[22.04] - 2022-04-29

Added

• catalyst-tune for Config API added #1411
• tests for python 3.9 and 3.10 #1414

Fixed

• catalyst compatibility with python 3.10 #1409

[22.02.1] - 2022-02-27

"Few fixes and Config API v22 MVP".

Added

• catalyst-run for Config API support added #1406

Fixed

• Logger API naming #1405

[22.02] - 2022-02-13

Tl;dr

• Catalyst architecture simplification.
• #1395, #1396, #1397, #1398, #1399, #1400, #1401, #1402, #1403.

Added

• Additional tests for different hardware accelerators setups. Please check out the tests/pipelines folder for more information.
• BackwardCallback and BackwardCallbackOrder as an abstraction on top of loss.backward. Now you could easily log model gradients or transform them before OptimizerCallback.
• CheckpointCallbackOrder for ICheckpointCallback.

Changed

• Minimal python version moved to 3.7, minimal PyTorch version moved to 1.4.0.
• Engines were rewritten on top of Accelerate. First, we found these two abstractions very close to each other. Second, Accelerate provides additional user-friendly API and more stable API for "Nvidia APEX" and "Facebook Fairscale" - it does not support them.
• SelfSupervisedRunner moved to the examples folder from the Catalyst API. The only Runners API, that will be supported in the future: IRunner, Runner, ISupervisedRunner, SupervisedRunner due to their consistency. If you are interested in any other Runner API - feel free to write your own CustomRunner and use SelfSupervisedRunner as an example.
• Runner.{global/stage}_{batch/loader/epoch}_metrics renamed to Runner.{batch/loader/epoch}_metrics
• CheckpointCallback rewritten from scratch.
• Catalyst registry moved to full-imports-paths only.
• Logger API changed to receive IRunner for all log_* methods.
• Metric API: topk_args renamed to topk.
• Contrib API: init imports from catalyst.contrib - removed, use from catalyst.contrib.{smth} import {smth}. Could be change to full-imports-only in future versions for stability.
• All quickstarts, minimal examples, notebooks, and pipelines moved to the new version.
• Codestyle moved to 89 right margin. Honestly speaking, it's much easier to maintain Catalyst with 89 right margin on MBP'16.

Removed

• ITrial removed.
• Stages support removed. While we embrace stages in deep learning experiments, current hardware accelerators are not prepared well for such setups. Additionally, ~95% of dl pipelines are single-stage. Multi-stage runner support is under review. For multi-stage support, please define a CustomRunner with rewritten API.
• Config/Hydra API support removed. Config API is under review. For now, you could write your own Config API with hydra-slayer if needed.
• catalyst-dl scripts removed. Without Config API we don't need them anymore.
• Nvidia Apex, Fairscale, Albumentations, Nifti, Hydra requiremets removed.
• OnnxCallback, PruningCallback, QuantizationCallback, TracingCallback removed from callbacks API. These callbacks are under review now.

If you have any questions on the Catalyst 22 edition updates, please join Catalyst slack for discussion.

[22.02rc0] - 2022-02-07

Tl;dr

Beta version of Catalyst 22 edition.

• core architecture moved to Animus-like (stages were removed)
• engines moved to Accelerate
• config/hydra APIs deprecated in favor of hydra-slayer-custom config runners
• dl-based scripts removed from the API
• self-supervised runner moved to examples - it's better to have custom still
• contrib and utils - truncated
• requirements - simplified
• codestyle moved to -l 89 (better view on 16'' screen ;) )

[21.12] - 2021-12-28

Tl;dr

Distributed engines update (multi-node support) and many other improvements.

Added

• MNIST dataset for SSL banchmark (#1368)
• MoveiLens 20M dataset #1336
• logger property for logging customization (#1372)
• MacridVAE example (#1363)
• SSL benchmark results (#1374)
• Neptune example (#1377)
• multi-node support for engines (#1364)

Changed

• RL examples update to last version (#1370)
• DDPLoaderWrapper updated to new version (#1385)
• num_classes for classification metrics became optional (#1379)
• colab ci/cd update to new verion

Removed

Fixed

• requests requirements for catalyst[cv] added (#1371)
• loader step counter (#1374)
• detection example data preprocessing (#1369)
• gradient clipping with fp16 runs (#1378)
• config API fix for DDP runs (#1383)
• checkpoint creation for fp16 engines (#1382)

Contributors ❤️

@bagxi @ditwoo @MrNightSky @Nimrais @y-ksenia @sergunya17 @Thiefwerty @zkid18

[21.11] - 2021-11-30

Tl;dr

Framework architecture simplification and speedup + SSL & RecSys extensions.

Added

• MultiVAE RecSys example (#1340)`
• Returned resume support - resolved #1193 (#1349)
• Smoothing dice loss to contrib (#1344)
• profile flag for runner.train (#1348)
• MultiDAE RecSys example (#1356)
• SETTINGS.log_batch_metrics, SETTINGS.log_epoch_metrics, SETTINGS.compute_per_class_metrics for framework-wise Metric & Logger APIs specification (#1357)
• log_batch_metrics and log_epoch_metrics options for all available Loggers (#1357)
• compute_per_class_metrics option for all available multiclass/label metrics (#1357)
• pytorch benchmark script and simplified MNIST (#1360)

Changed

• A few framework simplifications were made (#1346):
  • catalyst-contrib scripts reduced to collect-env and project-embeddings only
  • catalyst-dl scripts recuded to run and tune only
  • transforms. prefix deprecated for Catalyst-based transforms
  • catalyst.tools moved to catalyst.extras
  • task-dependent extensions from catalyst.data moved to catalyst.contrib.data
  • catalyst.data.transforms moved to catalyst.contrib.data.transforms
  • Normalize, ToTensor transforms renamed to NormalizeImage, ImageToTensor
  • metric learning extensions moved to catalyst.contrib.data
  • catalyst.contrib moved to code-as-a-documentation development
  • catalyst[cv] and catalyst[ml] extensions moved to flatten architecture design; examples: catalyst.contrib.data.dataset_cv, catalyst.contrib.data.dataset_ml
  • catalyst.contrib moved to flatten architecture design; exampels: catalyst.contrib.data, catalyst.contrib.datasets, catalyst.contrib.layers, catalyst.contrib.models, catalyst.contrib.optimizers, catalyst.contrib.schedulers
  • internal functionality moved to ***._misc modules
  • catalyst.utils.mixup moved to catalyst.utils.torch
  • catalyst.utils.numpy moved to catalyst.contrib.utils.numpy
• default logging logic moved from "batch & epoch" to "epoch"-only to save computation time during logging; to respecify, please use:
  • SETTINGS.log_batch_metrics=True/False or os.environ["CATALYST_LOG_BATCH_METRICS"]
  • SETTINGS.log_epoch_metrics=True/False or os.environ["CATALYST_LOG_EPOCH_METRICS"]
• default metrics computation moved from "per-class & aggregations" to "aggregations"-only to save computation time during logging; to respecify, please use:
  • SETTINGS.compute_per_class_metrics=True/False or os.environ["CATALYST_COMPUTE_PER_CLASS_METRICS"]
• no transformations required for MNIST contrib dataset (#1360

Removed

• A few framework simplifications were made (#1346):
  • catalyst.contrib.pandas
  • catalyst.contrib.parallel
  • catalyst.contrib.models.cv
  • a few catalyst.utils.misc functions
  • catalyst.extras removed from the public documentation

Fixed

• documentation search error (21.10 only) (#1346)
• docs examples (#1362)
• Self-Supervised benchmark: (#1365), (#1361)

Contributors ❤️

@asteyo @Dokholyan @Nimrais @y-ksenia @sergunya17

[21.10] - 2021-10-30

Tl;dr

Readmes and tutorials with a few ddp fixes.

Added

• RSquareLoss (#1313)
• Self-Supervised example updates: (#1305), (#1322), (#1325), (#1335)
• Albert training example (#1326)
• YOLO-X (new) detection example and refactoring (#1324)
• TopKMetric asbtraction (#1330)

Changed

• simlified readme (#1312)
• improved DDP tutorial (#1327)
• CMCMetric renamed from <prefix>cmc<suffix><k> to <prefix>cmc<k><suffix> (#1330)

Removed

Fixed

• Zero seed error (#1329)
• updated codestyle issues (#1331)
• TopK metrics: (#1330), (#1334), (#1339)
• --expdir param for catalyst-dl run (#1338)
• ControlFlowCallback for distributed setup (#1341)

[21.09] - 2021-09-30

Added

• CometLogger support (#1283)
• CometLogger examples (#1287)
• XLA docs (#1288)
• Contarstive loss functions: NTXentLoss (#1278), SupervisedContrastiveLoss (#1293)
• Self supervised learning: ISelfSupervisedRunner, SelfSupervisedConfigRunner, SelfSupervisedRunner, SelfSupervisedDatasetWrapper (#1278)
• SimCLR example (#1278)
• Superivised Contrastive example (#1293)
• extra warnings for runner-callbacks interaction (#1295)
• CategoricalRegressionLoss and QuantileRegressionLoss to the contrib (#1295)
• R2 score metric (#1274)

Changed

• Improved WandbLogger to support artifacts and fix logging steps (#1309)
• full Runner cleanup, with callbacks and loaders destruction, moved to PipelineParallelFairScaleEngine only (#1295)
• HuberLoss renamed to HuberLossV0 for the PyTorch compatibility (#1295)
• codestyle update (#1298)
• BalanceBatchSampler - deprecated (#1303)

Removed

Fixed

• CI/CD (#1292), (#1299), (#1304), (#1306)
• Optuna configs (#1296), (#1296)

Contributors ❤️

@asteyo @AyushExel @bagxi @DN6 @gr33n-made @Nimrais @Podidiving @y-ksenia

Hi guys, nice project!

This is the test case release to check out our updated infrastructure.