Skip to main content

Documentation Index

Fetch the complete documentation index at: https://wb-21fd5541-style-guide-models-integrations-20260527-015516.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

If you run machine learning experiments in Julia, you can use wandb.jl, an unofficial set of Julia bindings created by a community contributor. For more examples, see the wandb.jl examples directory. The following code is the getting started example from the wandb.jl repository:
using Wandb, Dates, Logging

# Start a new run, tracking hyperparameters in config
lg = WandbLogger(project = "Wandb.jl",
                 name = "wandbjl-demo-$(now())",
                 config = Dict("learning_rate" => 0.01,
                               "dropout" => 0.2,
                               "architecture" => "CNN",
                               "dataset" => "CIFAR-100"))

# Use LoggingExtras.jl to log to multiple loggers together
global_logger(lg)

# Simulating the training or evaluation loop
for x  1:50
    acc = log(1 + x + rand() * get_config(lg, "learning_rate") + rand() + get_config(lg, "dropout"))
    loss = 10 - log(1 + x + rand() + x * get_config(lg, "learning_rate") + rand() + get_config(lg, "dropout"))
    # Log metrics from your script to W&B
    @info "metrics" accuracy=acc loss=loss
end

# Finish the run
close(lg)