From 33dd8ee85e1bed4b70285e095deb7aa3602174a4 Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Mon, 9 Jan 2017 19:32:25 -0500
Subject: [PATCH] fixes for kubeface-based model training
---
.../models_class1_allele_specific_single/README.md | 11 +++++++----
.../models_class1_allele_specific_single/models.py | 5 ++---
2 files changed, 9 insertions(+), 7 deletions(-)
diff --git a/downloads-generation/models_class1_allele_specific_single/README.md b/downloads-generation/models_class1_allele_specific_single/README.md
index 58b6880f..12046a00 100644
--- a/downloads-generation/models_class1_allele_specific_single/README.md
+++ b/downloads-generation/models_class1_allele_specific_single/README.md
@@ -11,10 +11,13 @@ To generate this download we run:
```
# If you are running dask distributed using our kubernetes config, you can use the DASK_IP one liner below.
# Otherwise, just set it to the IP of the dask scheduler.
-DASK_IP=$(kubectl get service | grep daskd-scheduler | tr -s ' ' | cut -d ' ' -f 3)
./GENERATE.sh \
- --joblib-num-jobs 100 \
- --joblib-pre-dispatch all \
--cv-folds-per-task 10 \
- --dask-scheduler $DASK_IP:8786
+ --backend kubernetes \
+ --storage-prefix gs://kubeface \
+ --worker-image hammerlab/mhcflurry:latest \
+ --kubernetes-task-resources-memory-mb 10000 \
+ --worker-path-prefix venv-py3/bin \
+ --max-simultaneous-tasks 200 \
+
```
diff --git a/downloads-generation/models_class1_allele_specific_single/models.py b/downloads-generation/models_class1_allele_specific_single/models.py
index 6375cd45..30f8e3d5 100644
--- a/downloads-generation/models_class1_allele_specific_single/models.py
+++ b/downloads-generation/models_class1_allele_specific_single/models.py
@@ -3,13 +3,12 @@ from mhcflurry.class1_allele_specific.train import HYPERPARAMETER_DEFAULTS
import json
models = HYPERPARAMETER_DEFAULTS.models_grid(
- #impute=[False, True],
- impute=[False],
+ impute=[False, True],
activation=["tanh"],
layer_sizes=[[12], [64], [128]],
embedding_output_dim=[8, 32, 64],
dropout_probability=[0, .1, .25],
- # fraction_negative=[0, .1, .2],
+ fraction_negative=[0, .1, .2],
n_training_epochs=[250])
sys.stderr.write("Models: %d\n" % len(models))
--
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