![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234817%2Fb1ea9256e37ac3f12070330a12a7c94e%2FScreenshot_20191024-093659-01.jpeg?generation=1571890158647945&alt=media\")
Gradient Community Notebooks offers public & shareable Jupyter Notebooks that run on free cloud GPUs
\n\nNEW YORK, Oct. 10, 2019 /PRNewswire/ -- Paperspace announced today \"Gradient Community Notebooks\", a free cloud GPU service based on Jupyter notebooks designed for machine learning and deep learning development. Now, any developer working with popular deep learning frameworks such as PyTorch, TensorFlow, Keras, and OpenCV, can launch and collaborate on their ML projects. The new solution provides easy-to-use templates pre-loaded with all of the various different libraries, dependencies, and drivers. Similar to a GitHub repo, Gradient Community Notebooks can easily be shared and forked into a user's own account.
\n\n\"GPUs are essential to ML development, yet the services available today are complex and prohibitively expensive for many developers,\" said Dillon Erb, CEO & Co-founder, Paperspace. \"This is precisely why we created Gradient Community: to make GPU and ML development resources widely accessible and easy to deploy. Our focus on empowering developers with cutting-edge technology, and a means to collaborate, supports our mission to help every developer become an AI developer.\"
\n\nFree GPUs (and CPU) backed notebooks;
New Jupyter-based collaborative environment ;
Access to popular libraries, prebuilt models, and a project showcase;
Users can create a public profile page to share their bio and the work they're interested in.
With Gradient Community Notebooks, users can get started from scratch with a new notebook or leverage pre-configured projects from the Gradient ML-Showcase, a curated list of machine learning examples.
\n\nFor more information or to sign up for Gradient Community Notebooks, please visit https://gradient.paperspace.com/free-gpu
\n\nAbout Paperspace\nPaperspace is a high-performance cloud computing and deep learning development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use Paperspace to iterate faster and collaborate on intelligent, real-time prediction engines.
\n\nPaperspace is backed by leading investors including Battery Ventures, Intel Capital, SineWave Ventures, Y Combinator and Initialized Capital.
\n\nTo learn more about Gradient, please visit https://gradient.paperspace.com/ or follow us on Twitter at: @hellopaperspace.
\n\n", "messages": [ { "id": 646735, "postDate": "2019-10-11T16:37:47.583Z", "content": "Gradient Community Notebooks offers public & shareable Jupyter Notebooks that run on free cloud GPUs
\n\nNEW YORK, Oct. 10, 2019 /PRNewswire/ -- Paperspace announced today \"Gradient Community Notebooks\", a free cloud GPU service based on Jupyter notebooks designed for machine learning and deep learning development. Now, any developer working with popular deep learning frameworks such as PyTorch, TensorFlow, Keras, and OpenCV, can launch and collaborate on their ML projects. The new solution provides easy-to-use templates pre-loaded with all of the various different libraries, dependencies, and drivers. Similar to a GitHub repo, Gradient Community Notebooks can easily be shared and forked into a user's own account.
\n\n\"GPUs are essential to ML development, yet the services available today are complex and prohibitively expensive for many developers,\" said Dillon Erb, CEO & Co-founder, Paperspace. \"This is precisely why we created Gradient Community: to make GPU and ML development resources widely accessible and easy to deploy. Our focus on empowering developers with cutting-edge technology, and a means to collaborate, supports our mission to help every developer become an AI developer.\"
\n\nFree GPUs (and CPU) backed notebooks;
New Jupyter-based collaborative environment ;
Access to popular libraries, prebuilt models, and a project showcase;
Users can create a public profile page to share their bio and the work they're interested in.
With Gradient Community Notebooks, users can get started from scratch with a new notebook or leverage pre-configured projects from the Gradient ML-Showcase, a curated list of machine learning examples.
\n\nFor more information or to sign up for Gradient Community Notebooks, please visit https://gradient.paperspace.com/free-gpu
\n\nAbout Paperspace\nPaperspace is a high-performance cloud computing and deep learning development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use Paperspace to iterate faster and collaborate on intelligent, real-time prediction engines.
\n\nPaperspace is backed by leading investors including Battery Ventures, Intel Capital, SineWave Ventures, Y Combinator and Initialized Capital.
\n\nTo learn more about Gradient, please visit https://gradient.paperspace.com/ or follow us on Twitter at: @hellopaperspace.
\n\n", "rawMarkdown": "Gradient Community Notebooks offers public & shareable Jupyter Notebooks that run on free cloud GPUs\n\nNEW YORK, Oct. 10, 2019 /PRNewswire/ -- Paperspace announced today \"Gradient Community Notebooks\", a free cloud GPU service based on Jupyter notebooks designed for machine learning and deep learning development. Now, any developer working with popular deep learning frameworks such as PyTorch, TensorFlow, Keras, and OpenCV, can launch and collaborate on their ML projects. The new solution provides easy-to-use templates pre-loaded with all of the various different libraries, dependencies, and drivers. Similar to a GitHub repo, Gradient Community Notebooks can easily be shared and forked into a user's own account.\n\n\"GPUs are essential to ML development, yet the services available today are complex and prohibitively expensive for many developers,\" said Dillon Erb, CEO & Co-founder, Paperspace. \"This is precisely why we created Gradient Community: to make GPU and ML development resources widely accessible and easy to deploy. Our focus on empowering developers with cutting-edge technology, and a means to collaborate, supports our mission to help every developer become an AI developer.\"\n\n\n#Benefits and features of Gradient Community Notebooks include:\n\n- Free GPUs (and CPU) backed notebooks;\n\n- New Jupyter-based collaborative environment ;\n\n- Access to popular libraries, prebuilt models, and a project showcase;\n\n- Users can create a public profile page to share their bio and the work they're interested in.\n\nWith Gradient Community Notebooks, users can get started from scratch with a new notebook or leverage pre-configured projects from the Gradient ML-Showcase, a curated list of machine learning examples.\n\nFor more information or to sign up for Gradient Community Notebooks, please visit https://gradient.paperspace.com/free-gpu\n\nAbout Paperspace\nPaperspace is a high-performance cloud computing and deep learning development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use Paperspace to iterate faster and collaborate on intelligent, real-time prediction engines.\n\nPaperspace is backed by leading investors including Battery Ventures, Intel Capital, SineWave Ventures, Y Combinator and Initialized Capital.\n\nTo learn more about Gradient, please visit https://gradient.paperspace.com/ or follow us on Twitter at: @hellopaperspace.\n\nsource link : https://finance.yahoo.com/amphtml/news/paperspace-introduces-free-gpu-cloud-130000464.html?fbclid=IwAR1fr9rs5K0b2gTJleQiHhAYhGN-i9_paPkr3xX--n3u5D4pTQo5tEXLW_Y", "votes": 18 }, { "id": 646804, "postDate": "2019-10-11T18:14:56.050Z", "content": "This is great news ! I am exploring it now . Thanks for information.
", "rawMarkdown": "This is great news ! I am exploring it now . Thanks for information.", "votes": 3, "replies": [ { "id": 646840, "postDate": "2019-10-11T19:13:28.290Z", "content": "same here mate,but i am unable to install opencv and albumentations there, i get : ImportError: libXrender.so.1: cannot open shared object file: No such file or directory
\n\nand i don't know how to install libXrender there,,if you can solve this error while using paperspace then please let me know,thanks in advance
", "rawMarkdown": "same here mate,but i am unable to install opencv and albumentations there, i get : ImportError: libXrender.so.1: cannot open shared object file: No such file or directory\n\n\nand i don't know how to install libXrender there,,if you can solve this error while using paperspace then please let me know,thanks in advance", "votes": 3 }, { "id": 656255, "postDate": "2019-10-24T04:12:16.277Z", "content": "Hey , I used the below type of VM . Was able to setup Understanding Cloud Dataset and performance seemed to be better than Kaggle Kernel . An epoch in Kaggle for my architecture takes 4+ minutes and in this it took a little over 2.5 mins .\nI was able to install Seaborn, Tqdm , and Albumentations . You might need to consider the version of library for Tqdm . tqdm.auto is not present in all version .
\n\n
Understood mate..thanks for letting me know :)
", "rawMarkdown": "Understood mate..thanks for letting me know :)" } ] }, { "id": 646778, "postDate": "2019-10-11T17:33:35.313Z", "content": "Hmm it is best to get gpu for more runs. Thanks a lot for sharing this
", "rawMarkdown": "Hmm it is best to get gpu for more runs. Thanks a lot for sharing this ", "votes": 3, "replies": [ { "id": 646838, "postDate": "2019-10-11T19:11:55.720Z", "content": "you are welcome mate
", "rawMarkdown": "you are welcome mate", "votes": 3 } ] }, { "id": 649686, "postDate": "2019-10-15T16:57:22.887Z", "content": "Thanks for sharing, this will be indeed useful for many.
", "rawMarkdown": "Thanks for sharing, this will be indeed useful for many.", "votes": 4, "replies": [ { "id": 649759, "postDate": "2019-10-15T18:21:59.617Z", "content": "glad it helps @psvishnu
", "rawMarkdown": "glad it helps @psvishnu ", "votes": 2 } ] }, { "id": 649767, "postDate": "2019-10-15T18:28:47.907Z", "content": "Wonderful kernel. Thanks for sharing!! Really helpful. @mobassir
", "rawMarkdown": "Wonderful kernel. Thanks for sharing!! Really helpful. @mobassir ", "votes": 2, "replies": [ { "id": 649776, "postDate": "2019-10-15T18:31:51.213Z", "content": "thank you mate @evgenyshtepin
", "rawMarkdown": "thank you mate @evgenyshtepin ", "votes": 1 } ] }, { "id": 649699, "postDate": "2019-10-15T17:14:54.437Z", "content": "Thanks for sharing.
\n\nCan you also share your experience about training models on Gradient Community Notebooks?
", "rawMarkdown": "Thanks for sharing. \n\nCan you also share your experience about training models on Gradient Community Notebooks?", "votes": 2, "replies": [ { "id": 649768, "postDate": "2019-10-15T18:28:54.927Z", "content": "thanks,good question @atikur \nit offers 10 notebooks per month\ni just downloaded the dataset there from kaggle using kaggleApi\nthen made baseline model but didn't ran for even a single epoch because i became busy working on steel defect detection competition using kaggle kernels\ni will go back to paperspace again when my gpu quota is over and will let you know about performance of gradient community\nbut personally i loved their notebooks\nit's just like jupyter notebook installed in our computer\njust curiously waiting to see if it is slow like colab while training or not
", "rawMarkdown": "thanks,good question @atikur \nit offers 10 notebooks per month\ni just downloaded the dataset there from kaggle using kaggleApi\nthen made baseline model but didn't ran for even a single epoch because i became busy working on steel defect detection competition using kaggle kernels\ni will go back to paperspace again when my gpu quota is over and will let you know about performance of gradient community\nbut personally i loved their notebooks\nit's just like jupyter notebook installed in our computer\njust curiously waiting to see if it is slow like colab while training or not", "votes": 5 }, { "id": 649981, "postDate": "2019-10-16T01:21:54.640Z", "content": "Thanks for your reply.
", "rawMarkdown": "Thanks for your reply.", "votes": 2 } ] }, { "id": 649035, "postDate": "2019-10-14T22:02:14.363Z", "content": "great news and interesting information. thanks for sharing
", "rawMarkdown": "great news and interesting information. thanks for sharing", "votes": 2, "replies": [ { "id": 649252, "postDate": "2019-10-15T06:21:13.700Z", "content": "you are welcome
", "rawMarkdown": "you are welcome", "votes": 2 } ] }, { "id": 647155, "postDate": "2019-10-12T05:39:54.240Z", "content": "Very Informative Share... Thanks @mobassir
", "rawMarkdown": "Very Informative Share... Thanks @mobassir ", "votes": 2, "replies": [ { "id": 647212, "postDate": "2019-10-12T07:12:49.220Z", "content": "you are welcome @veeralakrishna
", "rawMarkdown": "you are welcome @veeralakrishna ", "votes": 3 } ] } ], "comments": [ { "id": 646804, "author_name": "Nirjhar Roy", "author_url": "", "post_date": "2019-10-11T18:14:56.050000", "content": "This is great news ! I am exploring it now . Thanks for information.
", "votes": 3, "replies": [ { "id": 646840, "author_name": "Mobassir", "author_url": "", "post_date": "2019-10-11T19:13:28.290000", "content": "same here mate,but i am unable to install opencv and albumentations there, i get : ImportError: libXrender.so.1: cannot open shared object file: No such file or directory
\n\nand i don't know how to install libXrender there,,if you can solve this error while using paperspace then please let me know,thanks in advance
", "votes": 3, "replies": [] }, { "id": 656255, "author_name": "Nirjhar Roy", "author_url": "", "post_date": "2019-10-24T04:12:16.277000", "content": "Hey , I used the below type of VM . Was able to setup Understanding Cloud Dataset and performance seemed to be better than Kaggle Kernel . An epoch in Kaggle for my architecture takes 4+ minutes and in this it took a little over 2.5 mins .\nI was able to install Seaborn, Tqdm , and Albumentations . You might need to consider the version of library for Tqdm . tqdm.auto is not present in all version .
\n\n
Understood mate..thanks for letting me know :)
", "votes": 0, "replies": [] } ] }, { "id": 646778, "author_name": "Raju Kumar Mishra", "author_url": "", "post_date": "2019-10-11T17:33:35.313000", "content": "Hmm it is best to get gpu for more runs. Thanks a lot for sharing this
", "votes": 3, "replies": [ { "id": 646838, "author_name": "Mobassir", "author_url": "", "post_date": "2019-10-11T19:11:55.720000", "content": "you are welcome mate
", "votes": 3, "replies": [] } ] }, { "id": 649686, "author_name": "psv", "author_url": "", "post_date": "2019-10-15T16:57:22.887000", "content": "Thanks for sharing, this will be indeed useful for many.
", "votes": 4, "replies": [ { "id": 649759, "author_name": "Mobassir", "author_url": "", "post_date": "2019-10-15T18:21:59.617000", "content": "glad it helps @psvishnu
", "votes": 2, "replies": [] } ] }, { "id": 649767, "author_name": "Evgeny Shtepin", "author_url": "", "post_date": "2019-10-15T18:28:47.907000", "content": "Wonderful kernel. Thanks for sharing!! Really helpful. @mobassir
", "votes": 2, "replies": [ { "id": 649776, "author_name": "Mobassir", "author_url": "", "post_date": "2019-10-15T18:31:51.213000", "content": "thank you mate @evgenyshtepin
", "votes": 1, "replies": [] } ] }, { "id": 649699, "author_name": "Atikur Rahman", "author_url": "", "post_date": "2019-10-15T17:14:54.437000", "content": "Thanks for sharing.
\n\nCan you also share your experience about training models on Gradient Community Notebooks?
", "votes": 2, "replies": [ { "id": 649768, "author_name": "Mobassir", "author_url": "", "post_date": "2019-10-15T18:28:54.927000", "content": "thanks,good question @atikur \nit offers 10 notebooks per month\ni just downloaded the dataset there from kaggle using kaggleApi\nthen made baseline model but didn't ran for even a single epoch because i became busy working on steel defect detection competition using kaggle kernels\ni will go back to paperspace again when my gpu quota is over and will let you know about performance of gradient community\nbut personally i loved their notebooks\nit's just like jupyter notebook installed in our computer\njust curiously waiting to see if it is slow like colab while training or not
", "votes": 5, "replies": [] }, { "id": 649981, "author_name": "Atikur Rahman", "author_url": "", "post_date": "2019-10-16T01:21:54.640000", "content": "Thanks for your reply.
", "votes": 2, "replies": [] } ] }, { "id": 649035, "author_name": "Adrian Zinovei", "author_url": "", "post_date": "2019-10-14T22:02:14.363000", "content": "great news and interesting information. thanks for sharing
", "votes": 2, "replies": [ { "id": 649252, "author_name": "Mobassir", "author_url": "", "post_date": "2019-10-15T06:21:13.700000", "content": "you are welcome
", "votes": 2, "replies": [] } ] }, { "id": 647155, "author_name": "Ailurophile", "author_url": "", "post_date": "2019-10-12T05:39:54.240000", "content": "Very Informative Share... Thanks @mobassir
", "votes": 2, "replies": [ { "id": 647212, "author_name": "Mobassir", "author_url": "", "post_date": "2019-10-12T07:12:49.220000", "content": "you are welcome @veeralakrishna
", "votes": 3, "replies": [] } ] } ], "raw_markdown_by_id": { "646735": "Gradient Community Notebooks offers public & shareable Jupyter Notebooks that run on free cloud GPUs\n\nNEW YORK, Oct. 10, 2019 /PRNewswire/ -- Paperspace announced today \"Gradient Community Notebooks\", a free cloud GPU service based on Jupyter notebooks designed for machine learning and deep learning development. Now, any developer working with popular deep learning frameworks such as PyTorch, TensorFlow, Keras, and OpenCV, can launch and collaborate on their ML projects. The new solution provides easy-to-use templates pre-loaded with all of the various different libraries, dependencies, and drivers. Similar to a GitHub repo, Gradient Community Notebooks can easily be shared and forked into a user's own account.\n\n\"GPUs are essential to ML development, yet the services available today are complex and prohibitively expensive for many developers,\" said Dillon Erb, CEO & Co-founder, Paperspace. \"This is precisely why we created Gradient Community: to make GPU and ML development resources widely accessible and easy to deploy. Our focus on empowering developers with cutting-edge technology, and a means to collaborate, supports our mission to help every developer become an AI developer.\"\n\n\n#Benefits and features of Gradient Community Notebooks include:\n\n- Free GPUs (and CPU) backed notebooks;\n\n- New Jupyter-based collaborative environment ;\n\n- Access to popular libraries, prebuilt models, and a project showcase;\n\n- Users can create a public profile page to share their bio and the work they're interested in.\n\nWith Gradient Community Notebooks, users can get started from scratch with a new notebook or leverage pre-configured projects from the Gradient ML-Showcase, a curated list of machine learning examples.\n\nFor more information or to sign up for Gradient Community Notebooks, please visit https://gradient.paperspace.com/free-gpu\n\nAbout Paperspace\nPaperspace is a high-performance cloud computing and deep learning development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use Paperspace to iterate faster and collaborate on intelligent, real-time prediction engines.\n\nPaperspace is backed by leading investors including Battery Ventures, Intel Capital, SineWave Ventures, Y Combinator and Initialized Capital.\n\nTo learn more about Gradient, please visit https://gradient.paperspace.com/ or follow us on Twitter at: @hellopaperspace.\n\nsource link : https://finance.yahoo.com/amphtml/news/paperspace-introduces-free-gpu-cloud-130000464.html?fbclid=IwAR1fr9rs5K0b2gTJleQiHhAYhGN-i9_paPkr3xX--n3u5D4pTQo5tEXLW_Y", "646804": "This is great news ! I am exploring it now . Thanks for information.", "646778": "Hmm it is best to get gpu for more runs. Thanks a lot for sharing this ", "649686": "Thanks for sharing, this will be indeed useful for many.", "649767": "Wonderful kernel. Thanks for sharing!! Really helpful. @mobassir ", "649699": "Thanks for sharing. \n\nCan you also share your experience about training models on Gradient Community Notebooks?", "649035": "great news and interesting information. thanks for sharing", "647155": "Very Informative Share... Thanks @mobassir " } }