Map single-cell transcriptomes to copy number evolutionary trees. Check out the tutorial for more information.
$ pip install scatrex
SCATrEx uses JAX to perform automatic differentiation. By default, SCATrEx installs the CPU-only version of JAX, but we strongly recommend the use of GPU acceleration. Please follow the instructions in https://github.com/google/jax#pip-installation-gpu-cuda to install the GPU version of JAX.
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Running SCATrEx twice with the same random seed yields different results. This does not happen when we do not do any ELBO optimization, and just change the parameters in the move proposals and do ELBO evaluations with those. It is irrespective of local in the run_search being True or False. For some reason, some moves lead to ELBO optimization steps with small divergences between runs, and this accumulates leading to different trees in the end.
Commenting out the ancestor nodes' contribution from the node weights seems to fix it. So it might be a numerical instability problem that XLA does not deal with deterministically?
We get memory overflow when we run too many SCATrEx tree inferences in the same Python session. This only happens when using a GPU, and it occurs on the call to do_grad, which is a jitted function.
Full error trace:
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RuntimeError Traceback (most recent call last)
<ipython-input-216-9b90a41ab615> in <module>
47 'random_seed': 1,
48 'verbosity': logging.DEBUG}
---> 49 sca.learn_tree(reset=True, batch_key='batch', search_kwargs=search_kwargs)
/cluster/work/bewi/members/pedrof/miniconda3/envs/py38/lib/python3.8/site-packages/scatrex/scatrex.py in learn_tree(self, observed_tree, reset, cell_filter, filter_genes, max_genes, batch_key, search_kwargs)
289 logger.info("Will continue search from where it left off.")
290
--> 291 self.ntssb = self.search.run_search(**search_kwargs)
292 self.ntssb.create_augmented_tree_dict()
293
/cluster/work/bewi/members/pedrof/miniconda3/envs/py38/lib/python3.8/site-packages/scatrex/ntssb/search.py in run_search(self, n_iters, n_iters_elbo, factor_delay, posterior_delay, global_delay, joint_init, thin, local, num_samples, step_size, verbosity, tol, mb_size, max_nodes, debug, callback, alpha, Tmax, anneal, restart_step, move_weights, weighted, merge_n_tries, opt, search_callback, add_rule, add_rule_thres, random_seed, **callback_kwargs)
290 "log_baseline_mean"
291 ] = init_log_baseline
--> 292 self.tree.optimize_elbo(
293 root_node=None,
294 sticks_only=True,
/cluster/work/bewi/members/pedrof/miniconda3/envs/py38/lib/python3.8/site-packages/scatrex/ntssb/ntssb.py in optimize_elbo(self, root_node, local_node, global_only, sticks_only, unique_node, num_samples, n_iters, thin, step_size, debug, tol, run, max_nodes, init, opt, opt_triplet, mb_size, callback, **callback_kwargs)
1543 # data_mask_subset = data_mask
1544 # start = time.time()
-> 1545 opt_state, g, params, elbo = self.update(
1546 obs_params,
1547 parent_vector,
/cluster/work/bewi/members/pedrof/miniconda3/envs/py38/lib/python3.8/site-packages/scatrex/ntssb/ntssb.py in update(self, obs_params, parent_vector, children_vector, ancestor_nodes_indices, tssb_indices, previous_branches_indices, tssb_weights, dp_alphas, dp_gammas, node_mask, data_mask_subset, indices, do_global, global_only, sticks_only, num_samples, i, opt_state, opt_update, get_params)
1259 # print("Recompiling update!")
1260 params = get_params(opt_state)
-> 1261 value, gradient = self.do_grad(
1262 obs_params,
1263 parent_vector,
[... skipping hidden 8 frame]
RuntimeError: INTERNAL: Failed to load in-memory CUBIN: CUDA_ERROR_OUT_OF_MEMORY: out of memory
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