Docker

Dockerfile

@@ -1,19 +1,21 @@
 # Use an official Python runtime as a parent image
 FROM python:3.11-slim as base
 
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends git && \
+RUN apt-get update
+RUN apt-get install unzip -y
+RUN apt-get install -y --no-install-recommends git && \
     rm -rf /var/lib/apt/lists/*
 
+# Copy the application files
+COPY src /app/src
+COPY pyproject.toml app/pyproject.toml
+COPY README.md app/README.md
+
 # Set the working directory in the container
 WORKDIR /app
 
 # Copy the requirements file and install
-COPY pyproject.toml ./
-RUN pip install --no-cache-dir .
-
-# Copy the application files
-COPY src /app/src
+RUN pip install -e .
 
 # Set the entrypoint command to run the app
 CMD ["python", "src/cloudcasting_app/app.py"]

pyproject.toml

@@ -21,7 +21,8 @@ classifiers = [
 ]
 dependencies = [
     "torch[cpu]",
-    "fsspec[s3]",
+    "fsspec",
+    "s3fs",
     "xarray",
     "zarr<3.0",
     "numpy",

src/cloudcasting_app/app.py

@@ -32,6 +32,10 @@
 # ---------------------------------------------------------------------------
 # GLOBAL SETTINGS
 
+logging.basicConfig(
+    level=getattr(logging, os.getenv("LOGLEVEL", "INFO")),
+    format="[%(asctime)s] {%(pathname)s:%(lineno)d} %(levelname)s - %(message)s",
+)
 
 # Create a logger
 logger = logging.getLogger(__name__)
@@ -132,13 +136,13 @@ def app(t0=None):
     ds_y_hat = da_y_hat.to_dataset(name="sat_pred")
     ds_y_hat.sat_pred.attrs.update(ds.data.attrs)
 
-    # Save predictions to latest path and to path with timestring
+    # Save predictions to the latest path and to path with timestring
     out_dir = os.environ["OUTPUT_PREDICTION_DIRECTORY"]
 
     latest_zarr_path = f"{out_dir}/latest.zarr"
     t0_string_zarr_path = t0.strftime(f"{out_dir}/%Y-%m-%dT%H:%M.zarr")
 
-    fs, _ = fsspec.core.url_to_fs(out_dir)
+    fs = fsspec.open(out_dir).fs
     for path in [latest_zarr_path, t0_string_zarr_path]:
 
         # Remove the path if it exists already