The above picture illustrates the submission and debug workflows of TACC job.

Before using tcloud SDK, please make sure that you have applied for a TACC account and submitted your public key to TACC. You may generate SSH public key according to the steps. To apply for a TACC account, please visit our website .

• Download tcloud SDK
  Download the latest tcloud SDK from tags.
• Install tcloud SDK
  Place setup.sh and tcloud in the same directory, and run setup.sh.

• First, 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
  

• Then, run tcloud init command to obtain the latest cluster hardware information from TACC cluster.

  PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST
  tacc*        up   infinite      5  alloc 10-0-7-[18-19],10-0-8-[18-19]
  tacc*        up   infinite     19   idle 10-0-2-[18-19],10-0-3-[10-13]
  

You can use this link to download our example code.

Each job requires a main.py with tuxiv.conf

• main.py: Your machine learning training code.

• tuxiv.conf: Detail about tuxiv.conf

After tcloud is configured correctly, you can try to submit your first job.

1. Go to the example folder in your terminal.
2. Run tcloud submit command.

   ~/Dow/quickstart-master/example/helloworld ❯ tcloud submit
   Start parsing tuxiv.conf...
   building file list ...
   8 files to consider
   helloworld/
   helloworld/run.sh
           151 100%    0.00kB/s    0:00:00 (xfer#1, to-check=5/8)
   helloworld/configurations/
   helloworld/configurations/citynet.sh
             12 100%   11.72kB/s    0:00:00 (xfer#2, to-check=2/8)
   helloworld/configurations/conda.yaml
           107 100%  104.49kB/s    0:00:00 (xfer#3, to-check=1/8)
   helloworld/configurations/run.slurm
           278 100%  271.48kB/s    0:00:00 (xfer#4, to-check=0/8)
   
   sent 429 bytes  received 144 bytes  382.00 bytes/sec
   total size is 1071  speedup is 1.87
   Submitted batch job 2000
   Job helloworld submitted.
   

In this section, we provide two methods to monitor the job log.

After training, you can use tcloud ls [filepath] to find the output files

• cat

  You can configure your log path in the tuxiv.conf. The default path is slurm_log/slurm-jobid.out.

  tcloud cat slurm_log/slurm-jobid.out
  

  In the helloworld example, the tuxiv.conf file specifies the log path as slurm_log/hello.log

• download

  You can use tcloud download [filepath].

  Note that you can only read and download files in USERDIR, and the files in WORKDIR may be removed after the job is finished.

  tcloud download slurm_log/slurm-jobid.out
  

tcloud uses conda environment to manage your dependencies. We offer two ways of environment management:

1. One-off Environment. A new environment will be created every time you submit a task to TACC. If your dependencies configuration does not change between two consecutive submissions, we will reuse the previous environment to save time. This is the default behavior.
2. Persistent Environment. The environment will be shared across multiple task submissions. When you change your dependency configuration, tcloud will update this environment in stead of creating a new one. Learn how to do this in tuxiv.conf documentation

The following videos will help you use tcloud CLI to begin your TACC journey: demo video.

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

FAQ