Running SOL in a framework is the easiest operation mode, as SOL takes care of most parameters, options, etc. automatically. In principle you just need to load SOL and run the sol.optimize(model, args, kwargs={}, *, framwork=None, vdims=None, **fwargs) function on your target model.

See subchapters for details on the different frameworks and example codes.

Parameter Description
model Your model you want to optimize using SOL.
args Either a list or tuple of framework tensors or other inputs.
kwargs A dictionary of named arguments.
framework A framework in which the returned model shall be executed. By default the same as the input model.
vdims A list or tuple containing the value you want to assign to the variable dimension. Valid values are positive integers or None for variable values.
fwargs A dictionary containing framework specific flags. See corresponding framework for available flags.

Generic Model Functions

SOL models are implemented using the framework’s own model structure, so they provide the same functionality as the framework’s models, except that SOL models always assume, that training = False for inference and training = True for training runs. Additionally they support following functions:

Command Description Unique hash of the network.
model.__sol__.free_ctxs() Forces to free all SOL contexts of this network on all devices.

Command Description
sol.__version__ SOL version string
sol.cache.clear() Clears Sol’s build cache, to enforce rebuild of models.
sol.check_version() Checks if a new version of SOL is available
sol.config.print() Prints all config options and their current values
sol.config["..."] = ... Sets config options
sol.deploy(...) Details here
sol.device.disable(device) See sol.device.enable(device)
sol.device.enable(device) Enables code generation for the specified device. By default all available devices will be build. Device needs to be sol.device.[x86, nvidia, ve]
sol.device.set(device, deviceIdx) Forces Sol to run everything on the given device. If the data is not located on the target device, it will be explicitly copied between the host and the device.
sol.devices() Prints overview of available devices. sol.devices() Green: device is initialized (has been used for computations). Star: default device.
sol.env() Prints Env Vars + values used by SOL. sol.env()
sol.optimize(...) Details here
sol.plugins() Prints overview of loaded plugins. sol.plugins()
sol.seed(deviceType=None, deviceIdx=None) Fetches the global seed (both == None), the device type’s seed or the seed of a specific device.
sol.seeds() Prints seed overview: sol.seeds()
sol.set_seed(seed, deviceType=None, deviceIdx=None) Sets the seed.
sol.versions() Prints versions of used compiler and libraries. sol.versions()

For offloading the data needs to be on the host system, otherwise implicit copy is not possible!