recover values from ipyparallel map if a single evaluation failed
<p>I run a quite time consuming (>3 days) simulation using ipyparallel's map function like that in Jupyter Notebook</p>

<pre><code>from ipyparallel import Client
rc = Client()
lview = rc.load_balanced_view()
ar = lview.map(runsimulation, parameter)
</code></pre>

<p>and I really need those results. But unfortunately a single instance died because of memory starvation. Now i can not access the result using ar[i] anymore. Is there a chance to recover all the other results that where most certainly computed (I can see the characteristic memory and cpu time consumption in the Ganglia Monitor of the cluster).</p>

<p>I still have the object ar in the Jupyter Notebook.</p>

<p>You can get partial results for individual msg_ids. With map, each message corresponds to a chunk of the map (a list):</p>

<pre><code>import ipyparallel as ipp

results = []
for msg_id in ar.msg_ids:
    chunk_ar = rc.get_result(msg_id)
    try:
        chunk = chunk_ar.get()
    except ipp.RemoteError as e:
        print('ignoring error: %s' % e)
    results.extend(chunk)
</code></pre>

python jupyter ipython-parallel