Skip to content

Releases: NVIDIA/DALI

DALI v0.3.0

26 Sep 17:50
@JanuszL JanuszL
v0.3.0
74d9b58
Compare
Choose a tag to compare
DALI v0.3.0 Pre-release
Pre-release

Bug fixes

  • Adjusted PyTorch Dali pipeline to be similar to MXNet example (#107)
  • Add CPU fallback for BMP images and conscious fail for GIF (#124)
  • Enable FileReader shuffling for GPU0 (#134)
  • Fix squeeze for tensor with 1 element
  • Fix segfault in MXNetReader when given bad path to index file
  • Increase timeout, parametrize Python version in Jupyter tests (#126)
  • Fix segfault in Filereader if directory does not exist.
  • Update Workspace docstrings (#111)
  • Allow pkg_config to fail in the search for JpegTurbo
  • Fixed wrong rewind in TFRecord reader (#167)

Improvements

  • Added CPU version of Resize operator (#127)
  • Added Caffe reader to TF multi reader example (#103)
  • Added filtering extensions that FileReader can read (#137)
  • Made DALI understand float16 input from python
  • Added float16 as possible output type to python
  • Added flip operator (#130)
  • Added 'at' method to TensorListGPU (#131)
  • Refactored tests (#91)
  • Shortened git SHA in the Sphinx docs to 7 chars (#108)
  • Made files to be copied during build into build_dir. (#87)
  • Added links to GTC presentation to README
  • Reduced number of pinned memory allocations (#169)

Binary builds

Install via pip:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali==0.3.0

Or use direct download links:

Assets 2
Loading

DALI v0.2.0

27 Aug 22:40
@JanuszL JanuszL
v0.2.0
2319d24
Compare
Choose a tag to compare
DALI v0.2.0 Pre-release
Pre-release

Bug fixes

  • Avoid full construction of the pipeline during construction and fix seed support in serialized pipelines (#16)
  • Fix as_tensor not keeping the parent alive in Python (#60)
  • Fix for "invalid resource handle" in multi-gpu training
  • Fixes to PyTorch example. Need to reset DALI iterators between epochs. Putting model/loss computation back to default stream due to encountered memory access errors otherwise (#15)
  • Move example file_list to proper dir (#38)
  • Added fallback to host decoder when image is not JPEG but PNG instead (like n02105855_2933.JPEG from ImageNet) (#118)

Breaking API changes

  • The API for the Resize operator changed to match other similar operators like ResizeCropMirror.
  • The API for the TensorFlow plugin changed to allow specifying the whole shape of the tensor instead of N, H, and W separately; which enables handling both NCHW and NHWC outputs.
  • The type of labels produced by the TensorFlow plugin have changed. In DALI version 0.1.2, it was always tf.float32. In this release, a new optional parameter called label_type is introduced to the TensorFlow plugin to control the type of label. The default value for label_type is tf.int64 to better align with the label type in TFRecord.

Improvements

  • Add NVTX ranges for Operators run (#73)
  • Add a note about NGC containers in README (#78)
  • Unfused Crop operator and CropCastPermute operator (#50)
  • Make build more restrictive Werror (#71)
  • Add links to docs in README (#72)
  • Expanded TF compatibility tests
  • Add example with multiple readers pluged into TF (#58)
  • Make pkg-config optional for CMake (#59)
  • Resize refactor (#63)
  • Add type casting in Python (#54)
  • Add check that third_party git submodules are synced
  • Add fallback in cmake when .pc file is not available for libjpeg-turbo (#49)
  • Sphinx documentation (#36)
  • Fix nvJpeg include dir (#47)
  • Add private attribute naming convention to Pipeline::current_seed_ (#46)
  • Add a shape argument for the output of the TF plugin (#45)
  • Bump up libturbo-jpeg version to 1.5.3 (#44)
  • Clean up dependencies list and dependency checks (#42)
  • Switch over completely to FindProtobuf.cmake from CMake 3.9.6 (#41)
  • Update README for prerequisites (#40)
  • Add error checking for file_list format in file_loader. (#37)
  • Add test support for various versions of pyTorch (#35)
  • Add polymorphism for TF plugin outputs (#33)
  • Add tensor layout checking (#32)
  • Avoid rebuilding *.cu files during 'make install' after 'make' (#25)
  • Add CUDA 8, OpenCV 2 support and options to disable libjpeg-turbo and nvJPEG (#22)
  • Add CONTRIBUTING.md file and updated contribution section in the README.md (#20)
  • Avoid full construction of the pipeline during construction and fix seed support in serialized pipelines (#16)
  • Add int64 as label type and set it as default (#125)

Binary builds

Install via pip:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali==0.2.0

Or use direct download links:

Assets 2
Loading

DALI v0.1.2

31 Jul 00:50
@cliffwoolley cliffwoolley
v0.1.2
d99027d
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.
Compare
Choose a tag to compare
DALI v0.1.2 Pre-release
Pre-release

Bug fixes

  • Fix compatibility with TensorFlow 1.9 (#52)
  • Update to nvJPEG v0.1.2 to fix batched decoding when a batch contains both grayscale and color images (#79)

Improvements

  • Add Tensorflow 1.7 support (#24)
  • Better overlap when using DALI with multi-GPU in MXNet and pyTorch (#76)

Binary builds

Install via pip:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali==0.1.2

Or use direct download links:

Assets 2
Loading

DALI v0.1.1

29 Jun 20:26
@cliffwoolley cliffwoolley
v0.1.1
6d13bc6
Compare
Choose a tag to compare
DALI v0.1.1 Pre-release
Pre-release

Bug fixes

  • #4 - Race in processing multiple input sets
  • #5 - ImportError with various shared object file dependencies not found
  • #8 - Segfault in ops.FileReader when no files found
  • #12 - Python3 incompatibility in some examples
  • #13 - Crash when importing pre-built DALI PyTorch plugin w/ pre-built PyTorch
  • Pre-built binary includes an updated NVJPEG build that fixes a race condition seen in some DALI pipelines

Improvements

  • Binary compatibility of the pre-built DALI binaries with pre-built DL frameworks is improved (#13).
    • In support of this, most dependencies are now statically linked into the pre-built binaries, and the list of symbols exported from the shared objects are significantly reduced.
    • A beneficial side effect is that CUDA 9.0 Toolkit is no longer required to be installed to use pre-built binaries; only the corresponding NVIDIA Driver is required. This for example allows compatibility with a DL framework otherwise built against CUDA 9.1 or 9.2.

Binary builds

Install via pip:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali==0.1.1

Or use direct download links:

Assets 2
Loading

DALI v0.1.0 : Initial public release

28 Jun 22:33
@cliffwoolley cliffwoolley
v0.1.0
f4087bc
Compare
Choose a tag to compare
DALI v0.1.0 : Initial public release Pre-release
Pre-release

Binaries

Install via pip:
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali==0.1.0

Or use direct download links:

Assets 2
Loading