Manage runs

Create runs

Run is the main object that tracks and stores ML training metadata(e.g. metrics or hyperparams).

When initializing a Run object, Aim creates a .aim repository at the specified path. The tracked data is stored in the .aim repo. If the repo path is not specified, the run data is stored in the current working directory.

Use the following Run arguments to:

  • repo: define where to store the data

  • experiment: define experiment name to group related runs together

  • system_tracking_interval: Enable system resource usage tracking (CPU, GPU, memory, etc..). By default enabled. Set to None to disable

  • capture_terminal_logs: Enable process output capturing. By default enabled. Set to False to disable.

from aim import Run

my_run = Run(
    repo='/repo/path/to/store/runs',
    experiment='experiment_name'
)

Run class full spec.

Additionally, Aim SDK also gives a flexibility to:

  • Use multiple Runs in one training script to store multiple runs at once. Usually handy when doing hyperparameter search.

  • Use integrations to automate tracking

Continue runs

Each Run object has a hash associated with it which could be looked up at aim runs ls (check out the Aim CLI here). Specify the run hash when initializing a Run object to continue tracking.

from aim import Run

run = Run(run_hash='run_hash')

Delete runs

There are cases when Run data is not needed. Examples of such cases are, failed training runs or simple disk space cleanup. Aim provides SDK and CLI interfaces to delete Runs.

To remove Runs via the SDK:

from aim import Repo

repo = Repo.from_path('aim_repo_path')
repo.delete_run('run_hash')
repo.delete_runs(['run_hash_1', 'run_hash_2'])

Repo class full spec.

To remove Runs using command line:

aim runs rm run_hash_1 run_hash_2 run_hash_3

More details on aim runs in CLI reference.