Release Notes#

BioNeMo Framework v1.2#

New Models#

  • OpenFold implementation under BioNeMo framework, derived from public OpenFold and DeepMind AlphaFold-2.

  • DNABERT implementation for computing embeddings for each nucleotide in the input DNA sequence.

New Features#

  • Training recipes for DNABERT and OpenFold, including automated data processing and full configuration for training.

  • Example tutorials for running inference using OpenFold.

  • Splice Prediction downstream task example for DNABERT.

  • Wrapper scripts for DNABERT and OpenFold to launch jobs on BCP.

Bug fixes and Improvements#

  • Interface changes for ESM2 data ingestion and pre-processing: To allow useage of separate datasets for train/validation/test datasets, config.model.data.dataset_path has been deprecated and replaced with config.model.data.train.dataset_path, config.model.data.val.dataset_path and config.model.data.test.dataset_path.

Experimental Code#

  • There is an enformer model that is currently work in progress in bionemo/model/dna/enformer and examples/dna/enformer.

Known Issues#

  • OpenFold training speed does not yet include MLPerf optimizations, and these will be released in the subsequent release.

  • The container contains a known vulnerability which is exposed when using Apache Subversion (SVN).

  • The container contains five other high risk vulnerabilities associated with the NGC CLI which is used to download models from the NGC Registry. The BioNeMo Framework container is a means of shipping a functional development environment, not as a production service container. A full vulnerability report can always be found on the NGC Registry.

BioNeMo Framework v1.1#

New Models#

  • EquiDock for protein-protein docking pose prediction

  • DiffDock for protein-ligand blind docking pose generation

New Features#

  • Training recipes for EquiDock and DiffDock, including automated data processing and full configuration for training.

  • Accelerated inference and training for DiffDock via fast tensor-product kernels.

  • Example tutorials for running inference using EquiDock and DiffDock.

  • Recipes for running EquiDock and DiffDock on BCP and Slurm.

  • Pipeline parallel supported for ESM-2nv.

  • Migration of inference notebooks to using pytriton.

Bug fixes and Improvements#

  • Faster pre-processing of data on BCP.

  • Refactor of download_models.sh to download_models.py for easier CLI use.

  • Refactor of install structure to move from /opt/nvidia to /workspace/bionemo. The environment variable $BIONEMO_HOME now points to the repo base and is required to be set for tests to pass.

Security Notice#

SchedMD Slurm in the release container is shipped with a security vulnerability, CVE-2022-29501, and therefore this version of Slurm should not be used to run a Slurm cluster (specifically, the processes slurmdbd, slurmctld, and slurmd.

In general, the BioNeMo Framework release is designed to ship code and an environment that would be executed on local workstations, or deployed on clusters for large scale training jobs. This container is not designed to run as a service with public facing APIs. A full summary of security vulnerabilities can be found here.

BioNeMo Framework v1.0#

New Models#

  • ESM-2nv for protein sequence representations, pretrained weights of ESM-2 650M and ESM-2 3B converted from HF checkpoint available.

New Features#

  • Pre-training recipes for ESM-2nv, including automated data processing and full configuration for training

  • Fine-tuning of ESM-2nv with encoder frozen or trainable

  • Downstream task finetuning support for single-value classification (e.g. subcellular localization), single-value regression (e.g. meltome) and per-token classification (e.g. secondary structure)

  • Validation in loop to evaluate performance on downstream tasks during training

  • Example tutorials for pre-training, fine tuning, and downstream tasks

BioNeMo Framework v0.4.0#

New Models#

  • ESM-1nv for protein sequence representations, pretrained weights available

  • ProtT5nv for protein sequence representation and sequence-to-sequence tasks, pretrained weights available

New Features#

  • Pre-training for all models, including automated data processing and full configuration for training

  • Fine-tuning of MegaMolBART, ESM-1nv, and ProtT5nv with encoder frozen or trainable

  • Downstream task example applications – secondary structure prediction for ESM-1nv and ProtT5nv, physchem prediction (lipophilicity, FreeSolv, ESOL) and retrosynthesis prediction for MegaMolBART

  • Validation in loop to evaluate performance on downstream tasks during training: physchem prediction (MegaMolBART) and secondary structure prediction (ESM-1nv and ProtT5nv).

  • Pipeline parallelism supported as a beta feature. Not fully tested.

  • Example notebooks for pre-training, fine tuning, and downstream tasks

Known Issues#

  • Data preprocessing on DGX Cloud is slow. Faster to do it on a local machine.

New APIs#

  • BioNeMoDataModule - Encapsulates dataset instantiation in bionemo models so that many different datasets can be used with the same model

  • EncoderFineTuning - Base class to facilitate implementation of downstream tasks built on embeddings from other models