Added wait to upload/track_files (#301)

use-cases/nlp/classification/keras/code/keras_nb.ipynb

@@ -365,9 +365,8 @@
    "outputs": [],
    "source": [
     "project[\"keras/data/files\"].track_files(\n",
-    "    \"s3://neptune-examples/data/text-classification/aclImdb_v1.tar.gz\"\n",
-    ")\n",
-    "project.wait()"
+    "    \"s3://neptune-examples/data/text-classification/aclImdb_v1.tar.gz\", wait=True\n",
+    ")"
    ]
   },
   {
@@ -932,7 +931,7 @@
    "outputs": [],
    "source": [
     "keras_model.save_weights(\"model_weights.h5\")\n",
-    "model_version[\"model_weights\"].upload(\"model_weights.h5\")"
+    "model_version[\"model_weights\"].upload(\"model_weights.h5\", wait=True)"
    ]
   },
   {
@@ -953,24 +952,6 @@
     "model_version.change_stage(\"staging\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### (Neptune) Wait for all operations to reach with Neptune servers\n",
-    "Since Neptune sends data to servers asynchronously by default, we need to wait for operations to complete if we want to refer to fields/objects that were sent to Neptune earlier in the same code.  \n",
-    "Read about the `wait()` and `sync()` methods here: https://docs.neptune.ai/logging/wait_and_sync/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_version.wait()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

use-cases/nlp/classification/keras/code/keras_script.py

@@ -142,10 +142,8 @@ def build_model(model_params: dict, data_params: dict):
 # Data preparation #
 ####################
 project["keras/data/files"].track_files(
-    "s3://neptune-examples/data/text-classification/aclImdb_v1.tar.gz"
+    "s3://neptune-examples/data/text-classification/aclImdb_v1.tar.gz", wait=True
 )
-project.wait()
-
 
 # (Neptune) Download files from S3 using Neptune
 print("Downloading data...")
@@ -332,14 +330,12 @@ def build_model(model_params: dict, data_params: dict):
 model_version["serialized_model"] = keras_model.to_json()
 
 keras_model.save_weights("model_weights.h5")
-model_version["model_weights"].upload("model_weights.h5")
+model_version["model_weights"].upload("model_weights.h5", wait=True)
 
 
 # (Neptune) Update model stage
 model_version.change_stage("staging")
 
-model_version.wait()
-
 ##############################################
 # (Neptune) Promote best model to production #
 ##############################################