Skip to main content

Train multiple programs on multiple servers without pain

Project description

Training Noodles

documentation_link travis_build_status

A simple and powerful tool to help training multiple programs on multiple servers with only one human.


  • Automatically deploys experiments to available servers
  • No need to change any existing code
  • Considers CPU usage, GPU usage, memory usage, disk usage, and more
  • Uses only SSH protocol
  • Relies on minimal dependencies
  • Allows fast prototyping

Use Case

If we want to run 4 experiments on 3 servers, more specifically, we need to

  1. Upload the code to one of the servers which has low CPU usage
  2. Run the code on the server
  3. Download experimental results when they're ready


In the first deployment round (See image above), Noodles will use the user-defined commands to check CPU usage on the servers.

The CPU usage is high on Server 1 because there are some other programs running, so Noodles uses scp to upload the code Code 1 and Code 2, and run them on Server 2 and Server 3 respectively.

As for how to upload the code, it's just a list of commands written by us, Noodles just follows the commands.


In the second deployment round (See image above), we tell Noodles to check experimental results on all servers.

Noodles finds that Server 3 has just finished running Code 2, so it downloads the experimental results and process the data on local machine as we tell it to do so.


In the third deployment round (See image above), Code 3 and Code 4 still need to be deployed. Noodles checks the CPU usage on all servers again. As Server 1 has just become free now, Noodles can deploy Code 3 and Code 4 to Server 1 and Server 3 respectively.

The deployment round would continue until all experiments are successfully deployed. As in this case, Noodles will try to download and process the experimental results of Code 1, Code 3 and Code 4 in later rounds.

How Noodles Works

The general procedure is as follows:

  1. Initialize the list of experiments in E
  2. For each deployment round:
    1. Initialize the list of servers in S
    2. For each experiment in E:
      1. Noodles runs user-defined requirements on each server in S
      2. Noodles compares the metrics (results from the above step) to the user-defined expression
      3. If the expression is satisfied:
        1. Noodles runs the user-defined commands on the satisfied server
        2. Remove the current experiment from E
        3. Remove the satisfied server from S
        4. If S is empty, break
    3. If E is empty, break

The implementation of Noodles complies with the following rules:

  1. Simple (User can understand code and spec without looking documentation)
  2. Easy to debug (Noodles can take different actions when different error occurs)
  3. Stateless (The only state Noodles cares about is whether the deployment is successful or not, the states of the experiments must be handled by the user)


See full documentation here.


  1. Linux-based terminals (For Windows, I recommend using git-sdk)
  2. Python 3.5 or higher


Run the following command:

pip install training-noodles


noodles <command_type> <path_to_spec>

It's just that simple.


Here are some examples showing how Noodles is used:

noodles run my_training.yml
noodles status my_training.yml
noodles monitor my_training.yml
noodles stop my_training.yml
noodles download my_training.yml
noodles upload my_training.yml

You can also choose only some experiments:

noodles run "my_training.yml:Experiment 1,Experiment 2"

See the example Two Locals to get started. See Train TensorFlow Examples for a more complex example.

Default Spec

Noodles will use properties from default spec if the user spec doesn't specify them. See training_noodles/specs/defaults.yml for the default spec.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

training-noodles-1.2.2.tar.gz (33.6 kB view hashes)

Uploaded source

Built Distribution

training_noodles-1.2.2-py3-none-any.whl (35.8 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page