Search S3 if granules are not Explicitly Provided

.github/workflows/test-and-build.yml

@@ -17,22 +17,36 @@ jobs:
     with:
       local_package_name: hyp3_srg
       python_versions: >-
-        ["3.10", "3.11", "3.12"]
+        ["3.10", "3.11", "3.12", "3.13"]
 
   call-version-info-workflow:
     # Docs: https://github.com/ASFHyP3/actions
     uses: ASFHyP3/actions/.github/workflows/[email protected]
     with:
       python_version: "3.10"
 
-  call-docker-ghcr-workflow:
+  call-docker-ghcr-workflow-cpu:
     needs: call-version-info-workflow
     # Docs: https://github.com/ASFHyP3/actions
-    uses: ASFHyP3/actions/.github/workflows/reusable-docker-ghcr.yml@v0.11.2
+    uses: ASFHyP3/actions/.github/workflows/reusable-docker-ghcr.yml@v0.12.0
     with:
-      version_tag: ${{ needs.call-version-info-workflow.outputs.version_tag }}
+      version_tag: ${{ needs.call-version-info-workflow.outputs.version_tag }}.cpu
       release_branch: main
       develop_branch: develop
       user: tools-bot
+      file: Dockerfile
+    secrets:
+      USER_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  call-docker-ghcr-workflow-gpu:
+    needs: call-version-info-workflow
+    # Docs: https://github.com/ASFHyP3/actions
+    uses: ASFHyP3/actions/.github/workflows/[email protected]
+    with:
+      version_tag: ${{ needs.call-version-info-workflow.outputs.version_tag }}.gpu
+      release_branch: main
+      develop_branch: develop
+      user: tools-bot
+      file: Dockerfile.gpu
     secrets:
       USER_TOKEN: ${{ secrets.GITHUB_TOKEN }}

CHANGELOG.md

@@ -6,6 +6,19 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [PEP 440](https://www.python.org/dev/peps/pep-0440/)
 and uses [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.9.0]
+
+### Added
+* A new `--use-gslc-prefix` option has been added to the `back_projection` and `time_series` workflows:
+  * This option causes `back_projection` to upload the GSLC outputs to a `GSLC_granules/` subprefix located within the S3 bucket and prefix given by the `--bucket` and `--bucket-prefix` options.
+  * This option causes `time_series` to download the GSLC inputs from the `GSLC_granules/` subprefix located within the bucket and prefix given by the `--bucket` and `--bucket-prefix` options.
+
+### Changed
+* Releases and test deployments now trigger a Docker build for both a GPU-based image and a CPU-based image. The GPU image tag ends with `.gpu` and the CPU image tag ends with `.cpu`.
+
+### Fixed
+* Fixed the parsing for the `--bounds` option for `time_series`.
+
 ## [0.8.0]
 
 ### Added

README.md

@@ -77,21 +77,17 @@ aws ssm get-parameters --names /aws/service/ecs/optimized-ami/amazon-linux-2/gpu
 ### GPU Docker Container
 Once you have a compute environment set up as described above, you can build the GPU version of the container by running:
 ```bash
-docker build --build-arg="GPU_ARCH={YOUR_ARCH}" -t ghcr.io/asfhyp3/hyp3-srg:{RELEASE}.gpu -f Dockerfile.gpu .
+docker build --build-arg="GPU_ARCH={YOUR_ARCH}" -t srg -f Dockerfile.gpu .
 ```
 
 You can get the value of `COMPUTE_CAPABILITY_VERSION` by running `nvidia-smi` on the instance to obtain GPU type, then cross-reference this information with NVIDIA's [GPU type compute capability list](https://developer.nvidia.com/cuda-gpus). For a g6.2xlarge instance, this would be:
 ```bash
-docker --build-arg="GPU_ARCH=89" -t ghcr.io/asfhyp3/hyp3-srg:{RELEASE}.gpu -f Dockerfile.gpu .
+docker --build-arg="GPU_ARCH=89" -t srg -f Dockerfile.gpu .
 ```
 The compute capability version will always be the same for a given instance type, so you will only need to look this up once per instance type.
 The default value for this argument is `89` - the correct value for g6.2xlarge instances.
 **THE COMPUTE CAPABILITY VERSION MUST MATCH ON BOTH THE BUILDING AND RUNNING MACHINE!**
 
-The value of `RELEASE` can be obtained from the git tags.
-
-You can push a manual container to HyP3-SRG's container repository by following [this guide](https://docs.github.com/en/packages/working-with-a-github-packages-registry/working-with-the-container-registry#pushing-container-images).
-
 ### EC2 Setup
 > [!CAUTION]
 > Running the docker container on an Amazon Linux 2023 Deep Learning AMI runs, but will result in all zero outputs. Work is ongoing to determine what is causing this issue. For now, we recommend using option 2.3.
@@ -104,5 +100,5 @@ When running on an EC2 instance, the following setup is recommended:
     3. Use the latest AWS ECS-optimized GPU AMI (`aws ssm get-parameters --names /aws/service/ecs/optimized-ami/amazon-linux-2/gpu/recommended --region us-west-2`)
 3. Build the GPU docker container with the correct compute capability version (see section above). To determine this value, run `nvidia-smi` on the instance to obtain GPU type, then cross-referencke this information with NVIDIA's [GPU type compute capability list](https://developer.nvidia.com/cuda-gpus). For a g6.2xlarge instance, this would be:
 ```bash
-docker --build-arg="GPU_ARCH=89" -t ghcr.io/asfhyp3/hyp3-srg:{RELEASE}.gpu -f Dockerfile.gpu .
+docker --build-arg="GPU_ARCH=89" -t srg -f Dockerfile.gpu .
 ```

environment.yml

@@ -19,4 +19,4 @@ dependencies:
   - asf_search
   # For running
   - hyp3lib>=3,<4
-  - s1_orbits
+  - s1_orbits

pyproject.toml

@@ -18,6 +18,8 @@ classifiers=[
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
     "hyp3lib>=3,<4",

src/hyp3_srg/back_projection.py

@@ -92,6 +92,7 @@ def back_project(
     earthdata_password: str = None,
     bucket: str = None,
     bucket_prefix: str = '',
+    use_gslc_prefix: bool = False,
     work_dir: Optional[Path] = None,
     gpu: bool = False,
 ):
@@ -104,9 +105,15 @@ def back_project(
         earthdata_password: Password for NASA's EarthData service
         bucket: AWS S3 bucket for uploading the final product(s)
         bucket_prefix: Add a bucket prefix to the product(s)
+        use_gslc_prefix: Upload GSLCs to a subprefix
         work_dir: Working directory for processing
         gpu: Use the GPU-based version of the workflow
     """
+    if use_gslc_prefix:
+        if not (bucket and bucket_prefix):
+            raise ValueError('bucket and bucket_prefix must be given if use_gslc_prefix is True')
+        bucket_prefix += '/GSLC_granules'
+
     utils.set_creds('EARTHDATA', earthdata_username, earthdata_password)
     if work_dir is None:
         work_dir = Path.cwd()
@@ -149,6 +156,14 @@ def main():
     parser.add_argument('--earthdata-password', default=None, help="Password for NASA's EarthData")
     parser.add_argument('--bucket', help='AWS S3 bucket HyP3 for upload the final product(s)')
     parser.add_argument('--bucket-prefix', default='', help='Add a bucket prefix to product(s)')
+    parser.add_argument(
+        '--use-gslc-prefix',
+        action='store_true',
+        help=(
+            'Upload GSLC granules to a subprefix located within the bucket and prefix given by the'
+            ' --bucket and --bucket-prefix options'
+        )
+    )
     parser.add_argument('--gpu', default=False, action='store_true', help='Use the GPU-based version of the workflow.')
     parser.add_argument(
         '--bounds',
@@ -159,11 +174,14 @@ def main():
     )
     parser.add_argument('granules', type=str.split, nargs='+', help='Level-0 S1 granule(s) to back-project.')
     args = parser.parse_args()
+
     args.granules = [item for sublist in args.granules for item in sublist]
+
     if args.bounds is not None:
         args.bounds = [float(item) for sublist in args.bounds for item in sublist]
         if len(args.bounds) != 4:
             parser.error('Bounds must have exactly 4 values: [min lon, min lat, max lon, max lat] in EPSG:4326.')
+
     back_project(**args.__dict__)
 
 

src/hyp3_srg/dem.py

@@ -32,7 +32,7 @@ def download_dem_for_srg(bounds: list[float], work_dir: Optional[Path]):
 
     Args:
         bounds: The bounds of the extent of the desired DEM - [min_lon, min_lat, max_lon, max_lat].
-        work_dir: The directory to save create the DEM in
+        work_dir: The directory to save the DEM in
 
     Returns:
         The path to the downloaded DEM