v1.7.0

What's Changed

The primary feature of the new release is that pytorch is now the main mode of training.
The CMS status was presented at https://indico.cern.ch/event/1399688/#1-ml-for-pf.

• switch pytorch training to tfds array-record datasets by @farakiko in #228
• Timing the ONNX model, retrain CMS-GNNLSH-TF by @jpata in #229
• fixes for pytorch, CMS t1tttt dataset, update response plots by @jpata in #232
• fix pytorch multi-GPU training hang by @farakiko in #233
• feat: specify number of samples as cmd line arg in pytorch training and testing by @erwulff in #237
• Automatically name training dir in pytorch pipeline by @erwulff in #238
• pytorch backend major update by @farakiko in #240
• Update dist.barrier() and fix stale epochs for torch backend by @farakiko in #249
• multi-bin loss in TF, plot fixes by @jpata in #234
• PyTorch distributed num-workers>0 fix by @farakiko in #252
• speedup of the pytorch GNN-LSH model by @jpata in #245
• Implement HPO for PyTorch pipeline. by @erwulff in #246
• fix tensorboard error by @farakiko in #254
• fix config files by @erwulff in #255
• making the 3d-padded models more efficient in pytorch by @jpata in #256
• Fix pytorch inference after #256 by @jpata in #257
• Update training.py by @jpata in #261
• Reduce the number of data loader workers per dataset in pytorch by @farakiko in #262
• fix inference by @farakiko in #264
• Implementing configurable checkpointing. by @erwulff in #263
• restore onnx export in pytorch by @jpata in #265
• remove outdated forward_batch from pytorch by @jpata in #266
• Separate multiparticlegun samples from singleparticle gun samples by @farakiko in #267
• compare all three models in pytorch by @jpata in #268
• Allows testing on a given --load-checkpoint by @farakiko in #269
• added clic evaluation notebook by @jpata in #272
• Fix --load-checkpoint bug by @farakiko in #270
• Implement CometML logging to PyTorch training pipeline. by @erwulff in #273
• Add command line argument to choose experiments dir in PyTorch training pipeline by @erwulff in #274
• Implement multi-gpu training in HPO with Ray Tune and Ray Train by @erwulff in #277
• Better CometML logging + Ray Train vs DDP comparison by @erwulff in #278
• Fix checkpoint loading by @erwulff in #280
• Learning rate schedules and Mamba layer by @erwulff in #282
• use modern optimizer, revert multi-bin loss in TF by @jpata in #253
• track individual particle loss components, speedup inference by @jpata in #284
• Update the jet pt threshold to be the same as the PF paper by @farakiko in #283
• towards v1.7: new CMS datasets, CLIC hit-based datasets, TF backward-compat optimizations by @jpata in #285
• fix torch no grad by @jpata in #290
• pytorch regression output layer configurability by @jpata in #291
• Implement resume-from-checkpoint in HPO by @erwulff in #293
• enable FlashAttention in pytorch, update to torch 2.2.0 by @jpata in #292
• fix pad_power_of_two by @jpata in #296
• Feat val freq by @erwulff in #298
• normalize loss, reparametrize network by @jpata in #297
• fix up configs by @jpata in #300
• clean up loading by @jpata in #301
• Fix unpacking for 3d padded batch, update plot style by @jpata in #306

Full Changelog: v1.6...v1.7.0