Merge pull request #269 from mfumanelli/fix_typos

[Speedster] Fix typos and update benchmarks

apps/accelerate/speedster/README.md

@@ -2,9 +2,9 @@
 
 `Speedster` is an open-source module designed to speed up AI inference in just a few lines of code. The library automatically applies the best set of SOTA optimization techniques to achieve the maximum inference speed-up (latency, throughput, model size) physically possible on your hardware (single machine).
 
-`Speedster` makes it easy to combine optimization techniques across the whole software to hardware stack, delivering best in class speed-ups. If you like the idea, give us a star to support the project ⭐
+`Speedster` makes it easy to combine optimization techniques across the whole software-to-hardware stack, delivering best-in-class speed-ups. If you like the idea, give us a star to support the project ⭐
 
-![speedster_benchmarks](https://user-images.githubusercontent.com/42771598/212486740-431328f3-f1e5-47bf-b6c9-b6629399ad09.png)
+![speedster](https://user-images.githubusercontent.com/53374883/225599469-f1a626f0-c001-42bd-bc8b-ec0e966ddad6.png)
 
 The core `Speedster` workflow consists of 3 steps:
 
@@ -79,13 +79,13 @@ optimized_model = load_model("model_save_path")
 
 output = optimized_model(input_sample)
 ```
-For more details, please visit [Getting Started with PyTorch optimization](https://docs.nebuly.com/Speedster/getting_started/pytorch_getting_started/).
+For more details, please visit [Getting Started with PyTorch Optimization](https://docs.nebuly.com/Speedster/getting_started/pytorch_getting_started/).
 
 </details>
 <details>
-<summary>🤗 HuggingFace Transformers </summary>
+<summary>🤗 Hugging Face Transformers </summary>
 
-In this section, we will learn about the 4 main steps needed to optimize 🤗 Huggingface Transformer models:
+In this section, we will learn about the 4 main steps needed to optimize 🤗 Hugging Face Transformer models:
 
 1) Input your model and data
 2) Run the optimization
@@ -98,7 +98,7 @@ In this section, we will learn about the 4 main steps needed to optimize 🤗 Hu
     from transformers import AlbertModel, AlbertTokenizer
     from speedster import optimize_model, save_model
 
-    #1a. Provide input model: Load Albert as example
+    #1a. Provide input model: Load Albert as an example
     model = AlbertModel.from_pretrained("albert-base-v1")
     tokenizer = AlbertTokenizer.from_pretrained("albert-base-v1")
 
@@ -141,7 +141,7 @@ In this section, we will learn about the 4 main steps needed to optimize 🤗 Hu
     from transformers import T5Tokenizer, T5ForConditionalGeneration
     from speedster import optimize_model, save_model
 
-    #1a. Provide input model: Load T5 as example
+    #1a. Provide input model: Load T5 as an example
     model = T5ForConditionalGeneration.from_pretrained("t5-small")
     tokenizer = T5Tokenizer.from_pretrained("t5-small") 
 
@@ -182,7 +182,7 @@ In this section, we will learn about the 4 main steps needed to optimize 🤗 Hu
 </details>
 
 <details>
-<summary>🧨 HuggingFace Diffusers </summary>
+<summary>🧨 Hugging Face Diffusers </summary>
 
 In this section, we will learn about the 4 main steps needed to optimize Stable Diffusion models from the Diffusers library:
 
@@ -239,7 +239,7 @@ optimized_model = load_model("model_save_path", pipe=pipe)
 test_prompt = "futuristic llama with a cyberpunk city on the background"
 output = optimized_model(test_prompt).images[0]
 ```
-For more details, please visit [Getting Started with StableDiffusion optimization](https://docs.nebuly.com/Speedster/getting_started/diffusers_getting_started/).
+For more details, please visit [Getting Started with Stable Diffusion optimization](https://docs.nebuly.com/Speedster/getting_started/diffusers_getting_started/).
 
 </details>
 
@@ -338,8 +338,8 @@ For more details, please visit [Getting Started with ONNX optimization](https://
 
 - [Installation](https://docs.nebuly.com/Speedster/installation/)
 - [Getting started with PyTorch optimization](https://docs.nebuly.com/Speedster/getting_started/pytorch_getting_started/)
-- [Getting started with HuggingFace optimization](https://docs.nebuly.com/Speedster/getting_started/hf_getting_started/)
-- [Getting started with StableDiffusion optimization](https://docs.nebuly.com/Speedster/getting_started/diffusers_getting_started/)
+- [Getting started with Hugging Face optimization](https://docs.nebuly.com/Speedster/getting_started/hf_getting_started/)
+- [Getting started with Stable Diffusion optimization](https://docs.nebuly.com/Speedster/getting_started/diffusers_getting_started/)
 - [Getting started with TensorFlow optimization](https://docs.nebuly.com/Speedster/getting_started/tf_getting_started/)
 - [Getting started with ONNX optimization](https://docs.nebuly.com/Speedster/getting_started/onnx_getting_started/)
 - [Key concepts](https://docs.nebuly.com/Speedster/key_concepts/)
@@ -350,7 +350,7 @@ For more details, please visit [Getting Started with ONNX optimization](https://
 
 # **Key concepts**
 
-`Speedster`'s design reflects our mission to automatically master each and every existing AI acceleration techniques to deliver the **fastest AI ever**. As a result, `Speedster` leverages available enterprise-grade open-source optimization tools. If these tools and  communities already exist, and are distributed under a permissive license (Apache, MIT, etc), we integrate them and happily contribute to their communities. However, many tools do not exist yet, in which case we implement them and open-source the code so that our community can benefit from it.
+`Speedster`'s design reflects our mission to automatically master each and every existing AI acceleration technique to deliver the **fastest AI ever**. As a result, `Speedster` leverages available enterprise-grade open-source optimization tools. If these tools and  communities already exist, and are distributed under a permissive license (Apache, MIT, etc), we integrate them and happily contribute to their communities. However, many tools do not exist yet, in which case we implement them and open-source the code so that our community can benefit from it.
 
 `Speedster` is shaped around **4 building blocks** and leverages a modular design to foster scalability and integration of new acceleration components across the software to hardware stack.
 
@@ -379,12 +379,12 @@ The **compiler stage** leverages the following open-source projects:
 
 
 # **Community**
-We’re developing `Speedster` for and together with our community, so plase get in touch on GitHub or Discord. 
+We’re developing `Speedster` for and together with our community, so please get in touch on GitHub or Discord. 
 
 • **[GitHub issues](https://github.com/nebuly-ai/nebullvm/issues)**: suggest new acceleration components, request new features, and report bugs and improvements.
 
 • **[Discord](https://discord.gg/RbeQMu886J)**: learn about AI acceleration, share exciting projects and hang out with our global community.
 
-The best way to get started is to pick a good-first issue. Please read our [contribution guidelines](https://docs.nebuly.com/contributions/) for a deep dive on how to best contribute to our project!
+The best way to get started is to pick a good-first issue. Please read our [contribution guidelines](https://docs.nebuly.com/contributions/) for a deep dive into how to best contribute to our project!
 
 Don't forget to leave a star ⭐ to support the project and happy acceleration 🚀

apps/accelerate/speedster/docs/en/docs/overview.md

@@ -6,7 +6,7 @@ The library allows you to seamlessy modulate the inference performances of your
 
 `Speedster` makes it easy to combine optimization techniques across the whole software to hardware stack, delivering best in class speed-ups. If you like the idea, give us a star to support the project ⭐
 
-![speedster_benchmarks](https://user-images.githubusercontent.com/42771598/212486740-431328f3-f1e5-47bf-b6c9-b6629399ad09.png)
+![speedster](https://user-images.githubusercontent.com/53374883/225600620-1cd84073-d9b3-43d1-84fa-c3e6c25eb915.png)
 
 The core `Speedster` workflow consists of 3 steps:
 
@@ -17,4 +17,4 @@ The core `Speedster` workflow consists of 3 steps:
 - [x]  **Search**: the library automatically tests every combination of optimization techniques across the software-to-hardware stack (sparsity, quantization, compilers, etc.) that is compatible with your needs and local hardware.
 - [x]  **Serve**: finally, `Speedster` chooses the best configuration of optimization techniques and returns an accelerated version of your model in the DL framework of your choice (just on steroids 🚀).
 
-Now you are ready to start accelerating your models, visit the [Installation](installation.md) section to start right away!
+Now you are ready to start accelerating your models, visit the [Installation](installation.md) section to start right away!

notebooks/speedster/diffusers/Accelerate_Stable_Diffusion_with_Speedster.ipynb

@@ -259,28 +259,6 @@
     "    pipe = StableDiffusionPipeline.from_pretrained(model_id)\n"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "f7653250",
-   "metadata": {},
-   "source": [
-    "We can easily test the loaded model by generating a sample image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b640885",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_prompt = \"futuristic llama with a cyberpunk city on the background\"\n",
-    "\n",
-    "pipe.to(device)\n",
-    "pipe(test_prompt).images[0]"
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -428,6 +406,16 @@
     "If everything worked correctly, let's check the output of the optimized model"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b640885",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_prompt = \"futuristic llama with a cyberpunk city on the background\"\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,