Comments (4)
sharvil
commented on June 6, 2024
2
Yes! See https://colab.research.google.com/drive/1hzYhcyvbXYMAUwa3515BszSkhx1UUFSt for an example of using Haste with TensorFlow on Colab.
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sharvil
commented on June 6, 2024
It's possible but quite a bit of work for each release. Is there a particular issue you're running into when building the code? I may be able to help you get past that.
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flavioschneider
commented on June 6, 2024
Is it possible to install haste in google colab?
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PetrochukM
commented on June 6, 2024
The specific issue was a workflow issue.
Our team needed a reproducible approach to installing this repository on both our Macbooks and the cloud machines. We handle all of our dependency management with pip.
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Related Issues (20)
- Install on pip on systems without cuda HOT 7
- Segmentation fault on Cuda 10.0 HOT 2
- Support zoneout on lstm cell state and add recurrent dropout HOT 2
- CUDA error: an illegal memory access was encountered HOT 6
- haste_pytorch: Gradient for kernel/recurrent_kernel becomes zero when trained on gpu HOT 4
- How to expose LayerNormGRUCell to python ? HOT 2
- Can't run haste layers in Keras HOT 12
- Biases in final IndRNN layer are 0 HOT 1
- Zoneout remains during eval() HOT 2
- return_state_sequence for tf version
- layer_norm_gru_cell HOT 1
- Can Bidirectional Rnn and multi-layer Rnn be supported? HOT 1
- Activation function in IndRNN HOT 1
- haste_pytorch does not install properly with conda cudatoolkit? HOT 3
- Feature request for cell classes for pytorch HOT 7
- `RNN`s with `zoneout > 0.0` have wrong gradients HOT 1
- haste_tf compilation fails with "‘bfloat16’ in namespace ‘Eigen’ does not name a type"
- Support for PyTorch packed sequences HOT 2
- Supporting RWKV (a RNN that can match transformer LM & zero-shot performance at 1B+ params)
- Nan loss when replace pytorch LSTM with your LSTM or LayerNormLSTM HOT 2
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