Package: lightning-training-studio
PyTorch Lightning Training Studio AppLightning Gallery•Docs•ExamplesThePyTorch Lightning Training Studio Appis a full-stack AI application built using theLightningframework to enable running experiments or sweeps with state-of-the-art sampling hyper-parameters algorithms and efficient experiment pruning strategies and more.Learn morehere.InstallationCreate a new virtual environment with python 3.8+python-mvenv.venvsource.venv/bin/activateClone and install lightning-hpo.gitclonehttps://github.com/Lightning-AI/lightning-hpo&&cdlightning-hpo

pipinstall-e.-rrequirements.txt--find-linkshttps://download.pytorch.org/whl/cpu/torch_stable.html--preMake sure everything works fine.python-mlightningrunappapp.pyCheck thedocumentationto learn more !Run the Training Studio App locallyIn your first terminal, run the Lightning App.lightningrunappapp.pyIn second terminal, connect to the Lightning App and download its CLI.lightningconnectlocalhost--yeslightning--help

Usage:lightning[OPTIONS]COMMAND[ARGS]...--helpShowthismessageandexit.

LightningAppCommands
Usage:lightning[OPTIONS]COMMAND[ARGS]...--helpShowthismessageandexit.

LightningAppCommandscreatedataCreateaDataassociationbyprovidingapublicS3bucketandanoptionalmountpoint.Thecontentsofthebucketcanbethenmountedonexperimentsandsweepsandaccessedthroughthefilesystem.deletedataDeleteadataassociation.Notethatthiswillnotdeletethedataitself,itwillonlymakeitunavailabletoexperimentsandsweeps.deleteexperimentDeleteanexperiment.Notethatartifactswillstillbeavailableaftertheoperation.deletesweepDeleteasweep.Notethatartifactswillstillbeavailableaftertheoperation.downloadartifactsDownloadartifactsforexperimentsorsweeps.runexperimentRunanexperimentbyprovidingascript,thecloudcomputetypeandoptionaldataentriestobemadeavailableatagivenpath.runsweepRunasweepbyprovidingascript,thecloudcomputetypeandoptionaldataentriestobemadeavailableatagivenpath.Hyperparameterscanbeprovidedaslists(`model.lr="[0.01, 0.1]"`)orusingdistributions(`model.lr="uniform(0.01, 0.1)"`,`model.lr="log_uniform(0.01, 0.1)"`).Hydramultirunoverridesyntaxisalsosupported.showartifactsShowartifactsforexperimentsorsweeps,inflatortreelayout.showdataListalldataassociations.showexperimentsShowexperimentsandtheirstatuses.showlogsShowlogsofanexperimentorasweep.Optionallyfollowlogsastheystream.showsweepsShowallsweepsandtheirstatuses,ortheexperimentsforagivensweep.stopexperimentStopanexperiment.Notethatcurrentlyexperimentscannotberesumed.stopsweepStopallexperimentsinasweep.Notethatcurrentlysweepscannotberesumed.

YouareconnectedtothelocalLightningApp.ReturntotheprimaryCLIwith`lightningdisconnect`.Run your first Sweep fromsweep_examples/scriptsfolderlightningrunsweeptrain.py--model.lr"[0.001, 0.01, 0.1]"--data.batch"[32, 64]"--algorithm="grid_search"--requirements'jsonargparse[signatures]>=4.15.2'Scale by running the Training Studio App in the CloudBelow, we are about to train a 1B+ LLM Model with multi-node.lightningrunappapp.py--cloudConnect to the App once ready.lightning connect {APP_NAME} --yesRun your first  multi node training experiment fromsweep_examples/scriptsfolder (2 nodes of 4 V100 GPUS each).lightningrunexperimentbig_model.py--requirementsdeepspeedlightning-transformers==0.2.5--num_nodes=2--cloud_compute=gpu-fast-multi--disk_size=80