rename: nSamples to n_samples

docs/source/docker/advanced/code_examples/python_run_active_learning.py

@@ -75,7 +75,7 @@
         "pretagging_debug": False,
     },
     selection_config={
-        "nSamples": 100,
+        "n_samples": 100,
         "strategies": [
             {
                 "input": {

docs/source/docker/advanced/code_examples/python_run_datapool_example.py

@@ -77,7 +77,7 @@
         'pretagging_debug': False,
     },
     selection_config={
-        "nSamples": 100,
+        "n_samples": 100,
         "strategies": [
             {
                 "input": {

docs/source/docker/advanced/code_examples/python_run_object_level.py

@@ -78,7 +78,7 @@
         },
     },
     selection_config={
-        "nSamples": 100,
+        "n_samples": 100,
         "strategies": [
             {
                 "input": {

docs/source/docker/advanced/code_examples/python_run_object_level_pretagging.py

@@ -80,7 +80,7 @@
         }
     },
     selection_config={
-        "nSamples": 100,
+        "n_samples": 100,
         "strategies": [
             {
                 "input": {

docs/source/docker/advanced/code_examples/python_run_pretagging.py

@@ -76,7 +76,7 @@
         'pretagging_debug': True,   # we also want debugging images in the report
     },
     selection_config={
-        "nSamples": 100,
+        "n_samples": 100,
         "strategies": [
             {
                 "input": {

docs/source/docker/examples/academic_datasets.rst

@@ -45,8 +45,8 @@ bucket under `s3://dataset/imagenet/`. Start by creating a dataset and configuri
 
 
 
-Next, we schedule a job which extracts 500000 frames with the default Coreset strategy which
-selects a diverse set of frames:
+Next, we schedule a job which extracts 500000 images with the CORESET strategy which
+selects a visually diverse set of images:
 
 
 .. code-block:: python
@@ -62,7 +62,7 @@ selects a diverse set of frames:
             "pretagging_debug": False
         },
         selection_config = {
-            "nSamples": 500000,
+            "n_samples": 500000,
             "strategies": [
                 {
                     "input": {
@@ -145,7 +145,7 @@ The following command schedules a job to select a subset from Cityscapes:
             "pretagging_debug": False
         },
         selection_config = {
-            "nSamples": 10_000, # maket this number high (upper limit)
+            "n_samples": 10_000, # maket this number high (upper limit)
             "strategies": [
                 {
                     "input": {
@@ -193,7 +193,7 @@ the new images.
             "pretagging_debug": False,
         },
         selection_config = {
-            "nSamples": 10_000, # maket this number high (upper limit)
+            "n_samples": 10_000, # maket this number high (upper limit)
             "strategies": [
                 {
                     "input": {

docs/source/docker/examples/datasets_in_the_wild.rst

@@ -111,7 +111,7 @@ select a diverse set of frames:
           "remove_exact_duplicates": True
       },
       selection_config = {
-          "nSamples": 99,
+          "n_samples": 99,
           "strategies": [
               {
                   "input": {

docs/source/docker/getting_started/first_steps.rst

@@ -220,7 +220,7 @@ Now that everything is in place, let's configure and run a simple job.
             "remove_exact_duplicates": True,
         },
         selection_config={
-            "nSamples": 50,
+            "n_samples": 50,
             "strategies": [
                 {
                     "input": {
@@ -279,7 +279,7 @@ epochs on the input images before embedding the images and selecting from them.
             "enable_training": True
         },
         selection_config={
-            "nSamples": 50,
+            "n_samples": 50,
             "strategies": [
                 {
                     "input": {
@@ -316,7 +316,7 @@ you might want to change:
             "enable_training": True,
         },
         selection_config={
-            "nSamples": 50,
+            "n_samples": 50,
             "strategies": [
                 {
                     "input": {
@@ -400,7 +400,7 @@ a `shared directory` and then passing the checkpoint filename to the container.
             "checkpoint": "lightly_epoch_X.ckpt"
         },
         selection_config={
-            "nSamples": 50,
+            "n_samples": 50,
             "strategies": [
                 {
                     "input": {
@@ -476,7 +476,7 @@ The corresponding Python command to submit a job would then be as follows:
             "remove_exact_duplicates": True
         },
         selection_config={
-            "nSamples": 50,
+            "n_samples": 50,
             "strategies": [
                 {
                     "input": {

docs/source/docker/getting_started/selection.rst

@@ -55,7 +55,7 @@ The configuration of a selection needs to specify both the maximum number of sam
 .. code-block:: python
 
     {
-        "nSamples": 50,
+        "n_samples": 50,
         "strategies": [
             {
                 "input": {
@@ -69,7 +69,7 @@ The configuration of a selection needs to specify both the maximum number of sam
         ]
     }
 
-The variable :code:`nSamples` must be a positive integer specifying the absolute number of samples which should be selected.
+The variable :code:`n_samples` must be a positive integer specifying the absolute number of samples which should be selected.
 
 Each strategy is specified by a :code:`dictionary`, which is always made up of an :code:`input` and the actual :code:`strategy`.
 
@@ -243,7 +243,7 @@ There are several types of selection strategies, all trying to reach different o
         embeddings in the output dataset. Since we normalize the input embeddings
         to unit length, this value should be between 0 and 2.0.
         This is often a convenient method when working with different data sources and trying to combine them in a balanced way.
-        If you want to use this stopping condition to stop the selection early, make sure that you allow selecting enough samples by setting :code:`nSamples` high enough.
+        If you want to use this stopping condition to stop the selection early, make sure that you allow selecting enough samples by setting :code:`n_samples` high enough.
 
         .. note:: Higher minimum distance in the embedding space results in more
                   diverse images being selected. Furthermore, increasing the
@@ -317,7 +317,7 @@ Here are examples for the full configuration including the input for several obj
     .. code-block:: python
 
         {
-            "nSamples": 100, # set to the number of samples you want to select
+            "n_samples": 100, # set to the number of samples you want to select
             "strategies": [
                 {
                     "input": {
@@ -338,7 +338,7 @@ Here are examples for the full configuration including the input for several obj
     .. code-block:: python
 
         {
-            "nSamples": 100, # set to the number of samples you want to select
+            "n_samples": 100, # set to the number of samples you want to select
             "strategies": [
                 {
                     "input": {
@@ -362,7 +362,7 @@ Here are examples for the full configuration including the input for several obj
     .. code-block:: python
 
         {
-            "nSamples": 100, # set to the number of samples you want to select
+            "n_samples": 100, # set to the number of samples you want to select
             "strategies": [
                 {
                     "input": {
@@ -393,7 +393,7 @@ Here are examples for the full configuration including the input for several obj
     .. code-block:: python
 
         {
-            "nSamples": 100, # set to the number of samples you want to select
+            "n_samples": 100, # set to the number of samples you want to select
             "strategies": [
                 {
                     "input": {
@@ -416,7 +416,7 @@ Here are examples for the full configuration including the input for several obj
     .. code-block:: python
 
         {
-            "nSamples": 100, # set to the number of samples you want to select
+            "n_samples": 100, # set to the number of samples you want to select
             "strategies": [
                 {
                     "input": {
@@ -451,7 +451,7 @@ Here are examples for the full configuration including the input for several obj
     .. code-block:: python
 
         {
-            "nSamples": 100, # set to the number of samples you want to select
+            "n_samples": 100, # set to the number of samples you want to select
             "strategies": [
                 {
                     "input": {
@@ -493,7 +493,7 @@ The Lightly optimizer tries to fulfil all strategies as good as possible.
   of 1000 images are selected, the output can only have a maximum of 1% ambulances. Thus a BALANCE target of having 20% ambulances cannot be fulfilled.
 
 - **Too little samples to choose.**
-  If the selection algorithm can only choose a small number of samples, it may not be possible to fulfil the objectives. You can solve this by increasing :code:`nSamples`.
+  If the selection algorithm can only choose a small number of samples, it may not be possible to fulfil the objectives. You can solve this by increasing :code:`n_samples`.
 
 Selection on object level
 -------------------------

docs/source/docker/integration/dagster_aws.rst

@@ -335,7 +335,7 @@ Set the `YOUR_LIGHTLY_TOKEN`, `YOUR_DATASET_ID` accordingly.
                     "pretagging_debug": False,
                 },
                 selection_config={
-                    "nSamples": 50,
+                    "n_samples": 50,
                     "strategies": [
                         {
                             "input": {

docs/source/docker/integration/examples/trigger_job.py

@@ -13,7 +13,7 @@
         'enable_training': False,
     },
     selection_config={
-        "nSamples": 50,
+        "n_samples": 50,
         "strategies": [
             {
                 "input": {