samples comment update

Author: cbatis

samples/job_with_aws_input_sample.py

@@ -9,13 +9,13 @@
 sys.path.insert(0, '..')
 from modzy import ApiClient
 
-
+ 
 # Always configure the logger level (ie: DEBUG, INFO, WARNING, ERROR, CRITICAL)
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 # The system admin can provide the right base API URL, the API key can be downloaded from your profile page on Modzy.
-# You can configure those params as is described in the README file (as environment variables, or by using the .env file),
+# You can configure those params as described in the README file (as environment variables, or by using the .env file),
 # or you can just update the BASE_URL and API_KEY variables and use this sample code (not recommended for production environments).
 
 dotenv.load_dotenv()
@@ -35,8 +35,8 @@
 # Create a Job with an aws input, wait, and retrieve results:
 
 # Get the model object:
-# If you already know the model identifier (i.e.: you got from the URL of the model details page or the input sample),
-# you can skip this step. If you don't you can find the model identifier by using its name as follows:
+# If you already know the model identifier (i.e.: you got it from the URL of the model details page or the input sample),
+# you can skip this step. If you don't, you can find the model identifier by using its name as follows:
 model = client.models.get_by_name("Facial Embedding")
 # Or if you already know the model id and want to know more about the model, you can use this instead:
 # model = client.models.get("f7e252e26a")
@@ -49,10 +49,10 @@
 
 # Get the model version object:
 # If you already know the model version and the input key(s) of the model version you can skip this step. Also, you can
-# use the following code block to know about the inputs keys and skip the call on future job submissions.
+# use the following code block to know about the input keys and skip the call on future job submissions.
 modelVersion = client.models.get_version(model, model.latest_version)
 # The info stored in modelVersion provides insights about the amount of time that the model can spend processing,
-# the inputs, and output keys of the model.
+# the input, and output keys of the model.
 logger.info("This model version is {}".format(modelVersion))
 logger.info("  timeouts: status {}ms, run {}ms ".format(modelVersion.timeout.status, modelVersion.timeout.run))
 logger.info("  inputs: ")
@@ -75,11 +75,11 @@
 BUCKET_NAME="<<BucketName>>"
 #    The File Key: replace <<FileId>> (remember, this model needs an image as input)
 FILE_KEY="<<FileId>>"
-# With the info about the model (identifier), the model version (version string, input/output keys), you are ready to
+# With the info about the model (identifier) and the model version (version string, input/output keys), you are ready to
 # submit the job. Just prepare the source dictionary:
 sources = {"source-key": {"image": {'bucket': BUCKET_NAME, 'key': FILE_KEY}}}
-# An inference job groups input data that you send to a model. You can send any amount of inputs to
-# process and you can identify and refer to a specific input by the key that you assign, for example we can add:
+# An inference job groups input data sent to a model. You can send any amount of inputs to
+# process and you can identify and refer to a specific input by the key assigned. For example we can add:
 sources["second-key"] = {"image": {'bucket': BUCKET_NAME, 'key': FILE_KEY}}
 sources["another-key"] = {"image": {'bucket': BUCKET_NAME, 'key': FILE_KEY}}
 # If you send a wrong input key, the model fails to process the input.
@@ -93,7 +93,7 @@
 logger.info("job: %s", job)
 # The job moves to SUBMITTED, meaning that Modzy acknowledged the job and sent it to the queue to be processed.
 # We provide a helper method to listen until the job finishes processing. Its a good practice to set a max timeout
-# if you're doing a test (ie: 2*status+run). Otherwise, if the timeout is set to None, it will listen until the job
+# if you're doing a test (ie: 2*status+run). Otherwise, if the timeout is set to None, it listens until the job
 # finishes and moves to COMPLETED, CANCELED, or TIMEOUT.
 job.block_until_complete(timeout=None)
 
@@ -103,12 +103,12 @@
     # A completed job means that all the inputs were processed by the model. Check the results for each
     # input key provided in the source dictionary to see the model output.
     result = job.get_result()
-    # The result object has some useful info:
+    # The results object has some useful info:
     logger.info("Result: finished: {}, total: {}, completed: {}, failed: {}"
                 .format(result.finished, result.total, result.completed, result.failed))
     # Notice that we are iterating through the same input source keys
     for key in sources:
-        # The result object has the individual results of each job input. In this case the output key is called
+        # The results object has the individual results of each job input. In this case the output key is called
         # results.json, so we can get the results as follows:
         try:
             model_res = result.get_source_outputs(key)['results.json']