Add tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl (TF 2.20 + CUDA 12.8, sm_90/sm_120, aarch64)

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,120 @@
+---
+license: apache-2.0
+language:
+  - en
+tags:
+  - tensorflow
+  - tensorflow-gpu
+  - aarch64
+  - arm64
+  - linux-aarch64
+  - cuda
+  - cuda-12
+  - cudnn-9
+  - gpu
+  - blackwell
+  - gb10
+  - sm_90
+  - sm_120
+  - python-3.12
+  - wheel
+  - selfbuilt
+pretty_name: TensorFlow GPU wheels for linux_aarch64 (CUDA 12.8 / cuDNN 9.8)
+size_categories:
+  - n<1K
+---
+
+# TensorFlow 2.20 GPU wheel for linux_aarch64 (CUDA 12.8 / cuDNN 9.8)
+
+Self-built `tensorflow` wheel for the platforms PyPI does **not** ship a
+GPU build for. Produced by
+[`scripts/build_tf_gpu_aarch64.sh`](https://github.com/Infineon/lpwwd/blob/main/scripts/build_tf_gpu_aarch64.sh)
+in the LPWWD pipeline repo on an NVIDIA Spark / GB10 host.
+
+## Why this exists
+
+PyPI ships a CPU-only `tensorflow` wheel for `linux_aarch64`. There is no
+pip-installable GPU TensorFlow on this platform/Python combo, so to get
+GPU acceleration without Docker the wheel has to be built from source.
+A cold from-source build is 2–4 h and ~50–80 GB of bazel artifacts; this
+repo lets every other aarch64 host skip that.
+
+## Contents
+
+| File | Size | sha256 |
+|---|---:|---|
+| `tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl` | ~495 MiB | `6c63ce87206ac1485b5858a100f098674943098da946837b77d8d6c07a7ec35b` |
+| `tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl.sha256` | — | sidecar |
+
+## Build configuration
+
+| Setting | Value |
+|---|---|
+| TensorFlow | `v2.20.0` |
+| Python | 3.12 (cp312) |
+| Platform tag | `linux_aarch64` (ARM 64-bit) |
+| CUDA | 12.8 (hermetic) |
+| cuDNN | 9.8 (hermetic) |
+| Compute capabilities | `9.0` (Hopper) + `12.0` (Blackwell / GB10 `sm_120`) |
+| Device compiler | `nvcc` |
+| Host compiler | `clang-17` (via `--config=nvcc_clang`) |
+| Bazel | 7.4.1 |
+| Build host | NVIDIA Spark (GB10, aarch64, 20 cores, 121.7 GiB unified memory) |
+
+This wheel will run on any `linux_aarch64` host with a CUDA-12.x driver
+and a GPU of compute capability 9.0 or 12.0 (e.g. H100/H200/Hopper and
+Blackwell/GB10). Other compute capabilities are not embedded — if your
+device has e.g. `sm_80` you need a rebuild.
+
+## Install
+
+```bash
+pip download \
+    --no-deps \
+    --dest . \
+    "https://huggingface.co/datasets/infineon/tensorflow-gpu/resolve/main/tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl"
+sha256sum -c <(echo "6c63ce87206ac1485b5858a100f098674943098da946837b77d8d6c07a7ec35b  tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl")
+pip install --upgrade "./tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl"
+```
+
+Or with `huggingface_hub`:
+
+```python
+from huggingface_hub import hf_hub_download
+whl = hf_hub_download(
+    repo_id="infineon/tensorflow-gpu",
+    repo_type="dataset",
+    filename="tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl",
+)
+```
+
+Then verify the GPU is visible:
+
+```python
+import tensorflow as tf
+print(tf.__version__, tf.config.list_physical_devices("GPU"))
+```
+
+## Compatibility matrix
+
+| Host arch | CUDA driver | GPU SM | Status |
+|---|---|---|---|
+| `linux_aarch64` | 12.8+ | `sm_90` (Hopper) | OK |
+| `linux_aarch64` | 12.8+ | `sm_120` (Blackwell / GB10) | OK |
+| `linux_aarch64` | 12.8+ | other SM | rebuild required |
+| `linux_x86_64` | — | — | wrong arch; use upstream PyPI |
+| `macOS` / Windows | — | — | not supported |
+
+## Provenance
+
+Built from the upstream `tensorflow/tensorflow` repo at tag `v2.20.0`
+(no patches) using
+[`scripts/build_tf_gpu_aarch64.sh`](https://github.com/Infineon/lpwwd/blob/main/scripts/build_tf_gpu_aarch64.sh).
+The build script pins all toolchain versions (Bazel, CUDA, cuDNN, clang)
+and is the single source of truth — re-running it on a fresh aarch64
+host with `TF_VERSION=v2.20.0` reproduces this wheel bit-identically
+modulo timestamps.
+
+## License
+
+TensorFlow itself is Apache-2.0. This dataset card is also Apache-2.0.

tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c63ce87206ac1485b5858a100f098674943098da946837b77d8d6c07a7ec35b
+size 518467640

tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl.sha256

@@ -0,0 +1 @@
+6c63ce87206ac1485b5858a100f098674943098da946837b77d8d6c07a7ec35b  tensorflow-2.20.0.dev0+selfbuilt-cp312-cp312-linux_aarch64.whl