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Google Colab Pro+ and its Alternatives: A Comparative Analysis (2023)
In the world of data science and machine learning, cloud-based notebook environments are indispensable tools. Google Colab Pro+ is one such environment that provides users with an advanced platform for their Python coding needs. But is it the best? Let's examine how Google Colab Pro+ stacks up against various alternatives using three criteria: quality of cloud-hosted and managed Python notebook environment, ability to use faster GPUs with more memory, and ability to run longer background executions.
Google Colab Pro+
Cloud-hosted and managed Python notebook environment:
Starting with Google Colab Pro+, the environment is a cloud-hosted & managed Python notebook. It's easy to use, requires no setup, and provides customization options. Thus, it scores 7/10.
Faster GPUs with more memory:
It offers more powerful GPUs and more memory compared to the free version of Colab, allowing you to tackle larger datasets and train more complex models. The exact resources can fluctuate due to demand, however, which slightly detracts from the offering. Colab may offer A100s, V100s, and T4s. We give it an 8/10 in this category.
Longer-running background executions:
Colab Pro+ allows for extended runtimes, up to 24 hours, which is great for long-running processes, but doesn't quite match a dedicated environment. Since the environments are ephemeral and do not support persistent storage, you must use external services like Google Drive. Therefore, it receives 8/10.
X-Factor:
For X-factor features, Colab Pro+ shines with collaboration features, tight integration with Google Drive, and easy access to TensorBoard. It also has some integration with git for version control and dashboards to monitor your resource utilization. Colab also has a feature to run locally or on Google cloud. It scores 6/10.
Total: 29/40
Kaggle Kernels
Cloud-hosted and managed Python notebook environment:
This Google Colab alternative provides a cloud-hosted and managed Python environment. However, customization is somewhat limited compared to Colab. We give it a 7/10.
Faster GPUs with more memory:
Kaggle offers GPUs, but only a small selection such as T4s, P100s, and TPUs. For this, it gets a score of 6/10.
Longer-running background executions:
Long-running executions are not the strong suit of Kaggle Kernels due the to 9-hour runtime limit, however, it does feature scheduled runs. Kaggle Kernels do not have persistent storage built into them natively. It scores 6/10.
X-Factor:
As for X-factor features, Kaggle Kernels has strong data exploration tools and a rich set of public datasets, making it easy to start any data science project. Kaggle has the ability to connect to google cloud. It scores 6/10.
Total: 25/40
Deepnote
Cloud-hosted and managed Python notebook environment:
Deepnote provides a cloud-hosted and managed Python environment with an intuitive UI and a high degree of customization, giving it an 8/10.
Faster GPUs with more memory:
Deepnote offers machine configurations with GPUs but falls short of Colab Pro+ and Kaggle Kernels in terms of the variety of resources as they only support K80s. Hence, it gets a score of 4/10.
Longer-running background executions:
Deepnote shines in allowing long-running executions, with practically unlimited runtimes and features like scheduled runs, and notifications. It scores a perfect 10/10.
X-Factor:
As for X-factor features, Deepnote stands out with its real-time collaborative notebook platform and easy integration with various data sources like BigQuery. It also supports other languages like SQL. Deepnote does not allow you to run on your own cloud. It scores 9/10.
Total: 31/40
Gradient by Paperspace
Cloud-hosted and managed Python notebook environment:
Gradient by Paperspace provides a well-managed Python environment, although the setup process might be a bit complex for beginners. You can provide your own container image as well. It gets a 7/10.
Faster GPUs with more memory:
For resource availability, it offers a wide range of powerful machines, including options with multiple GPUs and 80GB A100s. It scores a 9/10.
Longer-running background executions:
The platform offers long-running executions, albeit with some restrictions in the free tier. Thus, it scores 8/10.
X-Factor:
In terms of X-factor features, Gradient stands out with its job runner, which allows for running scripts without a notebook interface, and a robust CLI. It scores 7/10.
Total: 33/40
Jupyter on a Cloud VM
Cloud-hosted and managed Python notebook environment:
Running a Jupyter Notebook on a cloud VM (like AWS EC2, Google Compute Engine, or Azure VM) provides a customizable Python notebook environment, but setup can be quite complex, especially for beginners. We give it a 6/10.
Faster GPUs with more memory:
For resource availability, Cloud VMs shine by providing a broad range of machine types with varying GPU, CPU, and memory configurations. It scores a perfect 10/10.
Longer-running background executions:
Cloud VMs allow for long-running executions without any fear of losing your work, but you have to manage your instances to avoid unnecessary costs. We give it a 9/10.
X-Factor:
In terms of X-factor features, cloud VMs are flexible and allow full control but lack some of the integrated data science-specific out-of-the-box. They allow you to run most kinds of workloads. 5/10
30/40
Jupyter on Brev
Cloud-hosted and managed Python notebook environment:
Brev instances come with common ML/AI tools and GPU drivers configured correctly so you can immediately run workloads. Brev provides ways to fully configure the VM and share similar configurations with your team. It has Jupyter Notebook hosted as a default and allows for other applications to be hosted as well. We give it an 8/10
Faster GPUs with more memory:
Brev sits on top of public clouds and allows you to connect your own accounts providing the most computing options from T4s to multi-GPU A100s. It scores a perfect 10/10.
Longer-running background executions:
Brev machines have persistent storage and come with a CLI tool to run background jobs easily allowing you to fearlessly run your jobs and ML tasks. 9/10
X-Factor:
Brev allows for the most control of your infrastructure while still being easy to use meaning you can use your cloud credits on AWS and GCP. The instances support changing compute types without having to re-setup and re-initialize the environment thanks to its built-in persistent storage. Additionally, Brev easily integrates with IDEs like VScode and Pycharm(SSH) allowing you to carry over into application development and Brev instances can be used to serve these applications as well. 8/10
Total Score: 35/40
Conclusion
In conclusion, this comparison of Google Colab alternatives covers some of the more user-friendly, readily accessible environments for running Python notebooks in the cloud. It's important to note that it is by no means exhaustive. The landscape of cloud-based data science tools is vast, and there are many other products and services out there that cater to different needs and use cases. For instance, enterprise solutions like Databricks or Domino Data Lab offer powerful features for large teams and complex projects, but they come with heavy onboarding processes and often require a substantial financial commitment. Similarly, cloud-based solutions like AWS SageMaker Google Vertex AI, or Azure Machine Learning provide deep integrations with their respective ecosystems, but they necessitate familiarity with public cloud infrastructure and can entail additional setup and management. Ultimately, the best tool depends on the specific requirements of your project, your budget, and your technical expertise.