Save and load variables in Julia Data format (JLD)
JLD, for which files conventionally have the extension
.jld, is a
widely-used format for data storage with the Julia programming
language. JLD is a specific "dialect" of HDF5, a
cross-platform, multi-language data storage format most frequently
used for scientific data. By comparison with "plain" HDF5, JLD files
automatically add attributes and naming conventions to preserve type
information for each object.
For lossless storage of arbitrary Julia objects, the only other
complete solution appears to be Julia's serializer, which can be
accessed via the
deserialize commands. However,
because the serializer is also used for inter-process communication,
long-term backwards compatibility is currently uncertain. (The
JLDArchives repository exists to test compatibility of older JLD file
formats.) If you choose to save data using the serializer, please use
the file extension
.jls to distinguish the files from
Note: You should only read JLD files from trusted sources, as JLD files are capable of executing arbitrary code when read in.
Within Julia, use the package manager:
To use the JLD module, begin your code with
If you just want to save a few variables and don't care to use the more advanced features, then a simple syntax is:
t = 15 z = [1,3] save("/tmp/myfile.jld", "t", t, "arr", z) # or equivalently: @save "/tmp/myfile.jld" t z
Here we're explicitly saving
myfile.jld. You can alternatively pass
save a dictionary; the keys must be
strings and are saved as the variable names of their values within the JLD
file. You can read these variables back in with
d = load("/tmp/myfile.jld")
which reads the entire file into a returned dictionary
d. Or you can be more
specific and just request particular variables of interest. For example,
z = load("/tmp/myfile.jld", "arr") will return the value of
arr from the file
and assign it back to z.
JLD uses the FileIO package to provide a generic
load files. However this means that the user needs to
explicitly request for the JLD format to be used while saving a new file.
save("/tmp/foo","bar",0.0) # ambiguous save("/tmp/foo.jld","bar",0.0) # JLD format is inferred from the file extension using FileIO; save(File(format"JLD","/tmp/foo"),"bar",0.0) # JLD format explicitly requested using FileIO
This problem is not encountered while loading a JLD file because FileIO can use magic bytes at the beginning of the file to infer its data format.
There are also convenience macros
@load that work on the
@save "/tmp/myfile.jld" t z # or @save compress=true "/tmp/myfile.jld" t z
will create a file with just
z, with or without compression.
If you don't mention any variables, then
@save saves all the variables in the
current module. Conversely,
@load will pop the saved variables directly into
the global workspace of the current module.
However, keep in mind that these macros have significant limitations: for example,
you can't use
@load inside a function, there are constraints on using string
interpolation inside filenames, etc. These limitations stem
from the fact that Julia compiles functions to machine code before evaluation,
so you cannot introduce new variables at runtime or evaluate expressions
in other workspaces.
load functions do not have these limitations, and are therefore
recommended as being considerably more robust, at the cost of some slight
reduction in convenience.
More fine-grained control can be obtained using functional syntax:
jldopen("mydata.jld", "w") do file write(file, "A", A) # alternatively, say "@write file A" end c = jldopen("mydata.jld", "r") do file read(file, "A") end
This allows you to add variables as they are generated to an open JLD file.
You don't have to use the
do syntax (
file = jldopen("mydata.jld", "w") works
just fine), but an advantage is that it will automatically close the file (
for you, even in cases of error.
Julia's high-level wrapper, providing a dictionary-like interface, may also be of interest:
using JLD, HDF5 jldopen("test.jld", "w") do file g = create_group(file, "mygroup") # create a group g["dset1"] = 3.2 # create a scalar dataset inside the group g["dest2"] = rand(2,2) end
Note that the features of HDF5 generally can also be used on JLD files.
Types and their definitions
You can save objects that have user-defined type; in a fresh Julia session, before loading those objects these types need to be defined. If no definition is available, the JLD module will automatically create the types for you. However, it's important to note that
MyType, defined automatically by JLD, is not the same
MyType as defined in an external module---in particular, module functions will not work for types defined by JLD. To ensure that loaded types have the full suite of behaviors provided by their definition in external modules, you should ensure that such modules are available before reading such variables from a
To ensure automatic loading of modules, use
addrequire to specify any dependencies. For example, suppose you have a file
"MyTypes.jl" somewhere on your default
LOAD_PATH, defined this way:
module MyTypes export MyType struct MyType value::Int end end
and you have an object
x of type
MyType. Then save
x in the following way:
jldopen("somedata.jld", "w") do file addrequire(file, MyTypes) write(file, "x", x) end
This will cause
"MyTypes.jl" to be loaded automatically whenever
"somedata.jld" is opened.
If you have performance problems...
Please see the complete documentation, particularly the section about custom serializers.
More extensive documentation, including information about the JLD
format conventions, can be found in the
test directory contains a number of test scripts that also
Simon Kornblith and Tim Holy (co-maintainers and primary authors)
Tom Short contributed to string->type conversion
Thanks also to the users who have reported bugs and tested fixes