Add GPU support and fix logging

docker/grpc-base-cpu.dockerfile

@@ -24,5 +24,5 @@ RUN poetry install --only-root
 WORKDIR /opt/stalactite
 ENV GIT_PYTHON_REFRESH="quiet"
 LABEL framework="stalactite"
-
+COPY ./plugins /opt/plugins
 # docker build -f ./docker/grpc-base.dockerfile -t grpc-base:latest .

docker/grpc-base.dockerfile

@@ -24,5 +24,5 @@ WORKDIR /opt/stalactite
 ENV GIT_PYTHON_REFRESH="quiet"
 ENV CUDA_DEVICE_ORDER="PCI_BUS_ID"
 LABEL framework="stalactite"
-
+COPY ./plugins /opt/plugins
 # docker build -f ./docker/grpc-base.dockerfile -t grpc-base:latest .

docs/tutorial.rst

@@ -20,6 +20,7 @@ And to launch distributed multinode or multiprocess experiment go to :ref:`distr
     tutorials/distr_communicator_tutorial
     tutorials/inference_tutorial
     tutorials/batching_tutorial
+    tutorials/plugins
     tutorials/master_types
 
 

docs/tutorials/plugins.rst

@@ -0,0 +1,40 @@
+.. _plugins_tutorial:
+
+*how-to:* Implement your own ML-algorithm (plugin)
+====================================================
+
+In the :ref:`master_types`, the implemented algorithms are listed. If you want to incorporate your own
+logic into the framework, you should write the agents classes furnished with the specifications on your algorithm.
+For the framework to find the plugins you write, you should create (or use existing) folder `plugins`
+alongside the sourcecode of the `Stalactite`.
+
+
+
+.. code-block:: bash
+
+    |-- ...
+    |-- plugins
+    |-- stalactite
+    `-- ...
+
+In the `plugins` folder, create a folder containing your agents. The name of this folder does not matter, but
+it is important for the agent implementation discovery to name your files correctly:
+
+- the master class implementation should be placed in a file named: `party_master.py`
+- the member class implementation should be placed in a file named: `party_member.py`
+- the arbiter (if implemented) should be placed in a file named: `party_arbiter.py`
+
+We have copied the honest logistic regression implementation into the repository `plugins` folder for you to see as the example.
+
+At runtime, to use the plugin in the experiment, the configuration file must be adjusted accordingly. For example, to make the framework use
+the honest logistic regression implementation from the plugins folder, you should change the ``vfl_model.vfl_model_name``
+to the path from `plugins` to your directory with agents' files.
+
+
+.. code-block:: yaml
+
+    vfl_model:
+      vfl_model_name: plugins.logistic_regression
+
+After performing the aforementioned steps, the framework should be able to discover implemented agents and will use them
+in the experiment.

examples/configs/efficientnet-splitNN-mnist-multiprocess.yml

@@ -0,0 +1,64 @@
+common:
+  report_train_metrics_iteration: 1
+  report_test_metrics_iteration: 1
+  world_size: 2
+  experiment_label: experiment-efficientnet-mnist-local
+  reports_export_folder: "../../reports"
+  seed: 22
+
+vfl_model:
+  epochs: 2
+  batch_size: 250
+  eval_batch_size: 9000
+  vfl_model_name: efficientnet
+  is_consequently: False
+  use_class_weights: True
+  learning_rate: 0.01
+  do_train: True
+  do_predict: False
+  do_save_model: True
+  vfl_model_path: ../../saved_models/efficientnet_model
+
+prerequisites:
+  mlflow_host: 'node3.bdcl'
+  mlflow_port: '5555'
+
+master:
+  external_host: 'node3.bdcl'
+  run_mlflow: True
+  master_model_params: {
+    input_dim: 128,
+    dropout: 0.2,
+    num_classes: 10,
+  }
+  run_prometheus: False
+  port: "50051"
+  logging_level: 'debug'
+  disconnect_idle_client_time: 500.
+  recv_timeout: 3600.
+  cuda_visible_devices: "0"
+
+member:
+  member_model_params: {
+    width_mult: 0.1,
+    depth_mult: 0.1,
+  }
+  heartbeat_interval: 2.
+  logging_level: 'info'
+  recv_timeout: 3600.
+
+data:
+  dataset_size: 750
+  dataset: 'mnist'
+  host_path_data_dir: ../../data/sber_ds_vfl/mnist_efficientnet_multiclass
+  dataset_part_prefix: 'part_' # used in dataset folder structure inspection. Concatenated with the index of a party: 0,1,... etc.
+  train_split: "train_train" # name of the train split
+  test_split: "train_val" # name of the test split
+  features_key: "image_part_"
+  label_key: "label"
+  uids_key: "image_idx"
+
+docker:
+  docker_compose_command: "docker compose"
+  docker_compose_path: '../../prerequisites'
+  use_gpu: True

examples/configs/linreg-mnist-multiprocess.yml

@@ -1,66 +1,58 @@
 common:
-  experiment_label: experiment-vm
-  reports_export_folder: "../../reports"
   report_train_metrics_iteration: 1
   report_test_metrics_iteration: 1
   world_size: 2
+  experiment_label: test-experiment-mnist-local
+  reports_export_folder: ../../reports
+  seed: 22
 
 vfl_model:
-  epochs: 5
-  batch_size: 1000
+  epochs: 2
+  batch_size: 5000
+  eval_batch_size: 200
   vfl_model_name: linreg
   is_consequently: False
   use_class_weights: False
-  learning_rate: 0.01
-
-prerequisites:
-  mlflow_host: 'node3.bdcl'
-  mlflow_port: '5555'
-  prometheus_host: 'node3.bdcl'
-  prometheus_port: '9090'
-  grafana_port: '3001'
+  learning_rate: 0.2
+  do_train: True
+  do_predict: True
+  do_save_model: True
+  vfl_model_path: ../../saved_models/linreg_model
 
 data:
-  dataset_size: 1000
-  host_path_data_dir: ../../data/sber_ds_vfl/mnist_binary38_parts2
+  dataset_size: 5000
   dataset: 'mnist'
-  dataset_part_prefix: 'part_'
-  train_split: "train_train"
-  test_split: "train_val"
+  host_path_data_dir: ../../data/sber_ds_vfl/mnist_vfl_parts2
+  dataset_part_prefix: 'part_' # used in dataset folder structure inspection. Concatenated with the index of a party: 0,1,... etc.
+  train_split: "train_train" # name of the train split
+  test_split: "train_val" # name of the test split
   features_key: "image_part_"
   label_key: "label"
+  uids_key: "image_idx"
 
-grpc_server:
-  port: '50051'
-  max_message_size: -1
-  #  server_threadpool_max_workers: 10
-
+prerequisites:
+  mlflow_host: 'node3.bdcl'
+  mlflow_port: '5555'
 
 master:
   external_host: 'node3.bdcl'
-  run_mlflow: True
-  run_prometheus: True
+  run_prometheus: False
+  port: "50051"
   logging_level: 'debug'
-  disconnect_idle_client_time: 120.
-  #  time_between_idle_connections_checks: 3
-  #  recv_timeout: 360
+  disconnect_idle_client_time: 500.
+  recv_timeout: 3600.
+  cuda_visible_devices: "0"
 
 member:
-  logging_level: 'debug'
+  member_model_params: {
+    output_dim: 1,
+    reg_lambda: 0.5
+  }
   heartbeat_interval: 2.
-  #  heartbeat_interval: 2
-  #  sent_task_timout: 3600
+  logging_level: 'info'
+  recv_timeout: 3600.
 
 docker:
   docker_compose_command: "docker compose"
   docker_compose_path: '../../prerequisites'
-  use_gpu: False
-
-
-#grpc_arbiter:
-#  use_arbiter: False
-
-
-
-
-
+  use_gpu: True

examples/configs/mlp-splitNN-sbol-smm-multiprocess.yml

@@ -0,0 +1,62 @@
+common:
+  report_train_metrics_iteration: 10
+  report_test_metrics_iteration: 10
+  world_size: 2
+  experiment_label: experiment-mlp-sbol-smm-local
+  reports_export_folder: "../../reports"
+  seed: 22
+
+vfl_model:
+  epochs: 2
+  batch_size: 250
+  eval_batch_size: 200
+  vfl_model_name: mlp
+  is_consequently: False
+  use_class_weights: True
+  learning_rate: 0.01
+  do_train: True
+  do_predict: False
+  do_save_model: True
+  vfl_model_path: ../../saved_models/mlp_model
+
+prerequisites:
+  mlflow_host: 'node3.bdcl'
+  mlflow_port: '5555'
+
+master:
+  external_host: 'node3.bdcl'
+  run_mlflow: True
+  master_model_params: {
+    input_dim: 100,
+    output_dim: 19,
+    multilabel: True,
+  }
+  run_prometheus: False
+  port: "50051"
+  logging_level: 'debug'
+  disconnect_idle_client_time: 500.
+  recv_timeout: 3600.
+  cuda_visible_devices: "0"
+
+member:
+  member_model_params: {
+    hidden_channels:[1000, 300, 100],
+  }
+  heartbeat_interval: 2.
+  logging_level: 'info'
+  recv_timeout: 3600.
+
+data:
+  dataset_size: 10000
+  dataset: 'sbol_smm'
+  host_path_data_dir:  ../../data/sber_ds_vfl/multilabel_sber_sample10000_smm_parts2
+  dataset_part_prefix: 'part_'
+  train_split: "train_train"
+  test_split: "train_val"
+  features_key: "features_part_"
+  label_key: "labels"
+
+docker:
+  docker_compose_command: "docker compose"
+  docker_compose_path: '../../prerequisites'
+  use_gpu: True

examples/configs/resnet-splitNN-sbol-smm-multiprocess.yml

@@ -0,0 +1,61 @@
+common:
+  report_train_metrics_iteration: 1
+  report_test_metrics_iteration: 1
+  world_size: 2
+  experiment_label: experiment-resnet-sbol-smm-local
+  reports_export_folder: "../../reports"
+
+vfl_model:
+  epochs: 2
+  batch_size: 2500
+  eval_batch_size: 2000
+  vfl_model_name: resnet
+  is_consequently: False
+  use_class_weights: False
+  learning_rate: 0.01
+  do_train: True
+  do_predict: False
+  do_save_model: True
+  vfl_model_path: ../../saved_models/resnet_model
+
+prerequisites:
+  mlflow_host: 'node3.bdcl'
+  mlflow_port: '5555'
+
+master:
+  external_host: 'node3.bdcl'
+  run_mlflow: True
+  master_model_params: {
+    input_dim: 1356,
+    output_dim: 19,
+    use_bn: True,
+  }
+  run_prometheus: False
+  port: "50051"
+  logging_level: 'debug'
+  disconnect_idle_client_time: 500.
+  recv_timeout: 3600.
+  cuda_visible_devices: "0"
+
+member:
+  member_model_params: {
+    hid_factor: [ 1, 1 ],
+  }
+  heartbeat_interval: 2.
+  logging_level: 'info'
+  recv_timeout: 3600.
+
+data:
+  dataset_size: 10000
+  dataset: 'sbol_smm'
+  host_path_data_dir: ../../data/sber_ds_vfl/multilabel_sber_sample10000_smm_parts2
+  dataset_part_prefix: 'part_'
+  train_split: "train_train"
+  test_split: "train_val"
+  features_key: "features_part_"
+  label_key: "labels"
+
+docker:
+  docker_compose_command: "docker compose"
+  docker_compose_path: '../../prerequisites'
+  use_gpu: True