nit to allow local docker

decoder_demos/sklearn_sample.Dockerfile

@@ -10,13 +10,14 @@ FROM pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
 RUN pwd
 RUN /bin/bash -c "python3 -m pip install falcon_challenge --upgrade"
 ENV PREDICTION_PATH "/tmp/submission.pkl"
-ENV GT_PATH = "/tmp/ground_truth.pkl"
+ENV GT_PATH "/tmp/ground_truth.pkl"
 
 # TODO ensure falcon_challenge available on dockerhub...
 
 # Users should install additional decoder-specific dependencies here.
 
-ENV EVALUATION_LOC remote
+# ENV EVALUATION_LOC remote
+ENV EVALUATION_LOC local
 
 # Add files from local context into Docker image
 # Note local context reference is the working dir by default, see https://docs.docker.com/engine/reference/commandline/build/
@@ -34,12 +35,13 @@ ADD ./decoder_demos/sklearn_sample.py decode.py
 ADD ./preproc/filtering.py filtering.py
 
 ENV SPLIT "h1"
-ENV PHASE "test"
+ENV PHASE "minival"
+# ENV PHASE "test"
 
-# Don't touch
+# Make sure this matches the mounted data volume path. Generally leave as is.
 ENV EVAL_DATA_PATH "/evaluation_data"
 
 # CMD specifies a default command to run when the container is launched.
 # It can be overridden with any cmd e.g. sudo docker run -it my_image /bin/bash
 CMD ["/bin/bash", "-c", \
-    "python decode.py --evaluation $EVALUATION_LOC --model-path data/decoder.pkl --split $TRACK --phase $PHASE"]
+    "python decode.py --evaluation $EVALUATION_LOC --model-path data/decoder.pkl --split $SPLIT --phase $PHASE"]

falcon_challenge/evaluator.py

@@ -77,10 +77,14 @@ def __init__(self, eval_remote=False, split='h1'):
 
     @staticmethod
     def get_eval_handles(is_remote: bool, dataset: FalconTask, phase: str = 'minival'):
-        if is_remote:
+        if is_remote: # i.e. definitely docker
             data_dir = os.environ.get("EVAL_DATA_PATH")
-        else:
-            data_dir = "data"
+        else: # possibly docker or local
+            if os.path.exists("data"):
+                logger.info("Using local data directory.")
+                data_dir = "data"
+            else:
+                data_dir = os.environ.get("EVAL_DATA_PATH") # a local docker eval
         data_dir = Path(data_dir) / dataset.name
         if phase == 'test': # TODO wire wherever test is actually stored on remote
             eval_dir = data_dir / f"eval"
@@ -92,6 +96,8 @@ def get_eval_files(self, phase: str = 'minival'):
         logger.info("Searching for evaluation data.")
         handles = self.get_eval_handles(self.eval_remote, self.dataset, phase=phase)
         logger.info(f"Found {len(handles)} files.")
+        if len(handles) == 0:
+            raise FileNotFoundError(f"No files found in {self.dataset.name} for phase {phase}. Note test phase data is only available on EvalAI remote.")
         return handles
 
     def predict_files(self, decoder: BCIDecoder, eval_files: List):

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='falcon_challenge',
-    version='0.1.4',
+    version='0.1.5',
 
     url='https://github.com/snel-repo/stability-benchmark',
     author='Joel Ye',