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Documentation Index

Fetch the complete documentation index at: https://docs.trainy.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Hello Pluto

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Prerequisites

  • git - required for sending commit authorship and other metadata

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Launch Your First Experiement

Start logging your first experiment in 4 steps:
  1. Create an account/organization at https://pluto.trainy.ai
  2. Install the Python SDK. In your Python environment, install the latest build 
    pip install "pluto-ml-nightly[full]"
    or the release build 
    pip install "pluto-ml"
  3. If you haven’t already, request an API key through the terminal via: 
    import pluto
pluto.login()
    or login via the shell command 
    pluto login <API_KEY>
  4. Log your first experiment! 
    # hello_pluto.py
import pluto

config = {'lr': 0.001, 'epochs': 1000}
run = pluto.init(project="pluto", name="experiment", config=config)

# insert custom model training code
for i in range(config['epochs']):
    pluto.log({"val/loss": 0})

run.finish()
The code will print out a url where you can view your current run. The web dashboard allows you to compare time series between runs. The output for the above script might be the following. 
(pluto) ubuntu@ip-111-111-111-111:~$ python hello_pluto.py
pluto: 🚀 19:52:01 | Authentication: logged in as trainy
pluto: 🚀 19:52:01 | Interface: find live updates at https://pluto.trainy.ai/o/myorg/projects/my-project/mzHx3
pluto: 🚀 19:52:06 | Interface: find 1001 synced entries at https://pluto.trainy.ai/o/myorg/projects/my-project/mzHx3