Fix links in the readme (#3)

* Remove GPU badge and email ref. Add windows to OS badge. Add real links. Signed-off-by: Jeremy Fowers <jeremy.fowers@amd.com> * Link to OMZ Signed-off-by: Jeremy Fowers <jeremy.fowers@amd.com> * corrections to code.md Signed-off-by: Jeremy Fowers <jeremy.fowers@amd.com> * Increment the minor version number Signed-off-by: Jeremy Fowers <jeremy.fowers@amd.com> --------- Signed-off-by: Jeremy Fowers <jeremy.fowers@amd.com>
onnx · Dec 1, 2023 · e825501 · e825501
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diff --git a/README.md b/README.md
@@ -2,8 +2,7 @@
 
 [![Turnkey tests](https://github.com/onnx/turnkeyml/actions/workflows/test_turnkey.yml/badge.svg)](https://github.com/onnx/turnkeyml/tree/main/test "Check out our tests")
 [![Build API tests](https://github.com/onnx/turnkeyml/actions/workflows/test_build_api.yml/badge.svg)](https://github.com/onnx/turnkeyml/tree/main/test "Check out our tests")
-[![Turnkey GPU tests](https://github.com/onnx/turnkeyml/actions/workflows/test_gpu_turnkey.yml/badge.svg)](https://github.com/onnx/turnkeyml/tree/main/test "Check out our tests")
-[![OS - Linux](https://img.shields.io/badge/OS-Linux-blue?logo=linux&logoColor=white)](https://github.com/onnx/turnkeyml/blob/main/docs/install.md "Check out our instructions")ADD WINDOWS
+[![OS - Windows | Linux](https://img.shields.io/badge/OS-windows%20%7C%20linux-blue)](https://github.com/onnx/turnkeyml/blob/main/docs/install.md "Check out our instructions")
 [![Made with Python](https://img.shields.io/badge/Python-3.8,3.10-blue?logo=python&logoColor=white)](https://github.com/onnx/turnkeyml/blob/main/docs/install.md "Check out our instructions")
 
 
@@ -22,15 +21,15 @@ pip install turnkeyml
 turnkey my_model.py
 ```
 
-The [installation guide](ADDLINK), [tutorials](ADDLINK), and [user guide](ADDLINK) have everything you need to know.
+The [installation guide](https://github.com/onnx/turnkeyml/blob/main/docs/install.md), [tutorials](https://github.com/onnx/turnkeyml/tree/main/examples/cli), and [user guide](https://github.com/onnx/turnkeyml/blob/main/docs/tools_user_guide.md) have everything you need to know.
 
 ## Use Cases
 
 TurnkeyML is designed to support the following use cases. Of course, it is also quite flexible, so we are sure you will come up with some use cases of your own too.
 
 | Use Case               | Description |
 |------------------------|-------------|
-| ONNX Model Zoo         | Export thousands of ONNX files across different opsets and data types. This is how we generated the contents of the new [ONNX Model Zoo](ADDLINK). |
+| ONNX Model Zoo         | Export thousands of ONNX files across different opsets and data types. This is how we generated the contents of the new [ONNX Model Zoo](https://github.com/onnx/models). |
 | Performance validation | Measure latency and throughput in hardware across devices and runtimes to understand product-market fit. |
 | Functional coverage    | Measure the functional coverage of toolchain/hardware combinations over a large corpus of models (e.g., how many models are supported by a novel compiler?). |
 | Stress testing         | Run millions of inferences across thousands of models and log all the results to find the bugs in a HW/SW stack. |
@@ -146,7 +145,7 @@ Summary spreadsheet saved at /home/jfowers/2023-11-30.csv
 
 You're probably starting to get the idea :rocket:
 
-There's a lot more features you can learn about in the [tutorials](ADDLINK) and [user guide](ADDLINK).
+There's a lot more features you can learn about in the [tutorials](https://github.com/onnx/turnkeyml/tree/main/examples/cli) and [user guide](https://github.com/onnx/turnkeyml/blob/main/docs/tools_user_guide.md).
 
 ## What's Inside
 
@@ -157,9 +156,9 @@ The TurnkeyML framework has 5 core components:
 - **Reporting tool**: Visualize statistics about the models, builds, and invocations.  
 - **Models corpus**: Hundreds of popular PyTorch models that are ready for use with `turnkey`.
 
-All of this is seamlessly integrated together such that a command like `turnkey repo/models/corpus/script.py` gets you all of the functionality in one shot. Or you can access functionality piecemeal with commands and APIs like `turnkey analyze script.py` or `build_model(my_model_instance)`. The [tutorials](ADDLINK) show off the individual features.
+All of this is seamlessly integrated together such that a command like `turnkey repo/models/corpus/script.py` gets you all of the functionality in one shot. Or you can access functionality piecemeal with commands and APIs like `turnkey analyze script.py` or `build_model(my_model_instance)`. The [tutorials](https://github.com/onnx/turnkeyml/tree/main/examples/cli) show off the individual features.
 
-You can read more about the code organization [here](ADDLINK).
+You can read more about the code organization [here](https://github.com/onnx/turnkeyml/blob/main/docs/code.md).
 
 ## Extensibility
 
@@ -173,7 +172,7 @@ You can read more about the code organization [here](ADDLINK).
 
 This repository is home to a diverse corpus of hundreds of models. We are actively working on increasing the number of models in our model library. You can see the set of models in each category by clicking on the corresponding badge.
 
-Evaluating a new model is as simple as taking a Python script that instantiates and invokes a PyTorch `torch.nn.module` and call `turnkey` on it. Read about model contributions [here](ADDLINK).
+Evaluating a new model is as simple as taking a Python script that instantiates and invokes a PyTorch `torch.nn.module` and call `turnkey` on it. Read about model contributions [here](https://github.com/onnx/turnkeyml/blob/main/docs/contribute.md#contributing-a-model).
 
 ### Plugins
 
@@ -186,16 +185,16 @@ If you need more, the TurnkeyML plugin API lets you extend the build and runtime
 > turnkey my_model.py --sequence my-custom-sequence --device my-custom-device --runtime my-custom-runtime --rt-args my-custom-args
 ```
 
-All of the built-in sequences, runtimes, and devices are implemented against the plugin API. Check out this [example plugin](ADDLINK) and the [plugin API guide](ADDLINK).
+All of the built-in sequences, runtimes, and devices are implemented against the plugin API. Check out the [example plugins](https://github.com/onnx/turnkeyml/tree/main/examples/cli/plugins) and the [plugin API guide](https://github.com/onnx/turnkeyml/blob/main/docs/contribute.md#contributing-a-plugin).
 
 ## Contributing
 
 We are actively seeking collaborators from across the industry. If you would like to contribute to this project, please check out our [contribution guide](https://github.com/onnx/turnkeyml/blob/main/docs/contribute.md).
 
 ## Maintainers
 
-This project is sponsored by the [ONNX Model Zoo](ADDLINK) special interest group (SIG). It is maintained by @danielholanda @jeremyfowers @ramkrishna @vgodsoe in equal measure. You can reach us at [turnkeyml@???.com](ADDLINK) or by filing an [issue](ADDLINK).
+This project is sponsored by the [ONNX Model Zoo](https://github.com/onnx/models) special interest group (SIG). It is maintained by @danielholanda @jeremyfowers @ramkrishna @vgodsoe in equal measure. You can reach us by filing an [issue](https://github.com/onnx/turnkeyml/issues).
 
 ## License
 
-This project is licensed under the [Apache 2.0 License](ADDLINK).
+This project is licensed under the [Apache 2.0 License](https://github.com/onnx/turnkeyml/blob/main/LICENSE).
diff --git a/docs/code.md b/docs/code.md
@@ -11,12 +11,12 @@ The TurnkeyML source code has a few major top-level directories:
 - `models`: the corpora of models that makes up the TurnkeyML models (see [the models readme](https://github.com/onnx/turnkeyml/blob/main/models/readme.md)).
   - Each subdirectory under `models` represents a corpus of models pulled from somewhere on the internet. For example, `models/torch_hub` is a corpus of models from [Torch Hub](https://github.com/pytorch/hub).
 - `src/turnkey`: source code for the TurnkeyML tools (see [Benchmarking Tools](#benchmarking-tools) for a description of how the code is used).
-  - `src/turnkeyml/analysis`: functions for profiling a model script, discovering model instances, and invoking `benchmark_model()` on those instances.
-  - `src/turnkeyml/api`: implements the benchmarking APIs.
-  - `src/turnkeyml/cli`: implements the `turnkey` CLI.
+  - `src/turnkeyml/analyze`: functions for profiling a model script, discovering model instances, and invoking `benchmark_model()` on those instances.
+  - `src/turnkeyml/run`: implements the runtime and device plugin APIs and the built-in runtimes and devices.
+  - `src/turnkeyml/cli`: implements the `turnkey` CLI and reporting tool.
   - `src/turnkeyml/common`: functions common to the other modules.
   - `src/turnkeyml/version.py`: defines the package version number.
-- `src/turnkeyml/build`: source code for the build API (see [Model Build Tool](#model-build-tool))
+  - `src/turnkeyml/build`: source code for the build API (see [Model Build Tool](#model-build-tool))
 - `test`: tests for the TurnkeyML tools.
   - `test/analysis.py`: tests focusing on the analysis of model scripts.
   - `test/cli.py`: tests focusing on top-level CLI features.
@@ -88,4 +88,3 @@ The `State` class keeps track of the state of a `build_model()` build. `State` i
 
 1. Easily pass critical information between `Stages` in a standardized way
 1. Facilitate debugging by keeping the latest information and build decisions in one place on disk
-1. Make it easy to collect and report statistics across a large number of builds
diff --git a/src/turnkeyml/version.py b/src/turnkeyml/version.py
@@ -1 +1 @@
-__version__ = "0.2.0"
+__version__ = "0.3.0"