# - If `JOB ID`is in Q state, it is in the queue waiting for available resources.
# - If `JOB ID` is in R state, it is running.
# In[4]:
import liveQStat
liveQStat.liveQStat()
# #### Get Results
#
# Run the next cell to retrieve your job's results.
# In[5]:
import get_results
get_results.getResults(cpu_job_id[0], filename='output.tgz', blocking=True)
# #### Unpack your output files and view stdout.log
# In[6]:
get_ipython().system('tar zxf output.tgz')
# In[7]:
get_ipython().system('cat stdout.log')
# #### View stderr.log
# This can be used for debugging
# In[8]:
#
# 1. `job id` - This value is stored in the `job_id_core` variable we created during **Step 3**. Remember that this value is an array with a single string, so we access the string value using `job_id_core[0]`.
# 2. `filename` - This value should match the filename of the compressed file we have in our `load_multi_model_job.sh` shell script. In this example, filename shoud be set to `output.tgz`.
# 3. `blocking` - This is an optional argument and is set to `False` by default. If this is set to `True`, the cell is locked while waiting for the results to come back. There is a status indicator showing the cell is waiting on results.
#
# **Note**: The `getResults` function is unique to Udacity's workspace integration with Intel's DevCloud. When working on Intel's DevCloud environment, your job's results are automatically retrieved and placed in your working directory.
#
# Click the **Retrieving Output Files** button below for a demonstration.
# <span class="graffiti-highlight graffiti-id_v3k1sjd-id_emzwj1d"><i></i><button>Retrieving Output Files</button></span>
# In[7]:
import get_results
get_results.getResults(job_id_core[0], filename="output.tgz", blocking=True)
# ## Step 6: Viewing the Outputs
# In this step, we unpack the compressed file using `!tar zxf` and read the contents of the log files by using the `!cat` command.
#
# `stdout.log` should contain the printout of the print statement in our Python script.
# In[8]:
get_ipython().system('tar zxf output.tgz')
# In[9]:
get_ipython().system('cat stdout.log')
# In[10]:
# **Note**: The `getResults` function is unique to Udacity's workspace integration with Intel's DevCloud. When working on Intel's DevCloud environment, your job's results are automatically retrieved and placed in your working directory.
#
# Click the **Retrieving Output Files** button below for a demonstration.
# <span class="graffiti-highlight graffiti-id_6zhr5sh-id_yspqiev"><i></i><button>Retrieving Output Files</button></span>
# ### Step 5a: Get GPU Results
#
# **Without batches**
# In[21]:
import get_results
get_results.getResults(gpu_job_id_core[0],
filename="output.tgz",
blocking=True)
# In[22]:
get_ipython().system('tar zxf output.tgz')
# In[23]:
get_ipython().system('cat stdout.log')
# In[24]:
get_ipython().system('cat stderr.log')
# **With Batches**