> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> Integrate W&B with Julia to track experiments, log metrics, and visualize model performance from Julia programs.

# W&B for Julia

If you run machine learning experiments in Julia, you can use [`wandb.jl`](https://github.com/avik-pal/Wandb.jl), an unofficial set of Julia bindings created by a community contributor.

For more examples, see the [`wandb.jl` examples directory](https://github.com/avik-pal/Wandb.jl/tree/main/docs/src/examples). The following code is the getting started example from the `wandb.jl` repository:

```julia theme={null}
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)
```