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Google Cloud Run now supports GPU workloads for ML pipelines

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(@jerry.green681)
New Member
Joined: 1 year ago
Posts: 1
Topic starter  

Breaking: Google Cloud Run now supports GPU workloads for ML pipelines

This is huge for the DevOps community. I've been following this development for weeks and it's finally here.

Impact on our workflows:
✓ Reduced costs
✓ Simplified configuration
✗ Initial bugs expected

What's your take on this?



   
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(@christopher.mitchell35)
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Joined: 4 months ago
Posts: 0
 

Been using this for 6 months. Here's what I learned...



   
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(@angela.nguyen556)
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Joined: 11 months ago
Posts: 0
 

Resource consumption is a concern. What's your experience? Trying to build a business case for management.



   
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(@scott.allen968)
New Member
Joined: 9 months ago
Posts: 0
 

Exactly! This is what we implemented last month.



   
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(@frank.reyes19)
New Member
Joined: 2 months ago
Posts: 0
 

For those asking about cost: in our case (AWS, us-east-1, ~500 req/sec), we're paying about $500/month. That's 50% vs our old setup with Grafana. ROI was positive after just 2 months when you factor in engineering time saved.



   
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(@james.bennett725)
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Joined: 1 year ago
Posts: 0
 

Security team blocked this due to compliance requirements.



   
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(@linda.foster79)
New Member
Joined: 6 months ago
Posts: 0
 

What about monitoring? How do you track metrics? Our team is particularly concerned about production stability.



   
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(@jerry.green681)
New Member
Joined: 1 year ago
Posts: 1
Topic starter  

Did you use version X or Y? We found Y more stable. Looking for real-world benchmarks if anyone has them.



   
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(@dennis.king704)
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Joined: 5 months ago
Posts: 0
 

Great for small teams, but doesn't scale well past 50 people.



   
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(@david_jenkins)
New Member
Joined: 7 months ago
Posts: 0
 

We evaluated this last year. The main challenge was...



   
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(@laura.rivera601)
New Member
Joined: 2 months ago
Posts: 0
 

How did you handle the migration? Any gotchas to watch for? Trying to build a business case for management.



   
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(@patricia.morgan347)
New Member
Joined: 3 months ago
Posts: 0
 

In our production environment with 200+ microservices, we found that Docker significantly outperformed Grafana. The key was proper configuration of memory limits. Deployment time dropped from 45min to 8min. Highly recommended for teams running Kubernetes at scale.



   
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(@evelyn.sanders800)
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Joined: 2 months ago
Posts: 0
 

What about monitoring? How do you track metrics? Trying to build a business case for management.



   
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(@robert.stewart107)
New Member
Joined: 2 months ago
Posts: 0
 

We benchmarked 5 solutions:
1. Option A: fast but expensive
2. Option B: cheap but limited
3. Option C: goldilocks zone ✓
Ended up with C, saved 40% vs A.



   
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(@kimberly.james491)
New Member
Joined: 7 months ago
Posts: 0
 

In our production environment with 200+ microservices, we found that GitLab CI significantly outperformed GitHub Actions. The key was proper configuration of scaling parameters. Deployment time dropped from 45min to 8min. Highly recommended for teams running Kubernetes at scale.



   
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