `CallAsync` trait. These methods schedule a function call as a
new Julia `Task` and can be `await`ed until this task has completed. The async runtime can
switch to another task while the result is pending, allowing multiple tasks to run
concurrently on a single thread.
The previous example can be rewritten as an async task:
```rust
use jlrs::prelude::*;
struct AdditionTask {
a: u64,
b: u32,
}
// Only the runtime thread can call the Julia C API, so the async
// trait methods of `AsyncTask` must not return a future that
// implements `Send` or `Sync`.
#[async_trait(?Send)]
impl AsyncTask for AdditionTask {
// The type of the result of this task if it succeeds.
type Output = u64;
// The affinity of the task. Setting it to `DispatchAny` allows the
// task to be dispatched to both the main thread and worker threads
// if they are available.
type Affinity = DispatchAny;
// This async method replaces the closure from the previous examples,
// an `AsyncGcFrame` can be used the same way as a `GcFrame` but also
// can be used in combination with methods from the `CallAsync` trait.
async fn run<'frame>(
&mut self,
mut frame: AsyncGcFrame<'frame>,
) -> JlrsResult<Self::Output> {
let a = Value::new(&mut frame, self.a);
let b = Value::new(&mut frame, self.b);
let func = Module::base(&frame).function(&mut frame, "+")?;
// CallAsync::call_async schedules the function call on another
// thread and returns a Future that resolves when the scheduled
// function has returned or thrown an error.
unsafe { func.call_async(&mut frame, &mut [a, b]) }
.await
.into_jlrs_result()?
.unbox::<u64>()
}
}
```
While blocking and async tasks run once and return their result, a persistent task returns a
handle to the task. This handle can be shared across threads and used to call its `run`
method. In addition to a global and an async frame, this method can use the state and input
data provided by the caller.
As an example, let's accumulate some number of values in a Julia array and return the sum of
its contents:
```rust
use jlrs::prelude::*;
struct AccumulatorTask {
n_values: usize,
}
struct AccumulatorTaskState<'state> {
array: TypedArray<'state, 'static, usize>,
offset: usize,
}
// Only the runtime thread can call the Julia C API, so the async trait
// methods of `PersistentTask` must not return a future that implements
// `Send` or `Sync`.
#[async_trait(?Send)]
impl PersistentTask for AccumulatorTask {
// The type of the result of the task if it succeeds.
type Output = usize;
// The type of the task's internal state.
type State<'state> = AccumulatorTaskState<'state>;
// The type of the additional data that the task must be called with.
type Input = usize;
// The affinity of the task. Setting it to `DispatchAny` allows the
// task to be dispatched to both the main thread and worker threads
// if they are available.
type Affinity = DispatchAny;
// This method is called before the task can be called. Note that the
// frame is not dropped until the task has completed, so the task's
// internal state can contain Julia data rooted in this frame.
async fn init<'frame>(
&mut self,
mut frame: AsyncGcFrame<'frame>,
) -> JlrsResult<Self::State<'frame>> {
// A `Vec` can be moved from Rust to Julia if the element type
// implements `IntoJulia`.
let data = vec![0usize; self.n_values];
let array = TypedArray::from_vec(frame.as_extended_target(), data, self.n_values)?
.into_jlrs_result()?;
Ok(AccumulatorTaskState { array, offset: 0 })
}
// Whenever the task is called through its handle this method
// is called. Unlike `init`, the frame that this method can use
// is dropped after `run` returns.
async fn run<'frame, 'state: 'frame>(
&mut self,
mut frame: AsyncGcFrame<'frame>,
state: &mut Self::State<'state>,
input: Self::Input,
) -> JlrsResult<Self::Output> {
{
// Array data can be directly accessed from Rust.
// The data is tracked first to ensure it's not
// already borrowed from Rust.
unsafe {
let mut tracked = state.array.track_exclusive()?;
let mut data = tracked.bits_data_mut()?;
data[state.offset] = input;
};
state.offset += 1;
if (state.offset == self.n_values) {
state.offset = 0;
}
}
// Return the sum of the contents of `state.array`.
unsafe {
Module::base(&frame)
.function(&mut frame, "sum")?
.call1(&mut frame, state.array.as_value())
.into_jlrs_result()?
.unbox::<usize>()
}
}
}
```
### Calling Rust from Julia
Julia's `ccall` interface can be used to call `extern "C"` functions defined in Rust.
A function pointer can be cast to a void pointer and converted to a `Value`:
```rust
use jlrs::prelude::*;
// This function will be provided to Julia as a pointer, so its name can be mangled.
unsafe extern "C" fn call_me(arg: bool) -> isize {
if arg {
1
} else {
-1
}
}
let mut frame = StackFrame::new();
let mut julia = unsafe { RuntimeBuilder::new().start().unwrap() };
let mut julia = julia.instance(&mut frame);
julia
.scope(|mut frame| unsafe {
// Cast the function to a void pointer
let call_me_val = Value::new(&mut frame, call_me as *mut std::ffi::c_void);
// Value::eval_string can be used to create new functions.
let func = Value::eval_string(
&mut frame,
"myfunc(callme::Ptr{Cvoid})::Int = ccall(callme, Int, (Bool,), true)",
)
.into_jlrs_result()?;
// Call the function and unbox the result.
let result = func
.call1(&mut frame, call_me_val)
.into_jlrs_result()?
.unbox::<isize>()?;
assert_eq!(result, 1);
Ok(())
})
.unwrap();
```
You can also use functions defined in `cdylib` libraries. In order to create such
a library you need to add
```toml
[lib]
crate-type = ["cdylib"]
```
to your crate's `Cargo.toml`. It's also recommended to abort on panic:
```toml
[profile.release]
panic = "abort"
```
The easiest way to export Rust functions like `call_me` from the previous example is by
using the `julia_module` macro. The content of the macro is converted to an initialization
function that can be called from Julia to generate the module.
In Rust, the macro can be used like this:
```rust
julia_module! {
become callme_init_fn;
fn call_me(arg: bool) -> isize;
}
```
while on the Julia side things look like this:
```julia
module CallMe
using JlrsCore.Wrap
@wrapmodule("./path/to/libcallme.so", :callme_init_fn)
function __init__()
@initjlrs
end
end
```
All Julia functions are automatically generated and have the same name as the exported
function:
```julia
@assert CallMe.call_me(true) == 1
@assert CallMe.call_me(false) == -1
```
This macro has many more capabilities than just exporting extern "C" functions, for more
information see the [documentation](https://docs.rs/jlrs-macros/latest/jlrs_macros/macro.julia_module.html). A practical example that uses this macro is the
[rustfft-jl](https://github.com/Taaitaaiger/rustfft-jl) crate, which uses this macro to expose RustFFT to Julia. The recipe for
BinaryBuilder can be found [here](https://github.com/JuliaPackaging/Yggdrasil/tree/master/R/rustfft).
While `call_me` doesn't call back into Julia, it is possible to call arbitrary functions from
jlrs from a `ccall`ed function. This will often require a `Target`, to create a target you
must create an instance of `CCall` first.
## Testing
The restriction that Julia can be initialized once must be taken into account when running
tests that use `jlrs`. Because tests defined in a single crate are not guaranteed to be run
from the same thread you must guarantee that each crate has only one test that initializes
Julia. It's recommended you only use jlrs in integration tests because each top-level
integration test file is treated as a separate crate.
```rust
use jlrs::prelude::*;
fn test_1(julia: &mut Julia) {
// use instance
}
fn test_2(julia: &mut Julia) {
// use instance
}
#[test]
fn call_tests() {
let mut pending = unsafe { RuntimeBuilder::new().start().unwrap() };
let mut frame = StackFrame::new();
let mut julia = pending.instance(&mut frame);
test_1(&mut julia);
test_2(&mut julia);
}
```
Because `AsyncJulia` is thread-safe, it is possible to have multiple tests in a single crate
when the async runtime is used:
```rust
use std::{num::NonZeroUsize, sync::Arc};
use jlrs::prelude::*;
use once_cell::sync::OnceCell;
fn init() -> Arc<AsyncJulia<Tokio>> {
unsafe {
Arc::new(
RuntimeBuilder::new()
.async_runtime::<Tokio>()
.n_threads(4)
.channel_capacity(NonZeroUsize::new_unchecked(32))
.start::<4>()
.expect("Could not init Julia")
.0,
)
}
}
pub static JULIA: OnceCell<Arc<AsyncJulia<Tokio>>> = OnceCell::new();
#[test]
fn test_1() {
let julia = JULIA.get_or_init(init);
// use instance
}
#[test]
fn test_2() {
let julia = JULIA.get_or_init(init);
// use instance
}
```
## Custom types
In order to map a struct in Rust to one in Julia you can derive several traits. You normally
shouldn't need to implement these structs or traits manually. The `reflect` function defined
in the `JlrsCore.Reflect` module can generate Rust structs whose layouts match their counterparts
in Julia and automatically derive the supported traits.
The main restriction is that structs with atomic fields, and tuple or union fields with type
parameters are not supported. The reason for this restriction is that the layout of such
fields can be very different depending on the parameters in a way that can't be easily
represented in Rust.
These custom types can also be used when you call Rust from Julia with `ccall`.