Additional documentation changes with SDK 3.0 release

Author: RouxJohannes

README.md

@@ -10,7 +10,7 @@ To see what's new and easily filter applications by domain and framework, please
 
 For more detailed benchmark information, please visit our [Performance Results page](https://www.graphcore.ai/performance-results).
 
-> The code presented here requires using Poplar SDK 2.6.x
+> The code presented here requires using Poplar SDK 3.0.x
 
 Please install and enable the Poplar SDK following the instructions in the [Getting Started](https://docs.graphcore.ai/en/latest/getting-started.html#pod-system-getting-started-guides) guide for your IPU system.
 
@@ -67,7 +67,7 @@ If you require POD128/256 setup and configuration for our applications, please c
 | Group BERT | NLP | Training |[TensorFlow 1](nlp/bert/tensorflow1/README.md#GroupBERT_model) |
 | Packed BERT | NLP | Training |[PyTorch](nlp/bert/pytorch), [PopART](nlp/bert/popart) |
 | GPT2 | NLP | Training |[PyTorch](nlp/gpt2/pytorch) , [Hugging Face Optimum](https://huggingface.co/Graphcore/gpt2-medium-ipu) |
-| GPTJ | NLP | Training |[PopXL](nlp/gpt_j/popxl)|  
+| GPTJ | NLP | Training |[PopXL](nlp/gpt_j/popxl)|
 | GPT3-2.7B | NLP | Training |[PopXL](nlp/gpt3_2.7B/popxl) |
 | RoBERTa | NLP | Training | [Hugging Face Optimum](https://huggingface.co/Graphcore/roberta-large-ipu)|
 | DeBERTa | NLP | Training | [Hugging Face Optimum](https://huggingface.co/Graphcore/deberta-base-ipu)|
@@ -147,7 +147,7 @@ If you require POD128/256 setup and configuration for our applications, please c
 | Model | Domain | Type |Links |
 | ------- | ------- |------- | ------- |
 | MNIST RigL | Dynamic Sparsity | Training | [TensorFlow 1](sparsity/dynamic_sparsity/tensorflow1/mnist_rigl) |
-| Autoregressive Language Modelling | Dynamic Sparsity | Training | [TensorFlow 1](sparsity/dynamic_sparsity/tensorflow1/language_modelling) 
+| Autoregressive Language Modelling | Dynamic Sparsity | Training | [TensorFlow 1](sparsity/dynamic_sparsity/tensorflow1/language_modelling)
 | Block-Sparse library  | Sparsity | Training & Inference | [PopART](sparsity/block_sparse/popart) , [TensorFlow 1](sparsity/block_sparse/tensorflow1)|
 
 
@@ -178,7 +178,7 @@ The following applications have been archived. More information can be provided
 | Minigo | Reinforcement Learning | Training | TensorFlow 1|
 
 
-<br> 
+<br>
 
 ## Developer Resources
 - [Documentation](https://docs.graphcore.ai/en/latest/): Explore our software documentation, user guides, and technical notes
@@ -202,7 +202,7 @@ For more information on using the examples-utils benchmarking module, please ref
 ## PopVision™ Tools
 Visualise your code's inner workings with a user-friendly, graphical interface to optimise your machine learning models.
 
-[Download](https://www.graphcore.ai/developer/popvision-tools) PopVision to analyse IPU performance and utilisation. 
+[Download](https://www.graphcore.ai/developer/popvision-tools) PopVision to analyse IPU performance and utilisation.
 
 <br>
 
@@ -229,17 +229,18 @@ Unless otherwise specified by a LICENSE file in a subdirectory, the LICENSE refe
 
 <details>
 <summary>Sep 2022</summary>
-<br> 
+<br>
 
 *  Added those models below to reference models
     *  Vision : MAE (PyTorch), G16 EfficientNet (PyTorch)
     *  NLP : GPTJ (PopXL), GPT3-2.7B (PopXL)
     *  Multimodal : Frozen in time (PyTorchs), ruDalle(Preview) (PopXL)
+*  Deprecating all TensorFlow 1 applications. Support will be removed in the next release.
 </details>
 
 <details>
 <summary>Aug 2022</summary>
-<br> 
+<br>
 
 * Change the folder name of models
     * NLP : from gpt to gpt2
@@ -248,10 +249,10 @@ Unless otherwise specified by a LICENSE file in a subdirectory, the LICENSE refe
 </details>
 <details>
 <summary>July 2022</summary>
-<br> 
+<br>
 
 *  Major reorganisation of all the apps so that they are arranged as: problem domain / model / framework.
-*  Problem domains: Vision, NLP, Speech, GNN, Sparsity, AI for Simultation, Recomender systems, Reinforcement learning, Probability, Multimodal, and Miscellaneous. 
+*  Problem domains: Vision, NLP, Speech, GNN, Sparsity, AI for Simultation, Recomender systems, Reinforcement learning, Probability, Multimodal, and Miscellaneous.
 *  Added those models below to reference models
     *  Vision : Swin (PyTorch) , ViT (Hugging Face Optimum)
     *  NLP : GPT2 Small/Medium/Large (PyTorch), BERT-Base/Large (PopXL), BERT-Base(PaddlePaddle), BERT-Base/Large(Hugging Face Optimum), GPT2 Small/Medium (Hugging Face Optimum), RoBERTa Base/Large(Hugging Face Optimum), DeBERTa(Hugging Face Optimum), HuBERT(Hugging Face Optimum), BART(Hugging Face Optimum), T5 small(Hugging Face Optimum)

vision/cnns/tensorflow2/README.md

@@ -362,7 +362,7 @@ For ease of use, the entire instruction is implemented in the default configurat
 
 Following the training and validation runs, `export_for_serving.py` script can be used to export a model to SavedModel format, which can be subsequently deployed to TensorFlow Serving instances and thus made available for inference. The model should be defined using the same options or configuration file that have been provided to `train.py` for training.
 The following command line creates a SavedModel containing Resnet50 with weights initialized from `checkpoint.h5` file:
-    `python3 scripts/export_for_serving.py --config resnet50_16ipus_8k_bn_pipeline  --export-dir="./resnet50_for_serving/001" --micro-batch-size=1  --checkpoint-file=checkpoint.h5 --keep-pipeline-model=False --iterations=128`
+    `python3 scripts/export_for_serving.py --config resnet50_16ipus_8k_bn_pipeline  --export-dir="./resnet50_for_serving/001" --micro-batch-size=1  --checkpoint-file=checkpoint.h5 --pipeline-serving-model=False --iterations=128`
 
 Please keep in mind that the exported SavedModel can't be used to load the model back into a TensorFlow script, as it only contains the IPU runtime op and an opaque executable and no model state.