TCLOUD Command-line Interface v0.4.1

Usage:
tcloud [command] [flags] [args]

Available Commands:
    tcloud init
    tcloud config [-u/-f] [args]
    tcloud upload [-c] <local_dirpath> [<remote_dirpath>]
    tcloud download [<filepath>]
    tcloud add [<dependency_name>]
    tcloud submit [<path_to_repo>]
    tcloud ps [-j] [<JOB_ID>]
    tcloud install [<path_to_repo>]
    tcloud cancel [-j] [<JOB_ID>]
    tcloud ls [<dirpath>]

Use "tcloud [command] --help" for more information about commands.

You can try out the latest features by directly install from master branch:

git clone https://github.com/Turing-AI-Cloud/tcloud-sdk.git
cd tcloud-sdk
echo 'export GOPATH=$PWD' >> ~/.bash_profile
make install

1. Before using the tcloud CLI to submit ML jobs, you need to configure your TACC credentials. You can do this by running the tcloud config command:

$ tcloud config [-u/--username] MYUSERNAME
$ tcloud config [-f/--file] MYPRIVATEFILEPATH

2. You need to run tcloud init command to obtain the latest cluster hardware information from TACC cluster.

You can use tcloud init to pull the latest cluster configuration from TACC. There are four parts in tuxiv.conf that configure different parts of job submission. Noted that tuxiv.conf follows yaml format.

• Entrypoint

  In this section, you should input you shell commands to run your code line-by-line. The tcloud CLI will help run the job according to your commands.

  entrypoint:
      - python ${TACC_WORKDIR}/mnist.py --epoch=3

• Environment

  In this section, you can specify your software requirements, including the environment name, dependencies, source channels and so on. The tcloud CLI will help build your environment with miniconda.

  environment:
      name: torch-env
      dependencies:
          - pytorch=1.6.0
          - torchvision=0.7.0
      channels: pytorch

• Job

  In this section, you can specify your slurm configurations for slurm cluster resources, including number of nodes, CPUs, GPUs, output file and so on. All the slurm cluster configuration should be set in the general part.

  job:
      name: test
      general:
          - nodes=2
          - output=${TACC_SLURM_USERLOG}/output.log

  Note: You can modify the output log path in Job section. For debugging purpose, we recommend you set the output value under ${TACC_USERDIR} directory and check it using tcloud ls and tcloud download.

• Datasets

  In this section, you can specify your required CityNet dataset name, and tcloud will help place the dataset access in TACC_USERDIR. You can view the table of CityNet datasets at CityNet Dataset Info.

  datasets:
    - OpenRoadMap

• TACC_WORKDIR: TACC job workspace directory. Each job has a different workspace directory.
• TACC_USERDIR: TACC User directory.
• TACC_SLURM_USERLOG: Slurm log directory. The default value is ${TACC_USERDIR}/slurm_log.

Basic examples are provided under the example folder. These examples include: HelloWorld, TensorFlow, PyTorch and MXNet.