Karm Patel
Karm Patel
> @karm-patel I think you have already solved this? Where is the code? Yes, I have an un-polished code, I'll check in soon.
Okay, I'll take this
## Fixed Notebooks | Notebook | fig_no | Error | fix | | -- | -- | -- | -- | |[multi_collinear_legs_numpyro.ipynb](https://github.com/probml/pyprobml/tree/master/notebooks/book1/11/multi_collinear_legs_numpyro.ipynb) | 11.23, 11.24 | TypeError: TruncatedNormal() takes from...
1. Kernal not responding (5 notebooks) [book1/01/mnist_viz_tf](https://github.com/probml/pyprobml/tree/master/notebooks/book1/01/mnist_viz_tf.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/01/mnist_viz_tf.log) [book1/02/robust_pdf_plot](https://github.com/probml/pyprobml/tree/master/notebooks/book1/02/robust_pdf_plot.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/02/robust_pdf_plot.log) [book1/04/postDensityIntervals](https://github.com/probml/pyprobml/tree/master/notebooks/book1/04/postDensityIntervals.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/04/postDensityIntervals.log) [book1/04/biasVarModelComplexity3](https://github.com/probml/pyprobml/tree/master/notebooks/book1/04/biasVarModelComplexity3.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/04/biasVarModelComplexity3.log) [book1/07/gaussEvec](https://github.com/probml/pyprobml/tree/master/notebooks/book1/07/gaussEvec.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/07/gaussEvec.log) 2. figures not found in `savefig("figures/...")` (8 notebooks) [book1/16/parzen_window_demo2](https://github.com/probml/pyprobml/tree/master/notebooks/book1/16/parzen_window_demo2.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/16/parzen_window_demo2.log) [book1/17/svm_classifier_feature_scaling](https://github.com/probml/pyprobml/tree/master/notebooks/book1/17/svm_classifier_feature_scaling.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/17/svm_classifier_feature_scaling.log) [book1/18/rf_demo_2d](https://github.com/probml/pyprobml/tree/master/notebooks/book1/18/rf_demo_2d.ipynb) [log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book1/18/rf_demo_2d.log)...
Hi @nalzok, 1. For `cell execution timeout` I think you can increase the time out (maybe up to 1200 s?), because I think there is no harm if our workflow...
Yes, I agree, @nalzok I think we can put these notebooks in [IGNORE_LIST](https://github.com/probml/pyprobml/blob/master/tests/test_notebooks.py#:~:text=IGNORE_LIST%20%3D%20%5B%5D,strip().split(%22/%22)%5B%2D1%5D). I've created [copied_from_misc.txt](https://github.com/probml/pyprobml/blob/master/internal/copied_from%20misc_nb.txt) which contains a list of some notebooks which are being ignored by `test_notebooks.py` because...
Some non-trivial notebooks execution errors in book2 1. [smc_tempered_1d_bimodal.log](https://github.com/probml/pyprobml/tree/workflow_testing_indicator/notebooks/book2/12/smc_tempered_1d_bimodal.log), [linreg_hierarchical_non_centered_blackjax.log](https://github.com/probml/pyprobml/blob/workflow_testing_indicator/notebooks/book2/14/linreg_hierarchical_non_centered_blackjax.log) - These notebooks are implemented in old blackjax version, so need to refactor it. 2. [variational_mixture_gaussians_demo.log](https://github.com/probml/pyprobml/blob/workflow_testing_indicator/notebooks/book2/09/variational_mixture_gaussians_demo.log), [thompson_sampling_linear_gaussian.log](https://github.com/probml/pyprobml/blob/workflow_testing_indicator/notebooks/book2/33/thompson_sampling_linear_gaussian.log): from jax.ops import...
Hi, @rlouf, Could you suggest where should I add `create_trace_from_states()` function? I was looking for some `utils.py`, so anyone can import this function directly from `blackjax`.
>before pushing this on arviz, to have a standard way to provide samples in `blackjax`, i.e. simple sampling loops. We are open to having these implemented in `blackjax` directly. Hey...
By `simple sampling loops` are you suggesting to add method like `inference_loop()` in blackjax? ```py def inference_loop(rng_key, kernel, initial_state, num_samples): @jax.jit def one_step(state, rng_key): state, _ = kernel(rng_key, state) return...