fix: update comment and docs

Author: mesakhcienet

examples/science/af3-slurm/examples/simple_ipynb_launcher/Ipynb.md

@@ -10,24 +10,20 @@ Ensure your input JSON file is placed in the correct location **relative to your
 
 ```code
 alphafold/
-├── <uploaded_file>.json
-├── fold_input.json # Example input file (provided)
+├── <uploaded_file>.json # Your manually uploaded input file
+├── fold_input_example.json # Example input file (provided)
 └── slurm-rest-api-notebook.ipynb
 ```
 
-When configuring your data pipeline or inference process, use the correct **relative file path to reference the input JSON**. We provide an example input file of data pipeline process named `fold_input.json` to help you get started quickly.
+When configuring your data pipeline or inference process, ensure you use the correct ***relative file path*** to reference the input JSON. To help you get started, we provide an example input file for the data pipeline process named `fold_input_example.json`.
 
-Since both the notebook and the input file are in the same directory (`alphafold/`), you can specify the file like this in your notebook code:
+If your input file is located in the same directory(under `/alphafold/` directory) with `fold_input_example.json`, simply update the `input_file` variable in the notebook code to reflect the name of your uploaded file:
 
 ```python
-input_file= "fold_input.json"
+input_file= "fold_input_example.json" # Replace this with your actual file name
 ```
 
-If you're uploading your own file, simply replace the `input_file` variable with the name of your uploaded file:
-
-```python
-input_file = "<uploaded_file>.json"
-```
+Make sure the file is properly formatted and matches the expected schema (see [AlphaFold Input Documentation](https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md#alphafold-server-json-compatibility) for reference).
 
 **2. Running the Setup Cells (including System Configuration and SLURM Initialization):**
 

examples/science/af3-slurm/examples/simple_ipynb_launcher/README.md

@@ -31,38 +31,43 @@ The version mismatch does not impact the functionality required by this project.
 ### Resource Unavailability (Out of Capacity)
 
 **Description**:  
-Your job is queued but not executed because the cloud provider (e.g., GCP) can't provision the required compute nodes. This is often due to a temporary lack of available resources in the chosen region or zone.
+Your job is queued but not executed because GCP can't provision the required compute nodes. This is often due to a temporary lack of available resources in the chosen region or zone.
 
 **Resolution**:
-- Check the **Controller Logs**:  
+- Check the Slurm **Controller Node** Log:  
   Log on to the controller machine for your cluster to see detailed error messages like  
   `"GCP Error: Zone does not currently have sufficient capacity for resources"`.
 - **Wait and Re-check**:  
-  For temporary resource shortages, wait **5–10 minutes** and then re-run the "check job status" cell in your Jupyter notebook. Resources might become available and allow your job to proceed.
+  For temporary resource shortages, wait **5–10 minutes** and then re-run the `get_job_state` cell in your Jupyter notebook. Resources might become available and allow your job to proceed.
 
 ### Node Creation In Progress
 
 **Description**:  
-After your job is submitted, it takes time for the cluster to spin up new nodes. You might see a `NODE_FAIL` status if nodes are still being created and are not yet ready.
+After submitting your job, the cluster may take several minutes to provision and initialize new compute nodes. During this time, your job might temporarily show a status like `NODE_FAIL` if the nodes are not yet fully ready or registered.
 
 **Resolution**:
-- Monitor the **Controller Logs** for status updates.
-- **Wait 5–10 minutes**, then recheck your job status to see if nodes have become available.
-
----
-
-### Runtime-Related Issues
-
-These issues occur once your job attempts to run on the allocated nodes.
+- Check the Slurm **Controller Node** Logs for detailed status updates.
+- **Wait 5–10 minutes**, then recheck your job status to see if the nodes have become available and the job is progressing.
 
 ### Input File Format Error
 
 **Description**:  
 Your job might fail immediately if the input file format is incorrect or if the file is corrupted.
 
 **Resolution**:
-- Review the **Job Logs** to identify input file issues.
-- Ensure the input file meets the expected format and isn't corrupted.
+- Review the **Job Logs** to identify issues related to the input file. Logs are located at:
+  - `/home/af3ipynb/datapipeline_result/<input_file_name>/<timestamp>/slurm_logs/job_<job_id>/*.txt` for **data pipeline** process
+  - `/home/af3ipynb/inference_result/<input_file_name>/<timestamp>/slurm_logs/job_<job_id>/*.txt` for **inference** process.
+  
+  Each directory contains:
+  - `err.txt` – captures detailed **STDERR** output.
+  - `out.txt` – captures **STDOUT** output.
+
+- Ensure that the input file:
+  - Conforms to the expected format. Currently supported formats include alphafoldserver and alphafold3.
+  - Is not corrupted.
+
+For detailed input file requirements, please refer to [Alphafold Input Documentation](https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md#alphafold-server-json-compatibility).
 
 ### Out of Memory (OOM) Issue
 

examples/science/af3-slurm/examples/simple_ipynb_launcher/adm/slurm-rest-api-notebook.ipynb.j2

@@ -258,10 +258,11 @@
     "# In this example, we will use the output from the datapipeline job.\n",
     "# Datapipeline job could returns multiple output files in case the input file is a JSON file with multiple protein sequences\n",
     "# The output path is a list of dictionaries, each containing the path to the output file\n",
-    "# The first output path is used as input for inference\n",
+    "# In this example, the first datapipeline output is used as input for inference\n",
     "print(\"[INFO] Extracting output paths from datapipeline job response...\")\n",
     "print(\"[INFO] Datapipeline output path count:\", len(datapipeline_submit_job_response[\"output_path_list\"]))\n",
-    "inference_input_file = datapipeline_submit_job_response[\"output_path_list\"][0][\"output_path\"]\n",
+    "output_index = 0  # Change this index if you want to use a different output file\n",
+    "inference_input_file = datapipeline_submit_job_response[\"output_path_list\"][output_index][\"output_path\"]\n",
     "\n",
     "# Assign `enable_unified_memory` with True or False\n",
     "# No changes required to run default settings\n",

examples/science/af3-slurm/examples/simple_ipynb_launcher/adm/slurm_client.py.j2

@@ -213,6 +213,10 @@ class AF3SlurmClient:
 
     def submit_inference_job(self, input_file: str, partition_name: str, enable_unified_memory: Optional[bool] = None, output_dir: Optional[str] = None) -> Optional[Dict]:
         """Submits an inference job to Slurm server."""
+
+        if not input_file:
+            raise ValueError("Input file must be provided.")
+
         partition_config = self.PARTITION_CONFIG_MAP.get(partition_name, {})
         if not partition_config:
             raise ValueError(
@@ -258,7 +262,10 @@ class AF3SlurmClient:
             input_file=input_file
         )
         output_path_list: List[Dict] = []
-        for sanitised_input_name in sanitised_input_name_list:
+
+        print(
+            f"[INFO] Inference output path for input file: {input_file}")
+        for index, sanitised_input_name in enumerate(sanitised_input_name_list):
             inference_model_cif_output_path = os.path.join(
                 output_dir, f"{sanitised_input_name}", f"{sanitised_input_name}_model.cif")
             inference_summary_confidences_output_path = os.path.join(
@@ -270,19 +277,21 @@ class AF3SlurmClient:
                 "summary_confidences": inference_summary_confidences_output_path,
                 "confidences": inference_confidences_output_path
             })
-
-            print(f"[INFO] Output path for input file: {sanitised_input_name}")
             print(
-                f"         ├── Inference model CIF output path : {inference_model_cif_output_path}")
+                f"         ├── Model CIF output path {index+1} : {inference_model_cif_output_path}")
             print(
-                f"         ├── Inference confidences output path : {inference_confidences_output_path}")
+                f"         ├── Confidences output path {index+1} : {inference_confidences_output_path}")
             print(
-                f"         └── Inference summary confidences output path : {inference_summary_confidences_output_path}")
+                f"         └── Summary confidences output path {index+1} : {inference_summary_confidences_output_path}")
         response["output_path_list"] = output_path_list
         return response
 
     def submit_data_pipeline_job(self,  input_file: str, partition_name: str, output_dir: Optional[str] = None) -> Optional[Dict]:
-        """Submits an data pipeline job to Slurm server."""
+        """Submits a data pipeline job to Slurm server."""
+
+        if not input_file:
+            raise ValueError("Input file must be provided.")
+
         partition_config = self.PARTITION_CONFIG_MAP.get(partition_name, {})
         if not partition_config:
             raise ValueError(
@@ -320,15 +329,17 @@ class AF3SlurmClient:
         sanitised_input_name_list = retrieve_sanitised_input_file_name(
             input_file)
         output_path_list: List[Dict] = []
-        for sanitised_input_name in sanitised_input_name_list:
+
+        print(
+            f"[INFO] Datapipeline output path for input file: {input_file}")
+        for index, sanitised_input_name in enumerate(sanitised_input_name_list):
             datapipeline_output_file_path = os.path.join(
                 output_dir, f"{sanitised_input_name}", f"{sanitised_input_name}_data.json")
             output_path_list.append({
                 "output_path": datapipeline_output_file_path
             })
-            print(f"[INFO] Output path for input file: {sanitised_input_name}")
             print(
-                f"         └── Datapipeline output path : {datapipeline_output_file_path}")
+                f"         └── Path {index+1} : {datapipeline_output_file_path}")
 
         response["output_path_list"] = output_path_list
         return response