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Version: 0.6.0

Getting started with Starwhale Standalone

When the Starwhale Client (swcli) is installed, you are ready to use Starwhale Standalone.

We also provide a Jupyter Notebook example, you can try it in Google Colab or in your local vscode/jupyterlab.

Downloading Examples​

Download Starwhale examples by cloning the Starwhale project via:

GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/star-whale/starwhale.git --depth 1
cd starwhale

To save time in the example downloading, we skip git-lfs and other commits info. We will use ML/DL HelloWorld code MNIST to start your Starwhale journey. The following steps are all performed in the starwhale directory.

Core Workflow

Building a Pytorch Runtime​

Runtime example codes are in the example/runtime/pytorch directory.

  • Build the Starwhale runtime bundle:

    swcli runtime build --yaml example/runtime/pytorch/runtime.yaml
    tip

    When you first build runtime, creating an isolated python environment and downloading python dependencies will take a lot of time. The command execution time is related to the network environment of the machine and the number of packages in the runtime.yaml. Using the befitting pypi mirror and cache config in the ~/.pip/pip.conf file is a recommended practice.

    For users in the mainland of China, the following conf file is an option:

    [global]
    cache-dir = ~/.cache/pip
    index-url = https://pypi.tuna.tsinghua.edu.cn/simple
    extra-index-url = https://mirrors.aliyun.com/pypi/simple/

  • Check your local Starwhale Runtime:

    swcli runtime list
    swcli runtime info pytorch

Building a Model​

Model example codes are in the example/mnist directory.

  • Download the pre-trained model file:

    cd example/mnist
    make download-model
    # For users in the mainland of China, please add `CN=1` environment for make command:
    # CN=1 make download-model
    cd -

  • Build a Starwhale model:

    swcli model build example/mnist --runtime pytorch

  • Check your local Starwhale models:

    swcli model list
    swcli model info mnist

Building a Dataset​

Dataset example codes are in the example/mnist directory.

  • Download the MNIST raw data:

    cd example/mnist
    make download-data
    # For users in the mainland of China, please add `CN=1` environment for make command:
    # CN=1 make download-data
    cd -

  • Build a Starwhale dataset:

    swcli dataset build --yaml example/mnist/dataset.yaml

  • Check your local Starwhale dataset:

    swcli dataset list
    swcli dataset info mnist
    swcli dataset head mnist

Running an Evaluation Job​

  • Create an evaluation job:

    swcli -vvv model run --uri mnist --dataset mnist --runtime pytorch

  • Check the evaluation result

    swcli job list
    swcli job info $(swcli job list | grep mnist | grep success | awk '{print $1}' | head -n 1)

Congratulations! You have completed the Starwhale Standalone Getting Started Guide.