fix(executor): override default resources to remove mem/disk (#91)

Closes #90

reana_workflow_engine_snakemake/executor.py

@@ -21,6 +21,7 @@
 from snakemake.common import async_lock
 from snakemake.executors import ClusterExecutor, GenericClusterExecutor
 from snakemake.jobs import Job
+from snakemake.resources import DefaultResources
 from snakemake import scheduler  # for monkeypatch
 
 from reana_workflow_engine_snakemake.config import (
@@ -251,14 +252,27 @@ def run_jobs(
 ):
     """Run Snakemake jobs using custom REANA executor."""
 
-    def _generate_report(workflow_file_path):
+    workflow_file_path = os.path.join(workflow_workspace, workflow_file)
+
+    common_snakemake_args = dict(
+        snakefile=workflow_file_path,
+        config=workflow_parameters,
+        workdir=workflow_workspace,
+        keep_logger=True,
+        # Since Snakemake v7.3.0, if default resources are not specified when in
+        # cluster mode, then `mem_mb` and `disk_mb` are also added to the resources
+        # of each snakemake rule. This is misleading for the users, as they would see
+        # in the logs wrong disk/memory limits, which are not actually applied to their
+        # workflows. For this reason, default resources are here overridden with only the
+        # "bare" ones, that is only `tmpdir`.
+        default_resources=DefaultResources(mode="bare"),
+    )
+
+    def _generate_report():
         """Generate HTML report."""
         success = snakemake(
-            workflow_file_path,
-            config=workflow_parameters,
-            workdir=workflow_workspace,
+            **common_snakemake_args,
             report=operational_options.get("report", DEFAULT_SNAKEMAKE_REPORT_FILENAME),
-            keep_logger=True,
         )
         if not success:
             log.error("Error generating workflow HTML report.")
@@ -269,21 +283,17 @@ def _generate_report(workflow_file_path):
     # Monkeypatch GenericClusterExecutor class in `scheduler` module
     scheduler.GenericClusterExecutor = REANAClusterExecutor
 
-    workflow_file_path = os.path.join(workflow_workspace, workflow_file)
     success = snakemake(
-        workflow_file_path,
+        **common_snakemake_args,
         printshellcmds=True,
         # FIXME: Can be anything as it's not directly used. It's supposed
         # to be the shell command to submit to job e.g. `condor_q`,
         # but we call RJC API client instead.
         cluster="reana",
-        config=workflow_parameters,
-        workdir=workflow_workspace,
         notemp=True,
         nodes=SNAKEMAKE_MAX_PARALLEL_JOBS,  # enables DAG parallelization
-        keep_logger=True,
     )
     # Once the workflow is finished, generate the report,
     # taking into account the metadata generated.
-    _generate_report(workflow_file_path)
+    _generate_report()
     return success