MLBEDSW-5995: Clarify installation instructions in README
Signed-off-by: James Peet <james.peet@arm.com>
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: I4c9acb04a9df2181829e3a98aab840f32ae6458e
diff --git a/README.md b/README.md
index 40d9945..e223e88 100644
--- a/README.md
+++ b/README.md
@@ -29,27 +29,26 @@
## TensorFlow Support
-* Vela 3.1.0 to current supports TensorFlow 2.5
+* Vela 3.3.0 to current supports TensorFlow 2.7
+* Vela 3.1.0 to 3.2.0 supports TensorFlow 2.5
* Vela 2.1.0 to 3.0.0 supports TensorFlow 2.4
* Vela 2.0.0 to 2.0.1 supports TensorFlow 2.3
* Vela 0.1.0 to 1.2.0 supports TensorFlow 2.1
## Environment
-Vela runs on the Linux and Microsoft Windows 10 operating systems, see note in
-Installation section below.
+Vela runs on Linux and Microsoft Windows 10 operating systems.
## Prerequisites
The following should be installed prior to the installation of Vela:
-* Python >= 3.6
+* Python 3.8 or compatible
* Pip3
-* GNU toolchain (GCC, Binutils and libraries)
-
-And optionally:
-
-* Pipenv virtual environment tool
+* A C99 capable compiler and associated toolchain
+ - For Linux operating systems, a GNU toolchain is recommended.
+ - For Microsoft Windows 10, Microsoft Visual C++ 14.2 Build Tools is recommended.
+ See <https://wiki.python.org/moin/WindowsCompilers>
## Installation
@@ -59,10 +58,6 @@
[ML Platform](https://review.mlplatform.org/plugins/gitiles/ml/ethos-u/ethos-u-vela).
Both methods will automatically install all the required dependencies.
-**Note:** For installing on Microsoft Windows 10 you need to have a C99 capable
-toolchain installed. The recommended and tested toolchain is Microsoft Visual
-C++ 14.2 Build Tools, see <https://wiki.python.org/moin/WindowsCompilers>
-
### PyPi
Install Vela from PyPi using the following command:
@@ -83,13 +78,14 @@
```
Once you have the source code, Vela can be installed using the following
-command:
+command from the root directory of the repository:
```bash
pip3 install .
```
-Or, if you use `pipenv`:
+A `Pipfile` is maintained for the project, so users of the virtual environment
+tool `pipenv` may prefer the following command instead:
```bash
pipenv install .
@@ -99,41 +95,25 @@
If you plan to modify the Vela codebase then it is recommended to install Vela
as an editable package to avoid the need to re-install after every modification.
-This is done by adding the `-e` option to the above install commands like so:
+This is done by adding the `-e` option to the install command like so:
```bash
pip3 install -e .
```
-Or, if you use `pipenv`:
-
-```bash
-pipenv install -e .
-```
-
If you plan to contribute to the Vela project (highly encouraged!) then it is
recommended to install Vela along with the pre-commit tools (see
[Vela Testing](TESTING.md) for more details).
## Running
-Vela is run with an input `.tflite` file passed on the command line. This file
-contains the neural network to be compiled. The tool then outputs an optimised
-version with a `_vela.tflite` file prefix, along with the performance estimate
-(EXPERIMENTAL) CSV files, all to the output directory. It also prints a
-performance estimation summary back to the console, see
+Vela is run with an input `.tflite` or `.tosa` (EXPERIMENTAL) file passed on the
+command line. This file contains the neural network to be compiled. The tool then
+outputs an optimised `.tflite` file with a `_vela` suffix in the file name, along
+with performance estimate (EXPERIMENTAL) CSV files, all to the output directory.
+It also prints a performance estimation summary back to the console, see
[Vela Performance Estimation Summary](PERFORMANCE.md).
-If you use the `pipenv` virtual environment tool then first start by spawning a
-shell in the virtual environment:
-
-```bash
-pipenv shell
-```
-
-After which running Vela is the same regardless of whether you are in a virtual
-environment or not.
-
Example usage:
1) Compile the network `my_model.tflite`. The optimised version will be output
@@ -150,14 +130,14 @@
vela --output-dir ./results_dir /path/to/my_model.tflite
```
-3) Compile a network using a particular Ethos-U NPU. The following command
+3) Compile a network targeting a particular Ethos-U NPU. The following command
selects an Ethos-U65 NPU accelerator configured with 512 MAC units.
```bash
vela --accelerator-config ethos-u65-512 my_model.tflite
```
-4) Compile a network while minimizing peak SRAM usage,
-therefore prioritising a lower SRAM usage over runtime performance.
+4) Compile a network while minimizing peak SRAM usage, prioritising lower SRAM
+usage over runtime performance.
```bash
vela --optimise Size my_model.tflite