Comments (5)
I don't have a link because we never open-sourced that more experimental code. Let me check here if it would be easy to make it visible.
Btw, notice the CPU implementation can be really fast, depending on the inference engine used:
- Using the C++ API (called "Yggdrasil") directly for inference can be much faster than the TensorFlow API, due to the overhead of the framework.
- For some trees Yggdrasil can make use of AVX2, which gets particularly fast.
- For many our use cases, examples with < 100 trees will run on ~ 1 or 2 microseconds (one 70 trees model ran on 700 nanoseconds). It's anecdotal information ... but just something to consider, depending on your needs.
- But GPU was still faster in some cases, in our experiments, but not by an order of magnitude. Again anecdotal, no guarantees on any specific model.
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Hi everyone,
just wanted to share some quick tangentially related info: While there is still no GPU-implementation for TF-DF, TF-DF models can now run on even faster FPGAs for really fast inference through the Conifer project. While this is still very much experimental, feel free to contact us if this is relevant for you.
from decision-forests.
Hi there,
Following along the above comment, I was just curious whether or not GPU support is being implemented in the near future?
Thank you!
from decision-forests.
hi @shayansadeghieh, while we would also very much love to have it, our high priority bucket list is still very full :( so from ourside we will not likely work on this in the near future. Accelerators (GPU, TPU, etc) is in our TODO list though.
While inference would be simpler to do, leveraging GPU/TPUs for training would be much harder. Notice DF algorithms doesn't do many floating point operations (other than calculating the scores at each level of the tree). Inference could be accelerated more easily though -- we did a draft in the past.
Maybe some interested developer would contribute it ?
from decision-forests.
Hi @janpfeifer Thank you for the quick response. No worries that it is not in your high priority list, I was just curious. Do you by any chance have a link to the draft you previously did for inference?
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Related Issues (20)
- max_vocab_count won't work for CATEGORICAL integerized in tfdf.keras.GradientBoostedTreesModel HOT 5
- Save and load model with tunning in automatic_tuning_colab.ipynb HOT 4
- Symbol not found, but versions are compatible according to the website HOT 4
- Loading a model returns either an untrained model or broken model HOT 1
- Using call_get_leaves inside @tf.function call in ensemble model inherits from tensorflow.keras.Model HOT 10
- no wheels for apple silicon (macos-arm64) HOT 2
- ANE support through coremltools HOT 4
- Can't use both `sample_weight` and `class_weight` at the same time HOT 1
- Is there a method like ydf.load_model() to load model get a instance of tfdf.keras.RandomForestModel? HOT 2
- decision forests tutorial tf_df_in_tf_js code wasn't working for me
- gpu support for layer use HOT 1
- DistributedGradientBoostedTreesModel does not support Ranking task HOT 1
- TF-DF Compatibility with Keras 3? HOT 6
- make_inspector() throws object of type 'NoneType' has no len() when I retrieve TF DF RF model layer in the hybrid model HOT 3
- tfdf 1.9.0 only compatible with tf 2.16.1 which ships Keras 3 HOT 8
- tensorflow-decision-forests 1.5.0 requires tensorflow~=2.13.0, but you have tensorflow 2.16.1 which is incompatible.
- Decision forest documentation link is broken in the Main page HOT 2
- WARNING:root:Failure to load the inference.so custom c++ tensorflow ops HOT 8
- OOM errors for large datasets
- Error while convering tensor flow decision forests td model into TFlite model HOT 1
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