# New Version is Out!

**URL:** <https://melissa.discourse.group/t/new-version-is-out/49>\
**Category:** Software Updates\
**Created:** [June 17, 2025, 9:55am UTC](https://melissa.discourse.group/t/new-version-is-out/49 "2025-06-17T09:55:03Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![AbhishekP](https://avatars.discourse-cdn.com/v4/letter/a/aeb1de/32.png) [@AbhishekP](https://melissa.discourse.group/u/AbhishekP)\
**Post date:** [June 17, 2025, 9:55am UTC](https://melissa.discourse.group/t/new-version-is-out/49/1 "2025-06-17T09:55:03Z")

</div>

## Release Notes for Melissa v2.0.0

### Code Quality & Refactoring

- Added docstrings to all major files in `melissa/`.
- Added a `.nix/` folder containing files to spawn a nix devshell for quick Melissa testing.
- Refactored multiple components across `api/`, `melissa/`, and `examples/`:
  - Hid implementation details in user-inherited server classes for better usability.
  - Renamed variables for clarity and consistency.
  - Privatized internal attributes.
  - Added and improved type hinting across the codebase.
  - Enhanced code quality using `pylint`.
  - Rewrote configuration files to use clearer and more consistent keys.

### Configuration & Setup

- Removed hard-coded port numbers for client-server TCP connections:
  - Allows running multiple servers on the same node without port conflicts.
  - Introduced a retry strategy: if a port fails, server attempts the next one, up to a defined number of retries.

- Refactored the ZeroMQ CMake-based installation for cleaner integration.
- Dependencies can now be installed directly via `pyproject.toml` using pip:

```bash
pip install . # base dependencies
pip install .[dev] # development dependencies
pip install .[dl] # deep learning dependencies
pip install .[all] # all of the above

```

_Note: Manual installation via `requirements_*.txt` is still supported for environments._

### Performance & Usability Enhancements

- Added runtime feedback for users:
  - Logs average simulation duration every 10,000 samples.
  - Reports current memory usage.
  - Displays buffer memory usage (as `sample_size_in_bytes * buffer_size` in GB).

- Clients scripts are now generated on-the-fly at job submission time:
  - The server submits `job_limit` jobs and keeps submitting new ones as others complete.
  - Reduces unnecessary script creation and supports reactive resampling.

### Error Handling

- Created specific exception classes in `melissa.server.exceptions` for improved error reporting and traceability.
- Improved handling of failed or unexpected conditions across modules.

### New Features

- **New: `melissa.server.OfflineServer`**
  - Enables dataset creation from configuration studies without active server involvement during simulation.
  - Offline server principally samples parameters and submits client scripts.
  - Ideal for validation and offline training dataset generation.
  - Clients handle their own data saving. `melissa_send()` is not used.

- Added `melissa.utility.rank_helper`:
  - Provides helper functions and decorators for MPI operations.
  - Introduces `ClusterEnvironment` dataclass to retrieve and manage SLURM/OMPI environment variables.

* * *

## CI/CD

- Migrated to a dedicated machine on `ci.inria.fr` for running Melissa CI pipelines.
- Created a SLURM-compatible Docker cluster for local and CI testing.
- Added a manual version bumping stage in CI pipeline.
- Added consistency checks to compare results against the previous version using L2-norm metrics.
- Moved study run logic to `tests/ci` with modular bash scripts for maintainability.

* * *

## Parameter Sampling

- Replaced Python’s `random` module with NumPy’s `np.random` for consistent random number generation.
- Implemented static sampling using `np.memmap` for memory-efficient parameter access across MPI ranks.
- Introduced MixIn classes to enable reusable behaviors in custom parameter samplers.
- Extended `BaseExperiment` to support parameter manipulations in breed-style samplers.

* * *

## Deep Learning

- Refactored base classes by moving redundant logic to `BaseServer`.
- Users no longer need to manage buffer or dataset creation (instantiate objects manually) —this is handled based on configuration.
- Improved exception handling throughout DL modules.
- Separated dataset creation, dataloader, and tensorboard logging into dedicated modules.
- Improved imports to avoid loading unnecessary modules.
- Introduced a generic dataloader for iterable datasets in non-Torch/TensorFlow workflows.
- Added a framework-agnostic training loop with user-defined hook support, located in `melissa.server.deep_learning.train_workflow`.
- Introduced a new round-robin communication strategy:
  - Ensures all trajectory data from a single simulation stays on the same buffer.
  - Especially useful for DL workflows that require temporal coherence in training samples.

- Support for Parallel validation. _But, users must take care of reducing statistics per server rank._

* * *

## Sensitivity Analysis

- Refactored sensitivity analysis base classes.
- Fixed issues with Pearson arrays in `IterativeSensitivityMartinez`.
- Corrected internal use of `melissa_sobol.increment` to resolve result inconsistencies.
  - See: [IterativeStatistics issue #29](https://github.com/IterativeStatistics/BasicIterativeStatistics/issues/29)

- Disabled group data aggregation on rank 0 by hardcoding `sobol = 0` in the client API code—each client now sends data directly to the server (as in a non-sobol study).
- Introduced server-side Sobol caching:
  - Results are now cached using keys: `(client_rank, time_step, field)`.
  - Only computes Sobol statistics when all required data is received for a group.
  - This strategy minimizes client-side communication and leverages the typically higher memory availability on server processes, making caching more effective.

- Fixed data gathering issues:
  - Replaced P2P calls with `MPI_Gatherv`.
  - Aggregates results field-by-field for improved accuracy and scalability.