download the new version for apple Aim Assist

With tensorboard the users are forced to record those parameters in the training run name to be able to search and compare.

You can search, group, aggregate via params - deeply explore all the tracked data (metrics, params, images) on the UI.

The tracked params are first class citizens at Aim.

Order of magnitude faster training run comparison with Aim Read the full documentation on 📖 🆚 Comparisons to familiar tools TensorBoard vs Aim

A guide on how to deploy Aim on kubernetes:.

See the docs on how to set up the remote server. sparse_numpy () Set up a centralized tracking serverĪim remote tracking server allows running experiments in a multi-host environment and collect tracked data in a centralized location. run # Get metric values steps, metric_values = metric. iter_runs (): for metric in run_metrics_collection : # Get run params params = metric. from aim import Repo my_repo = Repo ( '/path/to/aim/repo' ) query = 'metric.name = 'loss'' # Example query # Get collection of metrics for run_metrics_collection in my_repo. In case of custom and complex scenarios you can use Aim SDK to implement your own conversion script.Īim easily integrates with a wide range of ML frameworks, providing built-in callbacks for most of them.Īim Python SDK empowers you to query and access any piece of tracked metadata with ease. These migrations cover the most common usage scenarios. Run the training as usual and start Aim UI aim upĪim has built-in converters to easily migrate logs from other tools. See the full list of supported trackable objects(e.g. Integrate Aim with your code from aim import Run # Initialize a new run run = Run () # Log run parameters run = ) Install Aim on your training environment pip3 install aimĢ.

aimlflowĮxploring MLflow experiments with a powerful UIįollow the steps below to get started with Aim. It's a groundwork for an ecosystem.Ĭheck out the two most famous Aim-based tools. Training logs of Microsoft's 'FastSpeech 2: Fast and High-Quality End-to-End Text to Speech'.Īim is not just an experiment tracker. Training logs of 'lightweight' GAN, proposed in ICLR 2021.

Training logs of a neural translation model(from WMT'19 competition). Runs grouping with tags and experimentsĬheck out live Aim demos NOW to see it in action.Centralized dashboard for holistic view.Detailed run information for easy debugging.Real-time alerting on training progress.System info and resource usage tracking.Metadata visualization via Aim Explorers.Easy migration from other experiment trackers.

ML experiments and any metadata tracking.

Download the new version for apple Aim Assist