Updated storage and training files to enable text generation tasks. A…

…dditionally, made minor updates to the environment and ensured everything passes the standard Python compliance tests, maintaining compatibility with Modyn's existing functionality.

benchmark/mnist/mnist.yaml

@@ -12,6 +12,7 @@ model_storage:
 training:
   gpus: 1
   device: "cuda:0"
+  generative: False
   dataloader_workers: 2
   use_previous_model: True
   initial_model: random

benchmark/wildtime_benchmarks/example_pipelines/arxiv.yaml

@@ -17,6 +17,7 @@ training:
   initial_model: random
   batch_size: 128
   shuffle: True
+  generative: False
   optimizers:
     - name: "default"
       algorithm: "SGD"

benchmark/wildtime_benchmarks/example_pipelines/data_drift_trigger/arxiv_datadrift.yaml

@@ -13,6 +13,7 @@ training:
   gpus: 1
   device: "cuda:0"
   dataloader_workers: 2
+  generative: False
   use_previous_model: True
   initial_model: random
   batch_size: 96

benchmark/wildtime_benchmarks/example_pipelines/data_drift_trigger/huffpost_datadrift.yaml

@@ -13,6 +13,7 @@ training:
   gpus: 1
   device: "cuda:0"
   dataloader_workers: 2
+  generative: False
   use_previous_model: True
   initial_model: random
   batch_size: 64

benchmark/wildtime_benchmarks/example_pipelines/data_drift_trigger/yearbook_datadrift.yaml

@@ -14,6 +14,7 @@ training:
   gpus: 1
   device: "cuda:0"
   dataloader_workers: 2
+  generative: False
   use_previous_model: True
   initial_model: random
   batch_size: 64

benchmark/wildtime_benchmarks/example_pipelines/fmow.yaml

@@ -13,6 +13,8 @@ training:
   gpus: 1
   device: "cuda:0"
   dataloader_workers: 2
+  generative: False
+
   use_previous_model: True
   initial_model: random
   batch_size: 64

benchmark/wildtime_benchmarks/example_pipelines/huffpost.yaml

@@ -12,6 +12,7 @@ model_storage:
 training:
   gpus: 1
   device: "cuda:0"
+  generative: False
   dataloader_workers: 2
   use_previous_model: True
   initial_model: random

benchmark/wildtime_benchmarks/example_pipelines/yearbook.yaml

@@ -14,6 +14,7 @@ training:
   gpus: 1
   device: "cuda:0"
   dataloader_workers: 2
+  generative: False
   use_previous_model: True
   initial_model: random
   batch_size: 64

environment.yml

@@ -30,7 +30,7 @@ dependencies:
   - psycopg2
   - sqlalchemy>=2.0
   - pyaml
-  - pydantic
+  - pydantic==2.9.2
   - numpy==1.26.*
   - pandas
   - bitstring
@@ -43,11 +43,10 @@ dependencies:
   - nltk
   - pytorch::pytorch=2.2.1
   - pytorch::torchvision
-  - pytorch::cpuonly # comment out if commenting in lines below for CUDA
-#  - pytorch::pytorch-cuda=12.1
-#  - nvidia::cuda-libraries-dev=12.1.*
-#  - nvidia::cuda-nvcc=12.1.*
-#  - nvidia::cuda-nvtx=12.1.*
-#  - nvidia::cuda-cupti=12.1.*
-#  - nvidia::cuda-cudart-dev=12.1.*
-#  - nvidia::cuda-profiler-api=12.1.*
+  - pytorch::pytorch-cuda=12.1
+  - nvidia::cuda-libraries-dev=12.1.*
+  - nvidia::cuda-nvcc=12.1.*
+  - nvidia::cuda-nvtx=12.1.*
+  - nvidia::cuda-cupti=12.1.*
+  - nvidia::cuda-cudart-dev=12.1.*
+  - nvidia::cuda-profiler-api==12.1.*

integrationtests/config/dummy.yaml

@@ -12,6 +12,7 @@ model_storage:
 training:
   gpus: 1
   device: "cpu"
+  generative: False
   dataloader_workers: 1
   use_previous_model: True
   initial_model: random

integrationtests/config/rho_loss.yaml

@@ -13,6 +13,7 @@ training:
   gpus: 1
   device: "cpu"
   dataloader_workers: 2
+  generative: False
   use_previous_model: False
   initial_model: random
   batch_size: 4
@@ -60,6 +61,7 @@ selection_strategy:
       il_model_config:
         num_classes: 10
       device: "cpu"
+      generative: False
       dataloader_workers: 1
       use_previous_model: False
       batch_size: 2
@@ -75,4 +77,4 @@ selection_strategy:
                 lr: 0.1
                 momentum: 0.001
       optimization_criterion:
-        name: "CrossEntropyLoss"
+        name: "CrossEntropyLoss"

modyn/common/grpc/grpc_helpers.py

@@ -251,6 +251,7 @@ def prepare_start_training_request(
             enable_accurate_gpu_measurements=training_config.enable_accurate_gpu_measurements,
             record_loss_every=training_config.record_loss_every,
             drop_last_batch=training_config.drop_last_batch,
+            generative=training_config.generative,
         )
 
     def start_training(

modyn/config/examples/modyn_config.yaml

@@ -278,7 +278,7 @@ selector:
   local_storage_directory: "/tmp/local_storage"
   local_storage_max_samples_in_file: 1000000
   cleanup_storage_directories_after_shutdown: true
-  ignore_existing_trigger_samples: false
+  ignore_existing_trigger_samples: true
 
 trainer_server:
   hostname: "trainer_server"

modyn/config/schema/pipeline/training/config.py

@@ -119,6 +119,11 @@ class TrainingConfig(ModynBaseModel):
             "we start with random weights. If initial_model is 'pretrained', cannot be False."
         )
     )
+    generative: bool = Field(False,
+        description=(
+            "If True then, then the training pipeline goes into the generative branch, data is sampled without expecting labels."
+        )
+    )
     seed: int | None = Field(
         None,
         description=(

modyn/config/schema/system/config.py

@@ -255,7 +255,7 @@ class SelectorConfig(HostnamePortMixin):
         ),
     )
     ignore_existing_trigger_samples: bool = Field(
-        False,
+        True,
         description=(
             "Whether to ignore existing trigger samples when starting the selector. If set to false, the trigger "
             "sample directory has to be empty upon startup. May lead to unexpected behaviour if set to true and the "