# - 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**