docs: fix broken references (links)

* related (master to main) commit: d5c82e5

Signed-off-by: Alex Aizman <[email protected]>

python/aistore/sdk/README.md

@@ -113,7 +113,7 @@ AIStore also supports [ETLs](https://aistore.nvidia.com/docs/etl), short for Ext
 
 > Note: AIS-ETL requires [Kubernetes](https://kubernetes.io/). For more information on deploying AIStore with Kubernetes (or Minikube), refer [here](https://github.com/NVIDIA/aistore/blob/main/deploy/dev/k8s/README.md).
 
-Check out the [provided examples](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk/etl_templates.py) to learn more about working with AIS ETL.
+To learn more about working with AIS ETL, check out [examples](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk/etl).
 
 ---
 

python/examples/aisio-pytorch/aisio_pytorch_example.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# PyTorch: Loading Data from AIStore \n",
     "\n",
-    "Listing and loading data from AIS buckets (buckets that are not 3rd party backend-based) and remote cloud buckets (3rd party backend-based cloud buckets) using [AISFileLister](https://pytorch.org/data/main/generated/torchdata.datapipes.iter.AISFileLister.html#aisfilelister) and [AISFileLoader](https://pytorch.org/data/main/generated/torchdata.datapipes.iter.AISFileLoader.html#torchdata.datapipes.iter.AISFileLoader).\n",
+    "Listing and loading data from AIS buckets (buckets that are not 3rd party backend-based) and remote cloud buckets (3rd party backend-based cloud buckets) using [AISFileLister](https://pytorch.org/data/beta/generated/torchdata.datapipes.iter.AISFileLister.html) and [AISFileLoader](https://pytorch.org/data/beta/generated/torchdata.datapipes.iter.AISFileLoader.html).\n",
     "\n",
     "In the following example, we use the [Caltech-256 Object Category Dataset](https://authors.library.caltech.edu/7694/) containing 256 object categories and a total of 30607 images stored on an AIS bucket and the [Microsoft COCO Dataset](https://cocodataset.org/#home) which has 330K images with over 200K labels of more than 1.5 million object instances across 80 object categories stored on Google Cloud. "
    ]
@@ -37,9 +37,9 @@
     "\n",
     "AIS is an elastic cluster that can grow and shrink at runtime and can be ad-hoc deployed, with or without Kubernetes, anywhere from a single Linux machine to a bare-metal cluster of any size. AIS fully supports Amazon S3, Google Cloud, and Microsoft Azure backends, providing a unified namespace across multiple connected backends and/or other AIS clusters, and [more](https://github.com/NVIDIA/aistore#features).\n",
     "\n",
-    "[Getting started with AIS](https://github.com/NVIDIA/aistore/blob/master/docs/getting_started.md) will take only a few minutes (prerequisites boil down to having a Linux with a disk) and can be done either by running a prebuilt [all-in-one docker image](https://github.com/NVIDIA/aistore/tree/master/deploy) or directly from the open-source.\n",
+    "[Getting started with AIS](https://github.com/NVIDIA/aistore/blob/main/docs/getting_started.md) will take only a few minutes (prerequisites boil down to having a Linux with a disk) and can be done either by running a prebuilt [all-in-one docker image](https://github.com/NVIDIA/aistore/tree/master/deploy) or directly from the open-source.\n",
     "\n",
-    "To keep this example simple, we will be running a [minimal standalone docker deployment](https://github.com/NVIDIA/aistore/blob/master/deploy/prod/docker/single/README.md) of AIStore."
+    "To keep this example simple, we will be running a [minimal standalone docker deployment](https://github.com/NVIDIA/aistore/blob/main/deploy/prod/docker/single/README.md) of AIStore."
    ]
   },
   {
@@ -69,7 +69,7 @@
    "id": "3b067695",
    "metadata": {},
    "source": [
-    "To create and put objects (dataset) in the bucket, I am going to be using [AIS CLI](https://github.com/NVIDIA/aistore/blob/master/docs/cli.md). But we can also use the [Python SDK](https://github.com/NVIDIA/aistore/tree/master/python/aistore) for the same."
+    "To create and put objects (dataset) in the bucket, I am going to be using [AIS CLI](https://github.com/NVIDIA/aistore/blob/main/docs/cli.md). But we can also use the [Python SDK](https://github.com/NVIDIA/aistore/tree/master/python/aistore) for the same."
    ]
   },
   {
@@ -216,12 +216,12 @@
     "### References\n",
     "- [AIStore](https://github.com/NVIDIA/aistore)\n",
     "- [AIStore Blog](https://aiatscale.org/blog)\n",
-    "- [AIS CLI](https://github.com/NVIDIA/aistore/blob/master/docs/cli.md)\n",
+    "- [AIS CLI](https://github.com/NVIDIA/aistore/blob/main/docs/cli.md)\n",
     "- [AIStore Cloud Backend Providers](https://aiatscale.org/docs/providers)\n",
     "- [AIStore Documentation](https://aiatscale.org/docs)\n",
     "- [AIStore Python SDK](https://github.com/NVIDIA/aistore/tree/master/python/aistore)\n",
     "- [Caltech 256 Dataset](https://authors.library.caltech.edu/7694/)\n",
-    "- [Getting started with AIStore](https://github.com/NVIDIA/aistore/blob/master/docs/getting_started.md)\n",
+    "- [Getting started with AIStore](https://github.com/NVIDIA/aistore/blob/main/docs/getting_started.md)\n",
     "- [Microsoft COCO Dataset](https://cocodataset.org/#home)\n"
    ]
   }

python/examples/dask/dask-aistore-demo.ipynb

@@ -61,9 +61,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Start by deploying an AIStore cluster. The following demonstrations will be utilizing a [Minikube (Kubernetes) deployment](https://github.com/NVIDIA/aistore/blob/master/deploy/dev/k8s/README.md) of AIStore. \n",
+    "Start by deploying an AIStore cluster. The following demonstrations will be utilizing a [Minikube (Kubernetes) deployment](https://github.com/NVIDIA/aistore/blob/main/deploy/dev/k8s/README.md) of AIStore. \n",
     "\n",
-    "> For information on AIStore deployment options, refer [here](https://github.com/NVIDIA/aistore/blob/master/deploy/README.md).\n",
+    "> For information on AIStore deployment options, refer [here](https://github.com/NVIDIA/aistore/blob/main/deploy/README.md).\n",
     "\n",
     "Once deployed, import the `aistore` package and initialize a `Client`:"
    ]
@@ -111,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note:** ETL processes using Dask with data on AIStore *are* possible, but has limitations as Dask does *not* currently support AIStore as a recognized storage provider. Refer to the AIS-ETL service [here](https://github.com/NVIDIA/aistore/blob/master/docs/etl.md), which offers both *offline* and *inline* custom transformations, as well as flexibility to the scope of those transformations (bucket-specific or object(s)-specific), and more."
+    "**Note:** ETL processes using Dask with data on AIStore *are* possible, but has limitations as Dask does *not* currently support AIStore as a recognized storage provider. Refer to the AIS-ETL service [here](https://github.com/NVIDIA/aistore/blob/main/docs/etl.md), which offers both *offline* and *inline* custom transformations, as well as flexibility to the scope of those transformations (bucket-specific or object(s)-specific), and more."
    ]
   },
   {
@@ -637,7 +637,7 @@
     "dd.to_json(\"gcs://dask-demo-bucket/sample.json\")\n",
     "```\n",
     "\n",
-    "> AIStore supports a subset of S3 API and Dask supports [S3-compatible storage services](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html#using-other-s3-compatible-services). However, Dask uses Boto3 for this and AIStore does not support Boto3 as it does not follow HTTP(s) protocol. For more information on AIStore compatibility with S3, refer [here](https://github.com/NVIDIA/aistore/blob/master/docs/s3compat.md).\n",
+    "> AIStore supports a subset of S3 API and Dask supports [S3-compatible storage services](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html#using-other-s3-compatible-services). However, Dask uses Boto3 for this and AIStore does not support Boto3 as it does not follow HTTP(s) protocol. For more information on AIStore compatibility with S3, refer [here](https://github.com/NVIDIA/aistore/blob/main/docs/s3compat.md).\n",
     "\n",
     "While we cannot directly write to AIStore with Dask API (`dd.to_csv(\"ais://bucket/file.csv\")` not supported as of now), we can convert the DataFrame to bytes and move it using AIStore's own API:"
    ]
@@ -668,7 +668,7 @@
    "source": [
     "However, the above method of converting a Dask DataFrame to a Pandas DataFrame may not be ideal as it defeats some of the performance advantages of using Dask. Converting a Dask DataFrame to a Pandas DataFrame only makes sense to do if the data can fully fit into memory (i.e. data has been filtered and is now much smaller).\n",
     "\n",
-    "For much larger datasets, [AIS-ETL](https://github.com/NVIDIA/aistore/blob/master/docs/etl.md) may offer better performance while offering similar ETL capabilities to those demonstrated above. Please refer [here](https://github.com/NVIDIA/aistore/blob/master/docs/etl.md) for more information."
+    "For much larger datasets, [AIS-ETL](https://github.com/NVIDIA/aistore/blob/main/docs/etl.md) may offer better performance while offering similar ETL capabilities to those demonstrated above. Please refer [here](https://github.com/NVIDIA/aistore/blob/main/docs/etl.md) for more information."
    ]
   },
   {
@@ -723,8 +723,8 @@
    "source": [
     "* [Dask API](https://docs.dask.org/en/stable/dataframe-api.html)\n",
     "* [Pandas API](https://pandas.pydata.org/docs/reference/index.html)\n",
-    "* [AIStore Python SDK](https://github.com/NVIDIA/aistore/blob/master/docs/python_sdk.md)\n",
-    "* [AIS-ETL](https://github.com/NVIDIA/aistore/blob/master/docs/etl.md)"
+    "* [AIStore Python SDK](https://github.com/NVIDIA/aistore/blob/main/docs/python_sdk.md)\n",
+    "* [AIS-ETL](https://github.com/NVIDIA/aistore/blob/main/docs/etl.md)"
    ]
   }
  ],

python/examples/sdk/accessing-objects.ipynb

@@ -254,7 +254,7 @@
    },
    "source": [
     "## Working with multiple objects\n",
-    "AIS supports multi-object operations on groups of objects. For examples of working with groups of objects see [here](https://github.com/NVIDIA/aistore/blob/master/python/examples/sdk/multi-object-operations.ipynb)"
+    "AIS supports multi-object operations on groups of objects. For examples of working with groups of objects see [here](https://github.com/NVIDIA/aistore/blob/main/python/examples/sdk/multi-object-operations.ipynb)"
    ]
   }
  ],

python/examples/sdk/multi-object-operations.ipynb

@@ -125,7 +125,7 @@
    },
    "source": [
     "### Create Object Group by Template String\n",
-    "String templates can be passed directly to AIS following the [syntax described here](https://github.com/NVIDIA/aistore/blob/master/docs/batch.md#operations-on-multiple-selected-objects)"
+    "String templates can be passed directly to AIS following the [syntax described here](https://github.com/NVIDIA/aistore/blob/main/docs/batch.md#operations-on-multiple-selected-objects)"
    ]
   },
   {
@@ -226,7 +226,7 @@
    "source": [
     "#### Transform -- Provide an ETL to be performed on each object so the result appears in the destination bucket.\n",
     "\n",
-    "Note: This step requires the AIS cluster to be running in Kubernetes; see [getting_started](https://github.com/NVIDIA/aistore/blob/master/docs/getting_started.md#kubernetes-playground) for setup info."
+    "Note: This step requires the AIS cluster to be running in Kubernetes; see [getting_started](https://github.com/NVIDIA/aistore/blob/main/docs/getting_started.md#kubernetes-playground) for setup info."
    ]
   },
   {

python/examples/sdk/sdk-etl-tutorial.ipynb

@@ -102,7 +102,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "Refer to more ETL templates [here](https://github.com/NVIDIA/aistore/blob/master/python/aistore/sdk/etl_templates.py)."
+    "Refer to more ETL templates [here](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk/etl)."
    ],
    "metadata": {
     "collapsed": false