Removing links to run on Gradient

CONTRIBUTING.md

@@ -4,36 +4,6 @@
 
 You can contribute to the development of the BESS-KGE project, even if you don't have access to IPUs (you can use the [IPUModel](https://docs.graphcore.ai/projects/poptorch-user-guide/en/3.2.0/reference.html#poptorch.Options.useIpuModel) to emulate most functionalities of the physical hardware).
 
-## VS Code server on Paperspace
-
-Setting up a VS Code server on [Paperspace](https://www.paperspace.com/graphcore) will allow you to tunnel into a machine with IPUs from the VS Code web editor or the desktop app. This requires minimum effort and is an excellent solution for developing and testing code directly on IPU hardware. Here's how to do it.
-
-1. Fork the [BESS-KGE repository](https://github.com/graphcore-research/bess-kge).
-
-2. You can launch a 6-hours session on a Paperspace machine with access to 4 IPUs **for free** by using a link of the form:
-    ```
-    https://console.paperspace.com/github/{USERID}/{REPONAME}?container=graphcore%2Fpytorch-paperspace%3A3.3.0-ubuntu-20.04-20230703&machine=Free-IPU-POD4
-    ```    
-
-    where `{USERID}/{REPOPNAME}` is the github address of the forked repository (e.g. `graphcore-research/bess-kge` for the original repo).
-
-3. Start the machine (this will also clone the repo for you) and open up a terminal from the left pane.
-
-    ![terminal_pane](docs/source/images/Terminal3.png)
-
-4. In the terminal, run the command
-    ```shell
-    bash .gradient/launch_vscode_server.sh {tunnel-name}
-    ```
-
-    where `tunnel-name` is an optional argument that you can use to define the name of the remote tunnel (if not set, it will default to `ipu-paperspace`). The script will download and install all dependencies and start the tunnel.
-
-5. When asked, authorize the tunnel through GitHub (with an account having writing privileges to the forked repository). You will be then provided with the tunnel link. Please refer to [this notebook](https://ipu.dev/fmo4AZ) for additional details on these steps and to connect the VS Code desktop app to the remote tunnel. 
-
-6. Once VS Code is connected to the Paperspace machine, run `./dev build` to build all custom ops. You are now ready to start developing!
-
-When closing a session and stopping the Paperspace machine, remember to unregister the tunnel in VS Code as explained in the "Common Issues" paragraph of the [notebook](https://ipu.dev/fmo4AZ). To resume your work, just access the clone of the BESS-KGE repo in the "Projects" section of your Paperspace profile, start a new machine and repeat the operations above. All code changes to the local repo, as well as VS Code settings and extensions installed, will persist across sessions.
-
 ## Setup on local machine
 
 To develop on a local machine, first install the Poplar SDK following the instructions in the [Getting Started guide for your IPU system](https://docs.graphcore.ai/en/latest/getting-started.html#getting-started).

README.md

@@ -3,7 +3,7 @@
 ![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)
 
 [**Installation guide**](#usage)
-| [**Tutorials**](#paperspace-notebook-tutorials)
+| [**Tutorials**](#notebook-tutorials)
 | [**Documentation**](https://graphcore-research.github.io/bess-kge/)
 
 
@@ -82,7 +82,7 @@ All APIs are documented in the [BESS-KGE API documentation](https://graphcore-re
 
 BESS-KGE provides built-in dataloaders for the following datasets. Notice that the use of these datasets is at own risk and Graphcore offers no warranties of any kind. It is the user's responsibility to comply with all license requirements for datasets downloaded with dataloaders in this repository.
 
-| Dataset | Builder method | Entities | Entity types | Relation types | Triples | License | 
+| Dataset | Builder method | Entities | Entity types | Relation types | Triples | License |
 | --- | --- | --- | --- | --- | --- | --- |
 | [ogbl-biokg](https://ogb.stanford.edu/docs/linkprop/#ogbl-biokg) | [KGDataset.build_ogbl_biokg](https://graphcore-research.github.io/bess-kge/generated/besskge.dataset.KGDataset.html#besskge.dataset.KGDataset.build_ogbl_biokg) | 93,773 | 5 | 51 | 5,088,434 | CC-0 |
 | [ogbl-wikikg2](https://ogb.stanford.edu/docs/linkprop/#ogbl-wikikg2) | [KGDataset.build_ogbl_wikikg2](https://graphcore-research.github.io/bess-kge/generated/besskge.dataset.KGDataset.html#besskge.dataset.KGDataset.build_ogbl_wikikg2) | 2,500,604 | 1 | 535 | 16,968,094 | CC-0 |
@@ -183,14 +183,14 @@ pip install git+https://github.com/graphcore-research/bess-kge.git
 import besskge
 ```
 
-## Paperspace notebook tutorials
+## Notebook tutorials
 
 For a walkthrough of the `besskge` library functionalities, see our Jupyter notebooks. We recommend the following sequence:
-1. [KGE training and inference on the OGBL-BioKG dataset](notebooks/1_biokg_training_inference.ipynb) [![Run on Gradient](docs/gradient-badge.svg)](https://console.paperspace.com/github/graphcore-research/bess-kge?container=graphcore%2Fpytorch-paperspace%3A3.3.0-ubuntu-20.04-20230703&machine=Free-IPU-POD4&file=%2Fnotebooks%2F1_biokg_training_inference.ipynb)
-2. [Link prediction on the YAGO3-10 dataset](notebooks/2_yago_topk_prediction.ipynb) [![Run on Gradient](docs/gradient-badge.svg)](https://console.paperspace.com/github/graphcore-research/bess-kge?container=graphcore%2Fpytorch-paperspace%3A3.3.0-ubuntu-20.04-20230703&machine=Free-IPU-POD4&file=%2Fnotebooks%2F2_yago_topk_prediction.ipynb)
-3. [FP16 weights and compute on the OGBL-WikiKG2 dataset](notebooks/3_wikikg2_fp16.ipynb) [![Run on Gradient](docs/gradient-badge.svg)](https://console.paperspace.com/github/graphcore-research/bess-kge?container=graphcore%2Fpytorch-paperspace%3A3.3.0-ubuntu-20.04-20230703&machine=Free-IPU-POD4&file=%2Fnotebooks%2F3_wikikg2_fp16.ipynb)
+1. [KGE training and inference on the OGBL-BioKG dataset](notebooks/1_biokg_training_inference.ipynb)
+2. [Link prediction on the YAGO3-10 dataset](notebooks/2_yago_topk_prediction.ipynb)
+3. [FP16 weights and compute on the OGBL-WikiKG2 dataset](notebooks/3_wikikg2_fp16.ipynb)
 
-For pointers on how to run BESS-KGE on a custom Knowledge Graph dataset, see the notebook [Using BESS-KGE with your own data](notebooks/0_custom_KG_dataset.ipynb) [![Run on Gradient](docs/gradient-badge.svg)](https://console.paperspace.com/github/graphcore-research/bess-kge?container=graphcore%2Fpytorch-paperspace%3A3.3.0-ubuntu-20.04-20230703&machine=Free-IPU-POD4&file=%2Fnotebooks%2F0_custom_KG_dataset.ipynb)
+For pointers on how to run BESS-KGE on a custom Knowledge Graph dataset, see the notebook [Using BESS-KGE with your own data](notebooks/0_custom_KG_dataset.ipynb) 
 
 ## Contributing
 
@@ -205,4 +205,4 @@ Copyright (c) 2023 Graphcore Ltd. Licensed under the MIT License.
 
 The included code is released under the MIT license (see [details of the license](LICENSE)).
 
-See [notices](NOTICE.md) for dependencies, credits, derived work and further details.
+See [notices](NOTICE.md) for dependencies, credits, derived work and further details.

dev

@@ -104,7 +104,7 @@ def copyright() -> None:
     """check for Graphcore copyright headers on relevant files"""
     command = (
         "find Makefile "
-        + " ".join(PYTHON_ROOTS + ["besskge/custom_ops", ".gradient/"])
+        + " ".join(PYTHON_ROOTS + ["besskge/custom_ops"])
         + " -type f -not -name *.pyc"
         " | xargs grep -L 'Copyright (c) 202. Graphcore Ltd[.] All rights reserved[.]'"
     )