As we have seen in Quick Start, GraphVite can be simply invoked from a command line. Here are some other useful commands we can use.
Reproduce baseline benchmarks¶
graphvite baseline [keyword ...] [--no-eval] [--gpu n] [--cpu m] [--epoch e]
GraphVite provides a large number of baselines on standard datasets. To reproduce a baseline benchmark, we only need to specify the keywords of the experiment, and the library will do the rest for us.
By default, baselines are configured to use all CPUs and GPUs. We may override this behavior by specifying the number of GPUs and the number of CPUs per GPU. We may also override the number of training epochs for fast experiments.
For example, the following command line reproduces RotatE model on FB15k dataset, using 4 GPUs and 12 CPUs.
graphvite baseline rotate fb15k --gpu 4 --cpu 3
graphvite list to get a list of available baselines.
Run configuration files¶
Custom experiments can be easily carried out in GraphVite through a yaml configuration. This is especially convenient if we want to use GraphVite as an off-the-shelf tool for pretraining embeddings.
graphvite new [application ...] [--file f]
The above command creates a configuration scaffold for our application, where most settings are ready. We just need to fill a minimal number of settings following the instructions. For a more detailed introduction on configuration files, see Experiment configuration.
Once we complete the configuration file, we can run it by
graphvite run [config] [--no-eval] [--gpu n] [--cpu m] [--epoch e]
Visualize high-dimensional vectors¶
graphvite visualize [file] [--label label_file] [--save save_file] [--perplexity n] [--3d]
We can visualize our high-dimensional vectors with a simple command line in GraphVite.
The file can be either a numpy dump
*.npy or a text matrix
*.txt. We can
also provide a label file indicating the category of each data point. For the save
file, we recommend to use
png format, while