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.
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