fix broken references

Signed-off-by: Gofastasf <[email protected]>

Author: Gofastasf

.github/workflows/link-checker.yml

@@ -32,6 +32,13 @@ jobs:
           echo "https://gist.github.com/*" >> .lycheeignore
           echo "https://www.youtube.com/embed/*" >> .lycheeignore
           echo "https://github.com/NVIDIA/aistore/commit/*" >> .lycheeignore
+          echo "https://www.uber.com/blog/scaling-hdfs/" >> .lycheeignore
+          echo "https://www.oracle.com/cloud/*" >> .lycheeignore
+          echo "https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=%22small+file+problem%22" >> .lycheeignore
+          echo "https://stackoverflow.com/*" >> .lycheeignore
+          echo "http://localhost:16686" >> .lycheeignore
+          echo "http://prometheus-server:9090/" >> .lycheeignore
+          echo "https://linux.die.net/man/1/ionice" >> .lycheeignore
 
       - name: Link Checker
         id: lychee

deploy/prod/docker/single/README.md

@@ -45,7 +45,7 @@ This message shows that your installation appears to be working correctly.
 ...
 ```
 
-The Docker image used to deploy AIS clusters in this guide is `aistorage/cluster-minimal:latest`, which can be found [here](https://hub.docker.com/repository/docker/aistorage/cluster-minimal) on Docker Hub. `aistorage/cluster-minimal:latest` is an all-in-one, custom Docker image with AIS resources pre-installed.
+The Docker image used to deploy AIS clusters in this guide is `aistorage/cluster-minimal:latest`, which can be found [here](https://hub.docker.com/r/aistorage/cluster-minimal) on Docker Hub. `aistorage/cluster-minimal:latest` is an all-in-one, custom Docker image with AIS resources pre-installed.
 
 ### <ins>CLI
 

docs/_posts/2021-10-29-ais-etl-3.md

@@ -97,7 +97,7 @@ loader = wds.WebLoader(
 #### Comments to the code above
 
 * [AIS Python client](https://github.com/NVIDIA/ais-etl/tree/post-3/package) helps with WebDataset data loader initialization. In this case, WebDataset loads a single `.tar` shard with `jpg` images, and transforms each image in the batch.
-* `.decode("pil")` indicates torchvision data augmentation (for details, see [WebDataset docs](https://webdataset.github.io/webdataset/examples/webdataset/wds-notes/#data-decoding) on decoding).
+* `.decode("pil")` indicates torchvision data augmentation (for details, see [WebDataset docs](https://rom1504.github.io/webdataset/decoding/) on decoding).
 * `.map_tuple` does the actual heavy lifting applying torchvision transforms.
 
 Let's visually compare original images with their loaded-and-transformed counterparts:

docs/_posts/2022-07-20-python-sdk.md

@@ -12,7 +12,7 @@ Python has grounded itself as a popular language of choice among data scientists
 
 [AIStore Python SDK](https://github.com/NVIDIA/aistore/tree/main/python/aistore) is a project which includes a growing library of client-side APIs to easily access and utilize AIStore clusters, objects, and buckets, as well as a number of tools for AIStore usage/integration with PyTorch.
 
-The [AIStore Python API](https://aistore.nvidia.com/docs/python-sdk) is essentially a Python port of AIStore's [Go APIs](https://github.com/NVIDIA/aistore/tree/main/api). In terms of functionality, the AIStore Python and Go APIs are quite similar, both of which essentially make simple [HTTP requests](https://aiatscale.org/docs/http-api#api-reference) to an AIStore endpoint. The HTTP requests allow the APIs to interact (reads and writes) with an AIStore instance's metadata. The API provides convenient and flexible ways (similar to those provided by the [CLI](https://aiatscale.org/docs/cli)) to move data (as objects) in and out of buckets on AIStore, manage AIStore clusters, and much more.
+The [AIStore Python API](https://aistore.nvidia.com/docs/python-sdk) is essentially a Python port of AIStore's [Go APIs](https://github.com/NVIDIA/aistore/tree/main/api). In terms of functionality, the AIStore Python and Go APIs are quite similar, both of which essentially make simple [HTTP requests](https://aiatscale.org/docs/http_api#api-reference) to an AIStore endpoint. The HTTP requests allow the APIs to interact (reads and writes) with an AIStore instance's metadata. The API provides convenient and flexible ways (similar to those provided by the [CLI](https://aiatscale.org/docs/cli)) to move data (as objects) in and out of buckets on AIStore, manage AIStore clusters, and much more.
 
 This technical blog will demonstrate a few potential ways the Python API provided in the [Python SDK](https://github.com/NVIDIA/aistore/tree/main/python/aistore) could be used with a running AIStore instance to manage and utilize data.
 

docs/_posts/2022-08-15-dask-data-analysis.md

@@ -43,7 +43,7 @@ This technical blog will dive into [Dask `DataFrames`](https://examples.dask.org
 
 The [Dask DataFrame](https://docs.dask.org/en/stable/dataframe.html#dask-dataframe) is a data structure based on the `pandas.dataframe` (data structure) representing two-dimensional, size-mutable tabular data. Dask DataFrames consist of many Pandas DataFrames arranged along the *index*. In fact, the Dask DataFrame API [copies](https://docs.dask.org/en/stable/dataframe.html#dask-dataframe-copies-the-pandas-dataframe-api) the Pandas DataFrame API, and should be very familiar to previous Pandas users.
 
-The `dask.dataframe` library, and most other Dask libraries, supports data access via [HTTP(s)](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html#http-s). AIStore, on the other hand, provides both native and Amazon S3 compatible [REST API](https://aiatscale.org/docs/http-api), which means that data stored on AIStore can be accessed and used directly from/by Dask clients.
+The `dask.dataframe` library, and most other Dask libraries, supports data access via [HTTP(s)](https://docs.dask.org/en/stable/how-to/connect-to-remote-data.html#http-s). AIStore, on the other hand, provides both native and Amazon S3 compatible [REST API](https://aiatscale.org/docs/http_api), which means that data stored on AIStore can be accessed and used directly from/by Dask clients.
 
 We can instantiate a Dask DataFrame, loading a sample CSV residing in an AIStore bucket as follows: 
 

docs/_posts/2023-04-10-tco-any-to-any.md

@@ -20,7 +20,7 @@ TL;DR a simple demonstration that does exactly that (and see detailed comments i
 
 ![show performance and running jobs](/assets/tco-any-to-any/show-perf-job.png)
 
-But when the traffic is running and the parts are getting periodically killed and recovered in a variety of realistic ways - then you would maybe want to watch it via [Prometheus](https://aiatscale.org/docs/prometheus) or Graphite/Grafana. Or, at the very least, via 'ais show performance' - the poor man's choice that's always available.
+But when the traffic is running and the parts are getting periodically killed and recovered in a variety of realistic ways - then you would maybe want to watch it via [Prometheus](https://aistore.nvidia.com/docs/monitoring-prometheus) or Graphite/Grafana. Or, at the very least, via 'ais show performance' - the poor man's choice that's always available.
 
 > `ais show performance --help` for details
 

docs/_posts/2024-11-21-enhancing-object-file-performance-with-zero-copy-techniques.md

@@ -76,6 +76,6 @@ Looking ahead, we aim to further enhance `ObjectFile` with parallelized read-ahe
     - [Source](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk/obj/obj_file/object_file.py)
     - [Docs](https://github.com/NVIDIA/aistore/blob/main/docs/python_sdk.md#obj.obj_file.object_file.ObjectFile)
     - [Demo](https://github.com/NVIDIA/aistore/blob/main/python/examples/sdk/resilient-streaming-object-file.ipynb)
-- [Benchmark Script](https://github.com/NVIDIA/aistore/blob/main/python/examples/sdk/object_file/obj-read-benchmark.py)
+- [Benchmark Script](https://github.com/NVIDIA/aistore/blob/main/python/tests/perf/object_file/obj-read-benchmark.py)
 - [Python MemoryView Documentation](https://docs.python.org/3/c-api/memoryview.html)
 - [Python `io.BufferedIOBase` Documentation](https://docs.python.org/3/library/io.html#io.BufferedIOBase)

docs/monitoring-kubernetes.md

@@ -775,7 +775,7 @@ To correlate issues with metrics:
 ## Further Reading
 
 - [AIStore K8s Repository](https://github.com/NVIDIA/ais-k8s)
-- [Prometheus Operator Documentation](https://github.com/prometheus-operator/prometheus-operator/blob/main/Documentation/user-guides/getting-started.md)
+- [Prometheus Operator Documentation](https://prometheus-operator.dev/docs/developer/getting-started/)
 - [Kubernetes Monitoring Best Practices](https://kubernetes.io/docs/tasks/debug-application-cluster/resource-usage-monitoring/)
 - [Grafana Loki Documentation](https://grafana.com/docs/loki/latest/)
 - [Fluent Bit Kubernetes Documentation](https://docs.fluentbit.io/manual/installation/kubernetes)

docs/performance.md

@@ -251,7 +251,7 @@ noatime,nodiratime,logbufs=8,logbsize=256k,largeio,inode64,swalloc,allocsize=131
 
 External links:
 
-* [The atime and noatime attribute](http://en.tldp.org/LDP/solrhe/Securing-Optimizing-Linux-RH-Edition-v1.3/chap6sec73.html)
+* [The atime and noatime attribute](https://tldp.org/LDP/solrhe/Securing-Optimizing-Linux-RH-Edition-v1.3/chap6sec73.html)
 * [Mount with noatime](https://access.redhat.com/documentation/en-us/red_hat_enterprise_linux/6/html/global_file_system_2/s2-manage-mountnoatime)
 * [Gain 30% Linux Disk Performance with noatime](https://lonesysadmin.net/2013/12/08/gain-30-linux-disk-performance-noatime-nodiratime-relatime)
 

python/aistore/sdk/README.md

@@ -2,7 +2,7 @@
 
 AIS Python SDK provides a (growing) set of client-side APIs to access and utilize AIS clusters, buckets, and objects.
 
-The project is, essentially, a Python port of the [AIS Go APIs](https://aistore.nvidia.com/docs/http-api), with additional objectives that prioritize *utmost convenience for Python developers*.
+The project is, essentially, a Python port of the [AIS Go APIs](https://aistore.nvidia.com/docs/http_api), with additional objectives that prioritize *utmost convenience for Python developers*.
 
 The SDK also includes the `authn` sub-package for managing authentication, users, roles, clusters, and tokens. For more details, refer to the [AuthN sub-package README](https://github.com/NVIDIA/aistore/blob/main/python/aistore/sdk/authn/README.md).