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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)