pip install --upgrade wandb. Make sure you have Docker installed and running.launch-quickstart example contains a script train.py that trains a simple neural net with keras and then logs metrics and predictions back to Weights & Biases. There is also a Dockerfile so that you can build the training script into a container image. To do so, run:fmnist project in your Weights & Biases account!wandb command line interface, you can also submit jobs through the Weights & Biases UI.wandb launch-agent -p fmnistfmnist project on the W&B site. Follow the video below to navigate to the launch menu:JSON snippet into the editor that appears, then click Push Run.run_config overrides that we passed in will actually modify the contents of our wandb.config when wandb.init is called, so you modify any hyperparameter and relaunch an experiment without leaving your dashboard.wandb launch supports running new runs (i.e. runs not based on an existing wandb run) from both remote git repositories or local directories.wandb launch https://github.com/user/repo. We require that the repo contain either a requirements.txt or environment.yml configuration file for dependencies, and the code should be already instrumented with wandb for us to track it as normal.wandb launch path/to/local. As with git repos, we also require a requirements.txt or environment.yml file at the root of the provided path. Queueing is not currently supported for launching from a local directory.--entry-point or -E flag.wandb launch-agent --queues <queue-name> <project>--queues q1,q2,q3. If you don't specify a queue with the --queues flag, the agent will run jobs from the default queue for the project.Error: Reproducing a run requires either an associated git repo or a code artifact logged withrun.log_code()
wandb.log_code() in your script to save the code as an artifact with your run.