Adding zero shot inference that fetches data directly from aws

.gitignore

@@ -4,6 +4,7 @@ __pycache__/
 *$py.class
 *.pth
 *.pt
+*.nc
 # C extensions
 *.so
 *.DS_Store

README.md

@@ -66,6 +66,23 @@ uv sync
 source .venv/bin/activate
 ```
 
+4. **Zero-Shot Inference**
+```bash
+python easy_inference/run_easy_inference.py --config-path easy_inference/config_easy.yaml
+```
+
+#### Note
+The above command is the fastest path to run Surya foundation-model inference on a date range without any additional setup. Data is downloaded from the public S3 bucket `nasa-surya-bench`. This prompts for start/end UTC datetime, downloads needed `.nc` files, and writes:
+- `easy_inference/outputs_.../prediction.nc`
+- `easy_inference/outputs_.../metrics/*`
+
+**Device selection behavior**
+- Please refer to the [config_easy.yaml](easy_inference/config_easy.yaml) file for the default inference configuration.
+- `advanced.device: auto` uses priority: `cuda -> mps -> cpu`
+- Works across CUDA GPUs, Apple Silicon/macOS MPS, and plain CPU systems
+
+---
+
 ### 🧪 Verify Installation
 
 Run the end-to-end test to ensure everything is working:

easy_inference/README.md

@@ -0,0 +1,57 @@
+# Easy Inference
+
+Use this folder for the simplest Surya flow:
+1. choose date window,
+2. download only required hourly files,
+3. run rollout inference,
+4. save one `prediction.nc`.
+
+## Quick start
+
+```bash
+source .venv/bin/activate
+bash easy_inference/run_easy_inference.sh
+```
+
+Non-interactive defaults:
+
+```bash
+bash easy_inference/run_easy_inference.sh --no-prompt
+```
+
+## Config
+
+Edit `easy_inference/config_easy.yaml`.
+
+- Normal users: edit only the top `user:` section.
+- Advanced users: optional changes in `advanced:`.
+
+Default and override behavior:
+
+```bash
+# Uses easy_inference/config_easy.yaml by default.
+python easy_inference/run_easy_inference.py
+
+# Optional: use a different YAML file.
+python easy_inference/run_easy_inference.py --config-path /path/to/custom_easy.yaml
+```
+
+### Debug mode
+
+Set in `advanced:`:
+- `debug_mode: true`
+- optional `debug_log_path: "path/to/inference_debug.txt"` (default is `<user.output_dir>/inference_debug.txt`)
+
+When enabled, the text log contains stage timings and per-step diagnostics with line number + UTC timestamp:
+- input file read / transform timing
+- GT file read timing
+- per-step forward / CPU-copy / inverse-transform / write timing
+- per-step memory stats (`CUDA` peak/allocated/reserved when available)
+
+## Metrics Notebook
+
+Use `easy_inference/compare_prediction_groundtruth.ipynb` to compare `prediction.nc` vs GT and compute:
+- overall metrics (`MSE`, `RMSE`, `MAE`, `bias`, `max_abs_error`)
+- per-channel metrics
+- per-step metrics
+- visual prediction vs ground-truth plots

easy_inference/config_easy.yaml

@@ -0,0 +1,97 @@
+# --------------------------------------------------------------------
+# Edit only this USER section for normal use.
+# --------------------------------------------------------------------
+user:
+  # Inclusive UTC start datetime for the download/inference window.
+  # Format: YYYY-MM-DD HH:MM[:SS]
+  start_datetime: "2014-10-23 10:00:00"
+  # Inclusive UTC end datetime for the download/inference window.
+  # Format: YYYY-MM-DD HH:MM[:SS]
+  end_datetime: "2014-10-23 17:00:00"
+  # If true, prompt in terminal to confirm/override start/end datetime and rollout steps.
+  prompt_for_dates: true
+  # Directory where run artifacts are written (prediction.nc, metrics CSV/JSON).
+  output_dir: easy_inference/outputs_24h
+  # Number of autoregressive prediction steps to generate.
+  rollout_steps: 5
+
+# --------------------------------------------------------------------
+# Advanced section (optional). Leave as-is unless needed.
+# --------------------------------------------------------------------
+advanced:
+  # Local path to foundation model architecture/config YAML.
+  # Downloaded automatically from model_repo_id if missing.
+  foundation_config_path: data/Surya-1.0/config.yaml
+  # Local path to scaler definitions used for inverse transform.
+  # Downloaded automatically from model_repo_id if missing.
+  scalers_path: data/Surya-1.0/scalers.yaml
+  # Local path to model weights checkpoint (.pt).
+  # Downloaded automatically from model_repo_id if missing.
+  weights_path: data/Surya-1.0/surya.366m.v1.pt
+  # Hugging Face repository used to fetch missing model assets.
+  model_repo_id: nasa-ibm-ai4science/Surya-1.0
+  # Files to pull from model_repo_id when assets are missing locally.
+  model_allow_patterns:
+    # Foundation model config.
+    - config.yaml
+    # Data scaler config.
+    - scalers.yaml
+    # Foundation model weights.
+    - surya.366m.v1.pt
+
+  # Local folder for downloaded/available validation .nc files.
+  validation_data_dir: data/Surya-1.0_validation_data_20141023_60min
+  # CSV index generated for the requested date window (used by dataset loader).
+  index_path: easy_inference/index_20141023_60min.csv
+
+  # Expected cadence (minutes) between consecutive source files/timestamps.
+  cadence_minutes: 60
+  # Relative input frame offsets (in minutes) used to build model input sequence.
+  # Example [-60, 0] means: previous hour + current time as input.
+  time_delta_input_minutes: [-60, 0]
+  # Target offset (minutes) for the first prediction horizon.
+  time_delta_target_minutes: 60
+
+  # Download settings
+  # Public S3 bucket containing benchmark .nc files.
+  s3_bucket: nasa-surya-bench
+  # If true, do not re-download files that already exist locally.
+  download_skip_existing: true
+  # If true, compare local file size with remote and re-download on mismatch.
+  download_verify_size: false
+  # Allowed timestamp matching tolerance (minutes) when mapping expected times to files.
+  # 0 means exact timestamp match only.
+  download_match_tolerance_minutes: 0
+  # If true, remove local validation files outside the requested window before download.
+  prune_validation_data_to_window: false
+
+  # Runtime
+  # Device selection. Values: auto | cuda | mps | cpu
+  # auto resolves in this order: cuda -> mps -> cpu.
+  device: auto
+  # Compute dtype for inference. Values: auto | float32 | float16 | bfloat16
+  dtype: auto
+  # Batch is fixed to 1 in easy mode (first valid sample only).
+  # Number of DataLoader worker processes (0 = main process).
+  num_workers: 0
+  # DataLoader prefetch batches per worker (used only when num_workers > 0).
+  prefetch_factor: 2
+  # Number of background workers for GT timestep prefetch during rollout (>=1).
+  gt_prefetch_workers: 4
+  # If true, disable autocast mixed precision even when supported.
+  disable_autocast: false
+  # If true, allow TF32 fast matmul/cudnn paths on CUDA.
+  enable_tf32: true
+  # If true, enable cuDNN benchmark autotuning (CUDA only).
+  enable_cudnn_benchmark: true
+  # CPU thread count for torch. 0 leaves PyTorch default behavior.
+  cpu_threads: 0
+  # If true, print progress logs for download and inference stages.
+  show_progress: true
+  # If true, write detailed debug profiling logs (plain text).
+  debug_mode: false
+  # Optional path for debug log file. Empty -> <user.output_dir>/inference_debug.txt
+  debug_log_path: "easy_inference/inference_debug.txt"
+
+  # Output dtype for saved prediction.nc values. Values: float16 | float32
+  prediction_dtype: float32